
#pragma nodebook off
#Use nodebook for better reproducibility https://github.com/uoa-eResearch/nodebook
%reload_ext nodebook.ipython
%nodebook disk phase2


# load libraries
import geopandas as gpd # vector data
import pandas as pd # tabular data, loading CSVs
import numpy as np # numeric data
from util import *
import matplotlib # plotting
import seaborn as sns # plotting
from matplotlib_scalebar.scalebar import ScaleBar # scalebar for plot
import matplotlib.pyplot as plt # plotting
from tqdm.auto import tqdm # progress bars
tqdm.pandas()
import json
from shapely.geometry import Point, shape # creating points
import requests # web requests
plt.rcParams['figure.figsize'] = (20, 20)

/usr/local/lib/python3.10/dist-packages/geopandas/_compat.py:123: UserWarning: The Shapely GEOS version (3.11.1-CAPI-1.17.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.
  warnings.warn(
<string>:2: UserWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas will still use PyGEOS by default for now. To force to use and test Shapely 2.0, you have to set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:

import os
os.environ['USE_PYGEOS'] = '0'
import geopandas

In a future release, GeoPandas will switch to using Shapely by default. If you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).


ls()

Total: 3451.0MB


df = gpd.read_file("input/Modified_Community_Boards_SHP.zip")
df.OBJECTID = df.OBJECTID.astype(int)
df = df.set_index("OBJECTID", drop=True)
df = df.sort_index()
display(df)


df = df.rename(columns={"Local_Area": "Name"})


df["Centroid_lon"] = df.centroid.to_crs(epsg=4326).x
df["Centroid_lat"] = df.centroid.to_crs(epsg=4326).y
df["Area"] = df.area


%%time
zones = gpd.read_file("input/MASTER_UP_BaseZone_SHP.zip")
zones

CPU times: user 48.8 s, sys: 2.26 s, total: 51 s
Wall time: 50.8 s


zones[pd.isna(zones.ZONE_resol)]


zones.ZONE_resol = zones.ZONE_resol.fillna("Special")


zones.GROUPZONE_.value_counts()

General                 52461
Coastal                 42200
Residential             22398
Public Open Space        6667
Business                 3237
Rural                    2750
Special purpose zone      299
New growth                288
Name: GROUPZONE_, dtype: int64


zones.ZONE_resol.value_counts()

Road                                                         47012
Coastal - General Coastal Marine Zone                        26326
Coastal - Coastal Transition Zone                            15703
Residential - Mixed Housing Suburban Zone                     9775
Residential - Mixed Housing Urban Zone                        5864
Strategic Transport Corridor Zone                             4215
Residential - Single House Zone                               3766
Open Space - Conservation Zone                                3063
Open Space - Informal Recreation Zone                         2926
Residential - Terrace Housing and Apartment Building Zone     2134
Rural - Rural Production Zone                                 1025
Water                                                         1013
Business - Mixed Use Zone                                      782
Rural - Rural Coastal Zone                                     768
Business - Light Industry Zone                                 593
Business - Neighbourhood Centre Zone                           564
Residential - Rural and Coastal Settlement Zone                480
Open Space - Sport and Active Recreation Zone                  477
Business - Town Centre Zone                                    432
Residential - Large Lot Zone                                   379
Rural - Mixed Rural Zone                                       344
Rural - Countryside Living Zone                                326
Future Urban Zone                                              282
Business - Local Centre Zone                                   266
Hauraki Gulf Islands                                           221
Business - City Centre Zone                                    191
Open Space - Community Zone                                    183
Rural - Waitakere Ranges Zone                                  172
Business - Metropolitan Centre Zone                            160
Business - Heavy Industry Zone                                 130
Special Purpose - School Zone                                  126
Coastal - Mooring Zone                                         124
Business - General Business Zone                               103
Rural - Rural Conservation Zone                                 71
Rural - Waitakere Foothills Zone                                44
Special Purpose - Cemetery Zone                                 41
Special Purpose - Quarry Zone                                   34
Special Purpose - Maori Purpose Zone                            32
Special Purpose - Major Recreation Facility Zone                28
Special Purpose - Healthcare Facility and Hospital Zone         23
Coastal - Marina Zone                                           20
Coastal - Ferry Terminal Zone                                   20
Open Space - Civic Spaces Zone                                  18
Business - Business Park Zone                                   16
Special Purpose - Airports and Airfields Zone                    9
Green Infrastructure Corridor                                    6
Special Purpose - Tertiary Education Zone                        4
Coastal - Minor Port Zone                                        4
Coastal - Defence Zone                                           3
Special                                                          2
Name: ZONE_resol, dtype: int64


# Create a dictionary mapping the first n character of the zone description to the desired variable name
zones_of_interest = {
    "Residential": "Residential_area",
    "Residential - Single House Zone": "SH_area",
    "Residential - Mixed Housing Suburban Zone": "MHS_area",
    "Residential - Mixed Housing Urban Zone": "MHU_area",
    "Residential - Terrace Housing and Apartment Building Zone": "THA_area",
    "Residential - Large Lot Zone": "LL_area",
    "Residential - Rural and Coastal Settlement Zone": "RCS_area",
    "Rural - Waitakere Ranges Zone": "WR_area",
    "Future Urban Zone": "FU_area",
    "Hauraki Gulf Islands":  "HGI_area",
    "Business": "Business_area",
    "Rural": "Rural_area",
    "Open Space": "Open_area",
    "Road": "Roads_area",
    "Strategic Transport Corridor Zone": "InfraCorr_area",
    "Green Infrastructure Corridor": "GrInfraCorr_area",
    "Rural - Waitakere Ranges": "RWR_area",
    "Rural - Waitakere Foothills": "RWF_area",
    "Rural - Rural Coastal": "RC_area",
    "Rural - Rural Production": "RP_area",
    "Rural - Rural Conservation": "RCZ_area",
    "Rural - Mixed Rural": "RMU_area",
    "Rural - Countryside Living": "RCL_area"
}
for k in zones_of_interest.keys():
    if not any(zones.ZONE_resol.str.startswith(k)):
        print(f"Don't know {k}")


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(df, subset) # Clip the local areas to the matched zones
    df[v] = clipped.area # Store the resulting area. Unit is m²

  0%|          | 0/23 [00:00<?, ?it/s]

Residential 22398

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Single House Zone 3766

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Suburban Zone 9775

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Urban Zone 5864

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Terrace Housing and Apartment Building Zone 2134


df = df.fillna(0)


skytower = Point(1757109.809, 5920500.841) # in NZGD2000 projection


df["Hdist_skytower"] = df.centroid.distance(skytower) # Unit is meters


df.plot(column="Hdist_skytower", legend=True)

<AxesSubplot: >


coastline = df.dissolve().boundary.iloc[0]


df["Coast_indicator"] = df.intersects(coastline)
df[["Name", "Coast_indicator"]]


households = pd.read_csv("input/2013-mb-dataset-Total-New-Zealand-Household.csv", encoding='unicode_escape')
households

<string>:1: DtypeWarning: Columns (1,2) have mixed types. Specify dtype option on import or set low_memory=False.


key = "2013_Census_total_household_income_(grouped)(2)(3)(4)_for_households_in_occupied_private_dwellings_Median_household_income_($)(18)(23)"
households_with_income = households[households.Area_Code_and_Description.str.startswith("MB") & (households[key] != "..C") & (households[key] != "*")].copy()
print(len(households_with_income))
households_with_income[key] = households_with_income[key].astype(int)
households_with_income[key].plot(kind="hist", bins=20, figsize=(10,10))

40300

<AxesSubplot: ylabel='Frequency'>


meshblocks = gpd.read_file("input/statsnzpopulation-by-meshblock-2013-census-FGDB.zip!population-by-meshblock-2013-census.gdb")
meshblocks


meshblocks.geometry = meshblocks.representative_point()


meshblocks = meshblocks.merge(households, left_on="Meshblock", right_on="Area_Code_and_Description")


meshblocks = gpd.sjoin(df, meshblocks, predicate="intersects")
meshblocks


group = meshblocks.groupby("OBJECTID")


df["Census2013_population"] = group["Population_Count_Usual_Resident_2013"].sum()


df["Census2013_dwellings"] = group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


meshblocks_with_income = meshblocks[(meshblocks[key] != "..C") & (meshblocks[key] != "*")].copy()
meshblocks_with_income[key] = meshblocks_with_income[key].astype(int)
meshblocks_with_income


meshblocks_with_income["income*dwellings"] = meshblocks_with_income[key] * meshblocks_with_income["2013_Census_total_households_in_occupied_private_dwellings"]
meshblocks_with_income["income*dwellings"]


group = meshblocks_with_income.groupby("OBJECTID")
df["Census2013_avg_HH_income"] = group["income*dwellings"].sum() / group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


df[["Name", "Census2013_population", "Census2013_dwellings", "Census2013_avg_HH_income"]].sort_values(by="Census2013_population", ascending=False)

df


df.drop(columns="geometry").to_csv("output/Local_Area.csv")


mb = gpd.read_file("input/statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip!meshblock-higher-geographies-2018-generalised.gdb")


mb = mb[
    ((mb.TA2018_V1_00_NAME == "Auckland") | (mb.SA22018_V1_00_NAME.isin(["Mangatangi", "Miranda-Pukorokoro"]))) &
    mb.LANDWATER_NAME.isin(["Mainland", "Island"])]


mb = mb.rename(columns={"MB2018_V1_00": "Code"})
mb["Centroid_lon"] = mb.centroid.to_crs(epsg=4326).x
mb["Centroid_lat"] = mb.centroid.to_crs(epsg=4326).y
mb


mb = mb.set_index("Code")


skytower = Point(1757109.809, 5920500.841)
mb["Hdist_skytower"] = mb.centroid.distance(skytower) # meters
mb


mb[mb.Hdist_skytower < 5000].plot(column="Hdist_skytower", legend=True)
plt.title("Meshblocks <5KM from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM from the Skytower')


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


%%time
coastline_shape = coastline.dissolve().boundary.iloc[0]


%%time
mb["Hdist_coast"] = mb.centroid.distance(coastline_shape)


mb["Hdist_coast"].describe() # meters

count    13473.000000
mean      1610.580308
std       1850.149374
min          0.488582
25%        444.468176
50%       1077.328825
75%       2121.651872
max      23873.710662
Name: Hdist_coast, dtype: float64


ax = mb.plot(column="Hdist_coast", legend=True)
gpd.clip(coastline, mb.dissolve().envelope).boundary.plot(ax=ax)

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

<AxesSubplot: >


len(mb)

13473


BASE_URL = "http://osrm.auckland-cer.cloud.edu.au"
def drive(points, to):
    points = [f"{point.x},{point.y}" for point in points]
    points = ";".join(points)
    result = requests.get(f"{BASE_URL}/table/v1/driving/{to};{points}?destinations=0&annotations=duration,distance")
    return result.json()


skytower = "174.76218883819053,-36.848429166610735"


# Make sure OSRM is working
drive(points=mb.centroid.head(1).to_crs(epsg=4326), to=skytower)

{'code': 'Ok',
 'sources': [{'hint': 'KDEIgCwxCIALAAAA1AAAANQAAAAdAAAAvNMTQL-rMEIpsi9C9IbCQAUAAABrAAAAaQAAAA8AAAB7AQAAJahqCoW8zf3NqGoK07zN_QYAXwkFi_8F',
   'distance': 17.308342,
   'name': '',
   'location': [174.762021, -36.848507]},
  {'hint': 'H3cJgCF3CYAAAAAALwAAAAAAAAAAAAAAAAAAAC-bnUEAAAAAAAAAAAAAAAAvAAAAAAAAAAAAAAB7AQAAUDdrCnWT1v2QMmsKq6HW_QAA3wkFi_8F',
   'distance': 418.202302,
   'name': '',
   'location': [174.798672, -36.269195]}],
 'destinations': [{'hint': 'KDEIgCwxCIALAAAA1AAAANQAAAAdAAAAvNMTQL-rMEIpsi9C9IbCQAUAAABrAAAAaQAAAA8AAAB7AQAAJahqCoW8zf3NqGoK07zN_QYAXwkFi_8F',
   'distance': 17.308342,
   'name': '',
   'location': [174.762021, -36.848507]}],
 'durations': [[0], [4741.4]],
 'distances': [[0], [83585.9]]}


%%time
result = drive(points=mb.centroid.to_crs(epsg=4326), to=skytower)

CPU times: user 1.4 s, sys: 27.2 ms, total: 1.43 s
Wall time: 1min 12s


result.keys()

dict_keys(['code', 'sources', 'destinations', 'durations', 'distances'])


mb["Mdist_skytower"] = [r[0] for r in result["distances"][1:]]
mb["Mdist_skytower"].describe() # Units are meters

count     13473.000000
mean      21210.377540
std       15885.697753
min          59.500000
25%       11533.800000
50%       17220.600000
75%       24347.100000
max      136633.200000
Name: Mdist_skytower, dtype: float64


mb.plot(column="Mdist_skytower", legend=True)

<AxesSubplot: >


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


# Some strange gaps there - let's investigate
points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.x},{point.y}" for point in points.centroid.to_crs(epsg=4326)]
points

['174.75624867471396,-36.85383185528419',
 '174.75345762242358,-36.85050348371209',
 '174.75551843174853,-36.8526249602678',
 '174.75734772557055,-36.855642008978414']


routes = []
for point in points:
    route_result = requests.get(f"{BASE_URL}/route/v1/driving/{skytower};{point}?geometries=geojson")
    routes.extend(route_result.json()["routes"])
routes

[{'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760279, -36.851092],
    [174.756401, -36.856918],
    [174.756298, -36.858244],
    [174.757545, -36.859416],
    [174.761092, -36.859842],
    [174.763064, -36.860822],
    [174.764566, -36.862759],
    [174.766927, -36.864565],
    [174.768415, -36.867436],
    [174.774277, -36.87211],
    [174.774115, -36.872578],
    [174.773385, -36.872143],
    [174.771509, -36.870028],
    [174.768209, -36.867454],
    [174.766746, -36.864588],
    [174.764693, -36.863068],
    [174.76298, -36.861326],
    [174.761243, -36.860494],
    [174.758161, -36.860144],
    [174.756868, -36.859449],
    [174.755729, -36.85724],
    [174.756435, -36.854967],
    [174.756232, -36.853838]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 6623.4,
    'duration': 471.2,
    'summary': '',
    'weight': 482.5}],
  'distance': 6623.4,
  'duration': 471.2,
  'weight_name': 'routability',
  'weight': 482.5},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.75828, -36.854101],
    [174.758023, -36.853973],
    [174.756934, -36.853677],
    [174.756692, -36.853485],
    [174.756478, -36.853123],
    [174.756232, -36.85299],
    [174.755149, -36.853046],
    [174.75503, -36.853003],
    [174.754968, -36.852487],
    [174.754131, -36.851451],
    [174.753566, -36.85047]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1487.9,
    'duration': 201.1,
    'summary': '',
    'weight': 212.4}],
  'distance': 1487.9,
  'duration': 201.1,
  'weight_name': 'routability',
  'weight': 212.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.760362, -36.847639],
    [174.756384, -36.84814],
    [174.75646, -36.846119],
    [174.751793, -36.844936],
    [174.749101, -36.843285],
    [174.746338, -36.842436],
    [174.744751, -36.840654],
    [174.742077, -36.838893],
    [174.741435, -36.837888],
    [174.74165, -36.836424],
    [174.749967, -36.823771],
    [174.750195, -36.822768],
    [174.749701, -36.81919],
    [174.751533, -36.813923],
    [174.750898, -36.812294],
    [174.753046, -36.811822],
    [174.753968, -36.812071],
    [174.754297, -36.812877],
    [174.753791, -36.813549],
    [174.75206, -36.814361],
    [174.751215, -36.815353],
    [174.750131, -36.819403],
    [174.750332, -36.822928],
    [174.750102, -36.823818],
    [174.741981, -36.836162],
    [174.74164, -36.837484],
    [174.742147, -36.838632],
    [174.744931, -36.840513],
    [174.746304, -36.842152],
    [174.749683, -36.843296],
    [174.752518, -36.845063],
    [174.752984, -36.845821],
    [174.753572, -36.850065],
    [174.755481, -36.852645]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 11247.2,
    'duration': 783.1,
    'summary': '',
    'weight': 794.4}],
  'distance': 11247.2,
  'duration': 783.1,
  'weight_name': 'routability',
  'weight': 794.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.758622, -36.85365],
    [174.757737, -36.854764],
    [174.757261, -36.855607]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1022.2,
    'duration': 135,
    'summary': '',
    'weight': 146.3}],
  'distance': 1022.2,
  'duration': 135,
  'weight_name': 'routability',
  'weight': 146.3}]


route_df = pd.read_json(json.dumps(routes))
route_df["geometry"] = route_df['geometry'].apply(shape)
route_df = gpd.GeoDataFrame(route_df)
route_df


ax = mb[mb.Mdist_skytower < 5000].to_crs(epsg=4326).plot(column="Mdist_skytower", legend=True)
route_df[route_df["distance"]>10000].plot(column="distance", legend=True, ax=ax, linewidth=3, cmap="plasma")

<AxesSubplot: >


points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.y},{point.x}" for point in points.centroid.to_crs(epsg=4326)]
for point in points:
    print(f"https://map.project-osrm.org/?loc={point}&loc=-36.848429166610735,174.76218883819053&srv=0")

https://map.project-osrm.org/?loc=-36.85383185528419,174.75624867471396&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.85050348371209,174.75345762242358&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.8526249602678,174.75551843174853&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.855642008978414,174.75734772557055&loc=-36.848429166610735,174.76218883819053&srv=0


%%time
north = drive(points=mb.centroid.translate(yoff=-100).to_crs(epsg=4326), to=skytower)
west = drive(points=mb.centroid.translate(xoff=-100).to_crs(epsg=4326), to=skytower)
east = drive(points=mb.centroid.translate(xoff=100).to_crs(epsg=4326), to=skytower)
south = drive(points=mb.centroid.translate(yoff=100).to_crs(epsg=4326), to=skytower)

CPU times: user 8.78 s, sys: 21.6 ms, total: 8.8 s
Wall time: 4min 48s


mb["Mdist_skytower_center"] = [r[0] for r in result["distances"][1:]]
mb["Mdist_skytower_north"] = [r[0] for r in north["distances"][1:]]
mb["Mdist_skytower_west"] = [r[0] for r in west["distances"][1:]]
mb["Mdist_skytower_east"] = [r[0] for r in east["distances"][1:]]
mb["Mdist_skytower_south"] = [r[0] for r in south["distances"][1:]]
mb[["Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"]].describe()


mb["Mdist_skytower"] = mb[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
(mb["Mdist_skytower_center"] - mb["Mdist_skytower"]).describe()

count    13473.000000
mean       270.188904
std        506.155787
min          0.000000
25%         81.200000
50%        131.500000
75%        260.900000
max      12976.900000
dtype: float64


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


mb["Mtime_skytower_center"] = [r[0] for r in result["durations"][1:]]
mb["Mtime_skytower_north"] = [r[0] for r in north["durations"][1:]]
mb["Mtime_skytower_west"] = [r[0] for r in west["durations"][1:]]
mb["Mtime_skytower_east"] = [r[0] for r in east["durations"][1:]]
mb["Mtime_skytower_south"] = [r[0] for r in south["durations"][1:]]
display(mb[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
mb["Mtime_skytower"] = mb[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
mb["Mtime_skytower"].describe() # Units are seconds

count    13473.000000
mean      1575.136428
std       3356.504241
min          7.200000
25%        842.000000
50%       1139.700000
75%       1490.000000
max      70521.800000
Name: Mtime_skytower, dtype: float64


mb[mb.Mtime_skytower < 60*20].plot(column="Mtime_skytower", legend=True)
plt.title("Meshblocks < 20 minutes drive from the Skytower")

Text(0.5, 1.0, 'Meshblocks < 20 minutes drive from the Skytower')


transit = pd.read_excel("input/Geocoordinates_Direct_Transit_stops_AKL.xlsx")
transit = gpd.GeoDataFrame(transit, geometry=gpd.points_from_xy(transit.Longitude, transit.Latitude), crs="EPSG:4326")
transit


len(mb)

13473


%%time
def drive(from_points, to_points):
    # Drive distance and time these meshblock centroids to all 58 rapid transit stops
    from_points_s = ";".join([f"{point.x},{point.y}" for point in from_points])
    from_indices = ";".join([str(i) for i in range(len(from_points))])
    to_points_s = ";".join([f"{point.x},{point.y}" for point in to_points])
    to_indices = ";".join([str(i + len(from_points)) for i in range(len(to_points))])
    result = requests.get(f"{BASE_URL}/table/v1/driving/{from_points_s};{to_points_s}?sources={from_indices}&destinations={to_indices}&annotations=duration,distance")
    return result.json()
result = drive(from_points=mb.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 3.78 s, sys: 79.2 ms, total: 3.86 s
Wall time: 33.8 s


min_indices = np.argmin(result["distances"], axis=1)
mb["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


mb.Mdist_rapid_transit_name.value_counts()

Constellation Bus Station     1006
Hibiscus Coast Bus Station     862
Half Moon Bay                  718
Pukekohe                       538
Papakura                       508
Manukau                        503
Glen Innes                     424
Onehunga                       420
Manurewa                       378
Kingsland                      371
Mt. Albert                     346
West Harbour                   332
Glen Eden                      326
Middlemore                     314
Avondale                       304
Henderson                      288
Papatoetoe                     286
Fruitvale Road                 248
Britomart                      246
Otahuhu                        246
Morningside                    227
Beach Haven                    223
Akoranga Bus Station           223
Sunnyvale                      219
Sylvia Park                    218
Mt Eden                        216
Smales Farm Bus Station        214
Ranui                          208
New Lynn                       202
Gulf Harbour                   193
Newmarket                      185
Orakei                         182
Sturges Road                   177
Homai                          154
Birkenhead                     147
Matiatia                       144
Takanini                       128
Ellerslie                      125
Panmure                        122
Swanson                        121
Meadowbank                     118
Devonport                      115
Grafton                        101
Te Papapa                       96
Bayswater                       95
Remuera                         91
Pine Harbour                    90
Parnell                         90
Puhinui                         83
Penrose                         81
Baldwin Avenue                  68
Northcote                       68
Hobsonville                     56
Stanley Bay                     15
Te Mahia                        11
Sunnynook Bus Station            2
Downtown                         1
Name: Mdist_rapid_transit_name, dtype: int64


ax = mb.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


ax = mb[mb.Mdist_rapid_transit < 5000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


onramps = gpd.read_file("input/kx-nz-state-highway-on-ramps-off-ramps-SHP.zip")
onramps = gpd.clip(onramps.to_crs(mb.crs), mb)
onramps

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[


# Filter out offramps
onramps = onramps[~onramps.DESCRIPTIO.str.contains("OFF")]
onramps


onramps["has_on"] = onramps.DESCRIPTIO.str.contains("ON").astype(str)
onramps.plot(column="has_on", legend=True)

<AxesSubplot: >


# Include Maioro road onramps for 2018 datasets
maioro = gpd.GeoDataFrame({
    "DESCRIPTIO": ["Maioro street southbound", "Maioro street northbound"]
}, geometry=gpd.GeoSeries([
    Point(174.723958, -36.903573),
    Point(174.722877,-36.903690)
], crs=4326).to_crs(onramps.crs))
maioro


onramps2018 = onramps.append(maioro)
onramps2018

<string>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.


%%time
result = drive(from_points=mb.centroid.to_crs(epsg=4326), to_points=onramps2018.centroid.to_crs(epsg=4326))

CPU times: user 8.79 s, sys: 166 ms, total: 8.96 s
Wall time: 1min 2s


len(result["distances"]), len(result["distances"][0])

(13473, 177)


min_indices = np.argmin(result["distances"], axis=1)
mb["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mdist_onramp_name"] = list(onramps2018.DESCRIPTIO.iloc[min_indices])


mb.Mdist_onramp_name.value_counts()

Maioro street northbound     795
01N-0381-R1                  777
01N-2398/00.07-X398-R1-ON    548
01N-2414/12.16-X426-R2-ON    517
01N-2461/00.17-X462-R2-ON    512
                            ... 
01N-2414/02.55-X417-R4-ON      1
002-0009-R3                    1
01N-0441-R4                    1
016-0007/04.31-D-ON            1
01N-2431/07.81-X438-R2-ON      1
Name: Mdist_onramp_name, Length: 119, dtype: int64


ax = mb.plot(column="Mdist_onramp", legend=True)
onramps2018.plot(ax=ax, color="red")

<AxesSubplot: >


ax = mb[mb.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot: >


all(mb.LAND_AREA_SQ_KM == mb.AREA_SQ_KM)

True


mb = mb.rename(columns={"LAND_AREA_SQ_KM": "Area"})


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(mb, subset) # Clip the local areas to the matched zones
    mb[v] = clipped.area # Store the resulting area. Unit is m²

  0%|          | 0/23 [00:00<?, ?it/s]

Residential 22398

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Single House Zone 3766

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Suburban Zone 9775

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Urban Zone 5864

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Terrace Housing and Apartment Building Zone 2134


mb = mb.fillna(0)


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


%%time
coastline = gpd.clip(coastline, mb.dissolve().envelope)

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

CPU times: user 2.5 s, sys: 80 ms, total: 2.58 s
Wall time: 2.58 s


%%time
coastline = coastline.dissolve().boundary.buffer(100)
coastline.plot()

CPU times: user 15.3 s, sys: 220 ms, total: 15.5 s
Wall time: 15.4 s

<AxesSubplot: >


coastline = coastline.iloc[0]


%%time
mb["Coast_indicator"] = mb.geometry.progress_apply(lambda poly: coastline.intersects(poly))

  0%|          | 0/13473 [00:00<?, ?it/s]

CPU times: user 12min 46s, sys: 1.51 s, total: 12min 47s
Wall time: 12min 43s


mb.plot(column="Coast_indicator", legend=True)

<AxesSubplot: >


# Note that this is meshblock 2020, which is not ideal, but there doesn't seem to be any changes in the Auckland region between 2018 and 2020, so it's probably fine
pop = pd.read_csv("input/2018-census-electoral-population-meshblock-2020-data.csv", index_col=0)
pop


pop.General_Electoral_Population = pop.General_Electoral_Population.replace(-999, np.nan)


mb["Census2018_population"] = list(pop.General_Electoral_Population[mb.index.astype(int)])
display(mb["Census2018_population"].describe())
mb.plot(column="Census2018_population", legend=True)

count    12914.000000
mean       113.633886
std         63.165389
min          6.000000
25%         72.000000
50%        108.000000
75%        147.000000
max       1029.000000
Name: Census2018_population, dtype: float64

<AxesSubplot: >


# Can't do this one due to missing data

mb


mb.drop(columns="geometry").to_csv("output/2018_Meshblocks.csv")


sa2 = gpd.read_file("input/statsnzstatistical-area-2-higher-geographies-2018-clipped-generalis-FGDB.zip!statistical-area-2-higher-geographies-2018-clipped-generalis.gdb")
sa2 = sa2[(sa2.TA2018_V1_00_NAME == "Auckland") | sa2.SA22018_V1_00_NAME.isin(["Mangatangi", "Miranda-Pukorokoro"])]
sa2


sa2 = sa2.rename(columns={"SA22018_V1_00_NAME": "Name", "SA22018_V1_00": "Code"})
sa2["Centroid_lon"] = sa2.centroid.to_crs(epsg=4326).x
sa2["Centroid_lat"] = sa2.centroid.to_crs(epsg=4326).y
sa2


sa2["Centroid_local_area"] = sa2.representative_point().apply(lambda point: df.Name[df.contains(point)].iloc[0] if any(df.contains(point)) else np.nan)
sa2["Centroid_local_area"].value_counts()

Henderson-Massey                    37
Auckland Central                    32
Beach Haven-Birkenhead-Northcote    28
Manurewa                            28
Orakei                              27
Mangere-Otahuhu                     26
Whau                                26
Botany                              23
Upper Harbour Local Board Area      22
Papakura                            22
Hibiscus Coast                      22
Puketapapa                          20
Devonport-Takapuna                  19
Franklin-Pukekohe                   17
Mt Albert                           16
Tamaki                              16
Papatoetoe                          16
Mt Eden                             15
East Coast Bays                     15
Pakuranga                           14
Rodney-Helensville                  13
Titirangi                           12
Howick                              12
Franklin-Beachlands-Hunua           12
Maungakiekie                        11
Otara                               10
Rodney-Warkworth                     9
Waitakere                            9
Franklin-Waiuku                      7
Waiheke                              7
Rodney-Kumeu-Riverhead               5
Rodney-Dairy Flat                    4
Rodney-Wellsford                     4
Name: Centroid_local_area, dtype: int64


sa2[pd.isna(sa2.Centroid_local_area)]


skytower = Point(1757109.809, 5920500.841) # in NZGD2000 projection


sa2["Hdist_skytower"] = sa2.centroid.distance(skytower)


sa2.plot(column="Hdist_skytower", legend=True)

<AxesSubplot: >


mway = gpd.read_file("input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb")
mway = mway[~pd.isna(mway.hway_num)]
mway


%%time
sa2["Hdist_motorway"] = sa2.centroid.distance(mway.dissolve().geometry[0])
ax = sa2.plot(column="Hdist_motorway", legend=True)
gpd.clip(mway, sa2.envelope).plot(ax=ax, color="red")

CPU times: user 877 ms, sys: 140 ms, total: 1.02 s
Wall time: 872 ms

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

<AxesSubplot: >


rail = gpd.read_file("input/lds-nz-railway-centrelines-topo-150k-SHP.zip")
rail


%%time
sa2["Hdist_rail"] = sa2.centroid.distance(rail.dissolve().geometry[0])
ax = sa2.plot(column="Hdist_rail", legend=True)
gpd.clip(rail, sa2.envelope).plot(ax=ax, color="red")

CPU times: user 612 ms, sys: 120 ms, total: 732 ms
Wall time: 589 ms

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

<AxesSubplot: >


skytower = gpd.GeoSeries(skytower, crs="EPSG:2193")
skytower

0    POINT (1757109.809 5920500.841)
dtype: geometry


%%time
result = drive(sa2.centroid.to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 792 ms, sys: 29.9 ms, total: 822 ms
Wall time: 2.38 s


sa2["Mdist_skytower"] = [r[0] for r in result["distances"]]
sa2["Mtime_skytower"] = [r[0] for r in result["durations"]]


sa2.plot(column="Mdist_skytower", legend=True)

<AxesSubplot: >


sa2[sa2.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)

<AxesSubplot: >


%%time
north = drive(sa2.centroid.translate(yoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
west = drive(sa2.centroid.translate(xoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
east = drive(sa2.centroid.translate(xoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
south = drive(sa2.centroid.translate(yoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 609 ms, sys: 111 µs, total: 609 ms
Wall time: 6.48 s


sa2["Mdist_skytower_center"] = [r[0] for r in result["distances"]]
sa2["Mdist_skytower_north"] = [r[0] for r in north["distances"]]
sa2["Mdist_skytower_west"] = [r[0] for r in west["distances"]]
sa2["Mdist_skytower_east"] = [r[0] for r in east["distances"]]
sa2["Mdist_skytower_south"] = [r[0] for r in south["distances"]]
sa2[["Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"]].describe()


sa2["Mdist_skytower"] = sa2[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
(sa2["Mdist_skytower_center"] - sa2["Mdist_skytower"]).describe()

count     558.000000
mean      241.676882
std       435.008947
min         0.000000
25%        85.250000
50%       118.050000
75%       229.500000
max      5626.500000
dtype: float64


sa2[sa2.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("SA2 <5KM driving from the Skytower")

Text(0.5, 1.0, 'SA2 <5KM driving from the Skytower')


sa2["Mtime_skytower_center"] = [r[0] for r in result["durations"]]
sa2["Mtime_skytower_north"] = [r[0] for r in north["durations"]]
sa2["Mtime_skytower_west"] = [r[0] for r in west["durations"]]
sa2["Mtime_skytower_east"] = [r[0] for r in east["durations"]]
sa2["Mtime_skytower_south"] = [r[0] for r in south["durations"]]
display(sa2[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
sa2["Mtime_skytower"] = sa2[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
sa2["Mtime_skytower"].describe() # Units are seconds

count      558.000000
mean      1595.834588
std       3312.276911
min         46.600000
25%        863.500000
50%       1176.350000
75%       1560.475000
max      70470.600000
Name: Mtime_skytower, dtype: float64


sa2[sa2.Mtime_skytower < 60*20].plot(column="Mtime_skytower", legend=True)
plt.title("SA2s < 20 minutes drive from the Skytower")

Text(0.5, 1.0, 'SA2s < 20 minutes drive from the Skytower')


%%time
result = drive(from_points=sa2.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 190 ms, sys: 29.8 ms, total: 220 ms
Wall time: 1.26 s


min_indices = np.argmin(result["distances"], axis=1)
sa2["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


sa2.Mdist_rapid_transit_name.value_counts()

Constellation Bus Station     41
Hibiscus Coast Bus Station    36
Half Moon Bay                 30
Manukau                       27
Papakura                      24
Pukekohe                      24
West Harbour                  18
Onehunga                      17
Britomart                     15
Middlemore                    15
Glen Innes                    14
Manurewa                      14
Mt. Albert                    13
Avondale                      12
Kingsland                     12
Otahuhu                       12
Sunnyvale                     11
Papatoetoe                    11
Henderson                     11
Homai                         10
Sylvia Park                   10
Smales Farm Bus Station        9
Fruitvale Road                 9
New Lynn                       9
Morningside                    9
Glen Eden                      9
Ranui                          9
Swanson                        8
Gulf Harbour                   8
Sturges Road                   8
Beach Haven                    7
Mt Eden                        7
Akoranga Bus Station           7
Takanini                       7
Grafton                        6
Newmarket                      6
Ellerslie                      6
Matiatia                       6
Birkenhead                     5
Orakei                         5
Meadowbank                     4
Panmure                        4
Puhinui                        4
Pine Harbour                   4
Bayswater                      4
Parnell                        3
Remuera                        3
Northcote                      3
Devonport                      3
Baldwin Avenue                 2
Hobsonville                    2
Te Papapa                      2
Stanley Bay                    1
Penrose                        1
Te Mahia                       1
Name: Mdist_rapid_transit_name, dtype: int64


ax = sa2.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


ax = sa2[sa2.Mdist_rapid_transit < 5000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


%%time
result = drive(from_points=sa2.centroid.to_crs(epsg=4326), to_points=onramps2018.centroid.to_crs(epsg=4326))

CPU times: user 440 ms, sys: 66 µs, total: 440 ms
Wall time: 2.22 s


min_indices = np.argmin(result["distances"], axis=1)
sa2["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mdist_onramp_name"] = list(onramps2018.DESCRIPTIO.iloc[min_indices])


ax = sa2.plot(column="Mdist_onramp", legend=True)
onramps2018.plot(ax=ax, color="red")

<AxesSubplot: >


ax = sa2[sa2.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot: >


sa2 = sa2.rename(columns={"LAND_AREA_SQ_KM": "Area"})


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(sa2, subset) # Clip the local areas to the matched zones
    sa2[v] = clipped.area # Store the resulting area. Unit is m²

  0%|          | 0/23 [00:00<?, ?it/s]

Residential 22398

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Single House Zone 3766

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Suburban Zone 9775

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Urban Zone 5864

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Terrace Housing and Apartment Building Zone 2134


cols = list(zones_of_interest.values())
sa2[cols] = sa2[cols].fillna(0)


%%time
sa2["Coast_indicator"] = sa2.geometry.progress_apply(lambda poly: coastline.intersects(poly))

  0%|          | 0/558 [00:00<?, ?it/s]

CPU times: user 33.8 s, sys: 69.9 ms, total: 33.9 s
Wall time: 33.7 s


sa2.plot(column="Coast_indicator", legend=True, edgecolor="red")

<AxesSubplot: >


pop = pd.read_csv("input/Individual_part1_totalNZ-wide_format_updated_16-7-20.csv")

<string>:1: DtypeWarning: Columns (1,2,300,312,324) have mixed types. Specify dtype option on import or set low_memory=False.


pop = pop.set_index("Area_code")
pop


sa2["Census2018_population"] = pop.Census_2018_usually_resident_population_count[sa2.Code].tolist()
sa2.plot(column="Census2018_population", legend=True)

<AxesSubplot: >


sa2[["Name", "Census2018_population"]].sort_values("Census2018_population", ascending=False).head(20)


sa2.Census2018_population.plot(kind="hist", bins=100)

<AxesSubplot: ylabel='Frequency'>


dwellings = pd.read_excel("input/2018_census_dwellings_by_SA2.xlsx", sheet_name="Table 1", skiprows=11)
dwellings = dwellings[~pd.isna(dwellings.Total)]
dwellings.index = dwellings["Unnamed: 0"].str.split(" ").str[0]
dwellings = dwellings.replace("..C", np.nan)
dwellings


sa2["Census2018_dwellings"] = dwellings.Total[sa2.Code].tolist()
sa2["Census2018_avg_HH_income"] = dwellings["Mean Household \nIncome ($)"][sa2.Code].tolist()
sa2["Census2018_med_HH_income"] = dwellings["Median Household\n Income ($)"][sa2.Code].tolist()


sa2.plot(column="Census2018_dwellings", legend=True)
sa2.plot(column="Census2018_avg_HH_income", legend=True)
sa2.plot(column="Census2018_med_HH_income", legend=True)

<AxesSubplot: >


sa2.Census2018_med_HH_income.plot(kind="hist", bins=100)

<AxesSubplot: ylabel='Frequency'>

sa2


sa2.drop(columns="geometry").to_csv("output/2018_Statistical_Area_2.csv")


au = gpd.read_file("input/statsnzarea-unit-2013-FGDB.zip!area-unit-2013.gdb")


au = au.rename(columns={"AU2013_V1_00_NAME": "Name", "AU2013_V1_00": "Code"})
au["Centroid_lon"] = au.centroid.to_crs(epsg=4326).x
au["Centroid_lat"] = au.centroid.to_crs(epsg=4326).y


au["Centroid_local_area"] = au.representative_point().apply(lambda point: df.Name[df.contains(point)].iloc[0] if any(df.contains(point)) else np.nan)
problems = au.Code.isin(["520602", "521135", "506634"])
au.loc[problems, "Centroid_local_area"] = au[problems].geometry.apply(lambda poly: df.Name[df.intersects(poly)].iloc[0])
au["Centroid_local_area"].value_counts()

Henderson-Massey                    29
Manurewa                            21
Orakei                              20
Auckland Central                    20
Mangere-Otahuhu                     19
Beach Haven-Birkenhead-Northcote    19
Hibiscus Coast                      17
Whau                                16
Rodney-Helensville                  15
Upper Harbour Local Board Area      15
Papakura                            14
Devonport-Takapuna                  13
Mt Eden                             13
Botany                              13
Franklin-Pukekohe                   12
Titirangi                           12
Rodney-Warkworth                    12
Papatoetoe                          11
Pakuranga                           11
Puketapapa                          11
Tamaki                              11
Mt Albert                           11
East Coast Bays                     11
Franklin-Beachlands-Hunua           10
Howick                              10
Maungakiekie                         9
Otara                                9
Waitakere                            8
Franklin-Waiuku                      5
Rodney-Kumeu-Riverhead               4
Waiheke                              3
Rodney-Wellsford                     3
Rodney-Dairy Flat                    2
Name: Centroid_local_area, dtype: int64


print(f'{sum(~pd.isna(au["Centroid_local_area"]))} matches, {sum(pd.isna(au["Centroid_local_area"]))} unmatched')

409 matches, 1595 unmatched


au = au[~pd.isna(au.Centroid_local_area)]
ax = au.plot(column="Centroid_local_area", cmap="Spectral", legend=True)


au["Hdist_skytower"] = au.centroid.distance(skytower.iloc[0])
au.plot(column="Hdist_skytower", legend=True)

<AxesSubplot: >


%%time
au["Hdist_motorway"] = au.centroid.distance(mway.dissolve().geometry[0])
ax = au.plot(column="Hdist_motorway", legend=True)
gpd.clip(mway, au.envelope).plot(ax=ax, color="red")

CPU times: user 716 ms, sys: 160 ms, total: 876 ms
Wall time: 751 ms

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

<AxesSubplot: >


%%time
au["Hdist_rail"] = au.centroid.distance(rail.dissolve().geometry[0])
ax = au.plot(column="Hdist_rail", legend=True)
gpd.clip(rail, au.envelope).plot(ax=ax, color="red")

CPU times: user 524 ms, sys: 110 ms, total: 634 ms
Wall time: 489 ms

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

<AxesSubplot: >


%%time
result = drive(au.centroid.to_crs(epsg=4326), skytower.to_crs(epsg=4326))
north = drive(au.centroid.translate(yoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
west = drive(au.centroid.translate(xoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
east = drive(au.centroid.translate(xoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
south = drive(au.centroid.translate(yoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 453 ms, sys: 10.2 ms, total: 463 ms
Wall time: 4.56 s


au["Mdist_skytower_center"] = [r[0] for r in result["distances"]]
au["Mdist_skytower_north"] = [r[0] for r in north["distances"]]
au["Mdist_skytower_west"] = [r[0] for r in west["distances"]]
au["Mdist_skytower_east"] = [r[0] for r in east["distances"]]
au["Mdist_skytower_south"] = [r[0] for r in south["distances"]]
display(au[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].describe())
au["Mdist_skytower"] = au[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
display(au["Mdist_skytower"].describe())

au["Mtime_skytower_center"] = [r[0] for r in result["durations"]]
au["Mtime_skytower_north"] = [r[0] for r in north["durations"]]
au["Mtime_skytower_west"] = [r[0] for r in west["durations"]]
au["Mtime_skytower_east"] = [r[0] for r in east["durations"]]
au["Mtime_skytower_south"] = [r[0] for r in south["durations"]]
display(au[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
au["Mtime_skytower"] = au[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
display(au["Mtime_skytower"].describe())

count      409.000000
mean     22531.981907
std      16385.776202
min        446.000000
25%      12227.900000
50%      18255.700000
75%      27187.300000
max      87888.800000
Name: Mdist_skytower, dtype: float64

count      409.000000
mean      1459.823227
std       1263.493685
min         73.100000
25%        883.600000
50%       1203.900000
75%       1588.300000
max      14753.900000
Name: Mtime_skytower, dtype: float64


au.plot(column="Mdist_skytower", legend=True)

<AxesSubplot: >


au[au.Mdist_skytower < 10000].plot(column="Mdist_skytower", legend=True)

<AxesSubplot: >


%%time
result = drive(from_points=au.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 191 ms, sys: 150 µs, total: 192 ms
Wall time: 1.08 s


min_indices = np.argmin(result["distances"], axis=1)
au["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


au.Mdist_rapid_transit_name.value_counts()

Hibiscus Coast Bus Station    36
Constellation Bus Station     25
Half Moon Bay                 24
Papakura                      21
West Harbour                  18
Pukekohe                      15
Manukau                       13
Papatoetoe                    12
Otahuhu                       12
Kingsland                     11
Manurewa                      11
Ranui                         10
Onehunga                      10
Glen Innes                    10
Sunnyvale                     10
Mt. Albert                    10
Henderson                      9
Middlemore                     9
Glen Eden                      9
Swanson                        8
Avondale                       7
Morningside                    7
Sylvia Park                    6
Homai                          6
New Lynn                       6
Beach Haven                    6
Fruitvale Road                 6
Grafton                        6
Britomart                      5
Smales Farm Bus Station        5
Akoranga Bus Station           5
Orakei                         5
Newmarket                      4
Gulf Harbour                   4
Birkenhead                     4
Ellerslie                      4
Mt Eden                        4
Panmure                        3
Te Papapa                      3
Remuera                        3
Bayswater                      3
Sturges Road                   3
Takanini                       3
Parnell                        2
Matiatia                       2
Devonport                      2
Northcote                      2
Puhinui                        2
Pine Harbour                   2
Baldwin Avenue                 2
Stanley Bay                    1
Penrose                        1
Meadowbank                     1
Te Mahia                       1
Name: Mdist_rapid_transit_name, dtype: int64


ax = au.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


ax = au[au.Mdist_rapid_transit < 10000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot: >


%%time
result = drive(from_points=au.centroid.to_crs(epsg=4326), to_points=onramps.centroid.to_crs(epsg=4326))

CPU times: user 338 ms, sys: 102 µs, total: 338 ms
Wall time: 1.79 s


min_indices = np.argmin(result["distances"], axis=1)
au["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mdist_onramp_name"] = list(onramps.DESCRIPTIO.iloc[min_indices])


ax = au.plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot: >


ax = au[au.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot: >


au = au.rename(columns={"LAND_AREA_SQ_KM": "Area"})


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(au, subset) # Clip the local areas to the matched zones
    au[v] = clipped.area # Store the resulting area. Unit is m²

  0%|          | 0/23 [00:00<?, ?it/s]

Residential 22398

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Single House Zone 3766

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Suburban Zone 9775

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Mixed Housing Urban Zone 5864

/usr/local/lib/python3.10/dist-packages/geopandas/tools/clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[

Residential - Terrace Housing and Apartment Building Zone 2134


au = au.fillna(0)


%%time
au["Coast_indicator"] = au.geometry.progress_apply(lambda poly: coastline.intersects(poly))

  0%|          | 0/409 [00:00<?, ?it/s]

CPU times: user 24.2 s, sys: 60.8 ms, total: 24.3 s
Wall time: 24.2 s


au.plot(column="Coast_indicator", legend=True, edgecolor="red")

<AxesSubplot: >


pop = pd.read_csv("input/2013-mb-dataset-Total-New-Zealand-Individual-Part-1.csv", encoding='unicode_escape', low_memory=False)
pop


pop = pop.set_index("Code")


au["Census2013_population"] = pop["2013_Census_census_usually_resident_population_count(1)"][au.Code].tolist()


au.plot(column="Census2013_population", legend=True)

<AxesSubplot: >


households = households.set_index("Code")


au["Census2013_dwellings"] = households["2013_Census_total_households_in_occupied_private_dwellings"][au.Code].tolist()


au.plot(column="Census2013_dwellings", legend=True)

<AxesSubplot: >


income = pd.read_excel("input/2013_census_mean_income_by_AU2013.xlsx", sheet_name = "Data", skiprows=8)
income = income.replace("..C", np.nan)
income.index = income["Census Night Area Unit (2013 Areas)"].str.split().str[0]
income


income[" ($) Mean Household Income"].plot(kind="hist", bins=100)

<AxesSubplot: ylabel='Frequency'>


au["Census2013_avg_HH_income"] = income[" ($) Mean Household Income"][au.Code].astype(float).tolist()


au.plot(column="Census2013_avg_HH_income", legend=True)

<AxesSubplot: >

key

'2013_Census_total_household_income_(grouped)(2)(3)(4)_for_households_in_occupied_private_dwellings_Median_household_income_($)(18)(23)'


au["Census2013_med_HH_income"] = households[key][au.Code].replace("..C", np.nan).astype(float).tolist()


au.plot(column="Census2013_med_HH_income", legend=True)

<AxesSubplot: >

au


au.drop(columns="geometry").to_csv("output/2013_Area_Unit.csv")


zones


cols = [
    "Residential - Terrace Housing and Apartment Building Zone",
    "Residential - Mixed Housing Urban Zone",
    "Residential - Mixed Housing Suburban Zone"
]


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


cmap = plt.cm.get_cmap('plasma', 512)
cmap = matplotlib.colors.ListedColormap(cmap([0,.2,.4]))
ax = zones[zones.ZONE_resol.isin(cols)].plot(column="ZONE_resol", legend=True, cmap=cmap, legend_kwds={'fontsize':20, 'loc': 'lower left'})
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin + 1e4, ymax - 4e4)

ax.add_artist(ScaleBar(1, location="upper right", font_properties={"size": 20}))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

ax.set_xticks([])
ax.set_yticks([])

1735024.9733500003 1779731.6056499996 5869626.39383 5980785.90757

[]


ax = zones[zones.ZONE_resol == "Residential - Single House Zone"].plot(column="ZONE_resol", legend=True, legend_kwds={'fontsize':20, 'loc': 'lower left'})
#xmin, xmax = ax.get_xlim()
#ymin, ymax = ax.get_ylim()
#print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin + 1e4, ymax - 4e4)

ax.add_artist(ScaleBar(1, location="upper right", font_properties={"size": 20}))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
ax.set_xticks([])
ax.set_yticks([])

[]


df.Name[df.Hdist_skytower < 2e4].tolist()

['Auckland Central',
 'Beach Haven-Birkenhead-Northcote',
 'Botany',
 'Devonport-Takapuna',
 'East Coast Bays',
 'Henderson-Massey',
 'Howick',
 'Mangere-Otahuhu',
 'Maungakiekie',
 'Mt Albert',
 'Mt Eden',
 'Orakei',
 'Otara',
 'Pakuranga',
 'Papatoetoe',
 'Puketapapa',
 'Tamaki',
 'Titirangi',
 'Upper Harbour Local Board Area',
 'Whau']


df["Core"] = df.Name.isin(['Auckland Central',
 'Beach Haven-Birkenhead-Northcote',
 'Botany',
 'Devonport-Takapuna',
 'East Coast Bays',
 'Henderson-Massey',
 'Howick',
 'Mangere-Otahuhu',
 'Manurewa',
 'Maungakiekie',
 'Mt Albert',
 'Mt Eden',
 'Orakei',
 'Otara',
 'Pakuranga',
 'Papatoetoe',
 'Papakura',
 'Puketapapa',
 'Tamaki',
 'Titirangi',
 'Upper Harbour Local Board Area',
 'Whau'])
display(df[["Name", "Core"]])


cmap = matplotlib.colors.ListedColormap(['orange', 'yellow'])
ax = df.plot(column = "Core", cmap=cmap, edgecolor="black", linewidth=.5)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.add_artist(ScaleBar(1, location="lower left"))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
ax.set_xticks([])
ax.set_yticks([])

1699039.1745048524 1809972.3879951476 5864874.988694763 6008322.51060524

[]

	name	filesize (MB)	last modified
0	2013-mb-dataset-Total-New-Zealand-Household.csv	37.12	2023-04-26 09:36:11.702367
1	2013-mb-dataset-Total-New-Zealand-Individual-Part-1.csv	31.66	2023-04-26 09:36:17.892367
2	2013_census_mean_income_by_AU2013.xlsx	0.59	2023-04-26 09:36:20.982367
3	2016_aup.zip	1192.41	2023-04-26 09:36:43.692368
4	2018-census-electoral-population-meshblock-2020-data.csv	5.60	2023-04-26 09:36:19.952367
5	2018_census_dwellings_by_SA2.xlsx	0.43	2023-04-26 09:36:20.982367
6	AC_Special_Housing_Area.zip	0.29	2023-04-26 09:36:21.012367
7	AUP_viewshafts.zip	0.21	2023-04-26 09:36:21.182367
8	AucklandArea.gpkg	0.09	2023-04-26 09:36:21.072367
9	Geocoordinates_Direct_Transit_stops_AKL.xlsx	0.01	2023-04-26 09:36:21.272367
10	Individual_part1_totalNZ-wide_format_updated_16-7-20.csv	36.58	2023-04-26 09:36:14.252367
11	MASTER_UP_BaseZone_SHP.zip	66.92	2023-04-26 09:36:11.212367
12	Modified_Community_Boards_SHP.zip	1.30	2023-04-26 09:36:20.962367
13	NZ_Primary_Parcels_Nov_2016.zip	1027.26	2023-04-26 09:36:41.572368
14	Primary_Parcels_2016_prepared.gpkg	235.79	2023-04-26 09:36:18.662367
15	Transmission_Lines_exTRANSPOWER.zip	0.50	2023-04-26 09:36:20.982367
16	area-unit-2013.gdb.zip	14.21	2023-04-26 09:36:18.892367
17	kx-nz-state-highway-on-ramps-off-ramps-SHP.zip	0.14	2023-04-26 09:36:21.212367
18	lds-nz-addresses-pilot-FGDB.zip	36.43	2023-04-26 09:36:14.652367
19	lds-nz-coastline-mean-high-water-FGDB.zip	4.88	2023-04-26 09:36:20.962367
20	lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip	4.25	2023-04-26 09:36:20.972367
21	lds-nz-primary-parcels-CLIPPED-4326.gpkg	273.35	2023-04-26 09:36:20.902367
22	lds-nz-primary-parcels-FGDB.zip	78.05	2023-04-26 09:36:05.642367
23	lds-nz-railway-centrelines-topo-150k-SHP.zip	0.87	2023-04-26 09:36:20.972367
24	lds-nz-road-centrelines-topo-150k-FGDB.zip	36.06	2023-04-26 09:36:15.032367
25	lds-nz-street-address-FGDB.zip	30.48	2023-04-26 09:36:20.952367
26	meshblock-2013.gdb.zip	106.57	2023-04-26 09:36:08.102367
27	meshblock-2018-clipped-generalised.gdb.zip	32.83	2023-04-26 09:36:17.712367
28	nz-primary-parcels.gdb.zip	55.00	2023-04-26 09:36:10.802367
29	statsnzarea-unit-2013-FGDB.zip	13.79	2023-04-26 09:36:19.132367
30	statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip	34.54	2023-04-26 09:36:17.482367
31	statsnzpopulation-by-meshblock-2013-census-FGDB.zip	82.11	2023-04-26 09:36:06.092367
32	statsnzstatistical-area-2-higher-geographies-2018-clipped-generalis-FGDB.zip	10.72	2023-04-26 09:36:19.912367

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
0	1.0	NaN	20160718211	NaN	{4C8F9436-7EA6-417E-B64F-15FCD44459F6}	2	Residential	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	60	Residential - Mixed Housing Urban Zone	NaN	285.664016	2.050275e+03	POLYGON ((1768030.306 5901206.846, 1768033.070...
1	2.0	NaN	20160718211	NaN	{604AAD87-8ED4-4111-8276-47CEE7E81F92}	1	Public Open Space	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	33	Open Space - Sport and Active Recreation Zone	NaN	1246.837757	1.684599e+04	POLYGON ((1764267.286 5919989.370, 1764218.153...
2	3.0	NaN	20160718211	NaN	{8D827DA8-BC5B-437A-B17A-532354F7D037}	4	Rural	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	11	Rural - Mixed Rural Zone	NaN	3582.113246	6.841744e+05	POLYGON ((1740091.195 5928308.839, 1740089.844...
3	4.0	NaN	20160718211	NaN	{96C9E266-3341-4C71-94F1-325F2EE45732}	2	Residential	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	23	Residential - Large Lot Zone	NaN	317.098469	6.024226e+03	POLYGON ((1750502.125 5928677.635, 1750456.131...
4	5.0	NaN	20160718211	NaN	{90B50FEE-45A3-4E88-819A-370751ACDE3D}	1	Public Open Space	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	31	Open Space - Conservation Zone	NaN	230.836636	5.639794e+02	POLYGON ((1741460.217 5918191.600, 1741476.745...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
130295	130296.0	NaN	20161115151	NaN	{B2F0FB45-80F6-41ED-AA57-A914B195B31E}	1	Public Open Space	20161115151	NaN	NaN	...	NaN	NaN	4	Operative	31	Open Space - Conservation Zone	NaN	179382.307787	9.040193e+07	POLYGON ((1738072.631 5902593.421, 1738095.386...
130296	130297.0	NaN	20161115151	NaN	{A3F4EF61-9162-43C3-90BD-DC84D93C6A64}	2	Residential	20161115151	NaN	NaN	...	NaN	NaN	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2769.292040	2.920602e+05	POLYGON ((1770189.642 5905789.775, 1770199.407...
130297	130298.0	NaN	20161115151	NaN	{A5FC7EB5-76E5-414C-96DD-715C671764B4}	4	Rural	20161115151	Kaipara South Head and Harbour coastal area	NaN	...	NaN	NaN	4	Operative	46	Rural - Rural Coastal Zone	NaN	27168.762393	6.344938e+06	POLYGON ((1730253.401 5955767.484, 1730254.399...
130298	130299.0	NaN	20161115151	NaN	{C371B83C-35CE-46A1-B94F-F1E592F271F7}	2	Residential	20161115151	NaN	NaN	...	NaN	NaN	4	Operative	8	Residential - Terrace Housing and Apartment Bu...	NaN	1812.109533	1.536829e+05	POLYGON ((1743032.759 5924130.564, 1742996.886...
130299	130300.0	NaN	20161115151	NaN	{31281924-C74D-4038-8260-FE6D886F4C94}	2	Residential	20161115151	NaN	NaN	...	NaN	NaN	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2511.267945	3.539606e+05	POLYGON ((1748979.767 5925771.895, 1748986.615...

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
20853	20854.0	NaN	20160718211	NaN	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	20161111011	NaN	NaN	...	NaN	NaN	4	Operative	58	NaN	NaN	72.629617	22.286945	POLYGON ((1767684.860 5903714.023, 1767691.955...
121765	121766.0	NaN	20160718211	NaN	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	20161111010	NaN	NaN	...	NaN	NaN	4	Operative	58	NaN	NaN	86.135341	4.114962	POLYGON ((1767684.083 5903713.690, 1767666.954...

	Area_Code_and_Description	Code	Description	2001_Census_total_households_in_occupied_private_dwellings	2006_Census_total_households_in_occupied_private_dwellings	2013_Census_total_households_in_occupied_private_dwellings	2001_Census_household_composition_for_households_in_occupied_private_dwellings_One-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Two-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Three_or_more_family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Other_multi-person_household	...	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_No_Access_to_Telecommunication_Systems	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Cellphone/Mobile_Phone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Telephone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Fax_Machine	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_the_Internet	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_stated	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings
0	MB 0000100	100	NaN	3.0	3.0	3.0	..C	..C	..C	..C	...	..C	3.0	..C	..C	..C	..C	..C	..C	..C	3.0
1	MB 0000200	200	NaN	27.0	27.0	30.0	15	3	0	0	...	3	24.0	3	9	24	6	15	30	3	30.0
2	MB 0000300	300	NaN	21.0	24.0	21.0	18	3	0	0	...	0	24.0	0	6	12	3	9	18	3	18.0
3	MB 0000400	400	NaN	9.0	9.0	9.0	6	..C	..C	..C	...	..C	12.0	..C	..C	9	..C	6	9	..C	9.0
4	MB 0000501	501	NaN	0.0	0.0	0.0	..C	..C	..C	..C	...	..C	0.0	..C	..C	..C	..C	..C	..C	..C	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49038	Footnotes for specific variables or categories	23	Median total household income is rounded to th...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49039	Symbols	..C	Confidential	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49040	Symbols	..	not available	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49041	Symbols	*	not able to be calculated	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49042	Source	Statistics New Zealand	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Meshblock	MeshblockNumber	Population_Count_Usual_Resident_2013	Population_Count_Census_Night_2013	geometry
0	MB 0352700	0352700	0	0	MULTIPOLYGON (((1753237.550 5923918.395, 17532...
1	MB 0728500	0728500	9	6	MULTIPOLYGON (((1761011.438 5905840.848, 17610...
2	MB 0829300	0829300	93	99	MULTIPOLYGON (((1739739.656 5899165.556, 17397...
3	MB 1280801	1280801	0	0	MULTIPOLYGON (((1869852.595 5695096.558, 18714...
4	MB 2360001	2360001	0	0	MULTIPOLYGON (((1623601.773 5423210.098, 16235...
...	...	...	...	...	...
46616	MB 0074002	0074002	0	0	MULTIPOLYGON (((1741178.600 6046572.236, 17414...
46617	MB 3208002	3208002	78	81	MULTIPOLYGON (((1770892.430 5911519.906, 17708...
46618	MB 3208003	3208003	36	36	MULTIPOLYGON (((1771025.156 5911674.629, 17709...
46619	MB 3209001	3209001	84	87	MULTIPOLYGON (((1771731.425 5912665.799, 17717...
46620	MB 3209002	3209002	75	81	MULTIPOLYGON (((1771758.103 5912422.760, 17717...

Phase 2: Geographic Datasets¶

Outputs¶

Local area dataset¶

Meshblock 2018 dataset:¶

Statistical Area 2 2018 dataset¶

Area Unit 2013 dataset¶

Maps¶

	Local_Area	geometry
OBJECTID
1	Auckland Central	POLYGON ((1755802.315 5921956.091, 1755861.443...
2	Beach Haven-Birkenhead-Northcote	POLYGON ((1757287.966 5925962.738, 1757211.333...
3	Botany	POLYGON ((1770748.846 5912611.168, 1770889.893...
4	Devonport-Takapuna	POLYGON ((1755276.581 5932026.336, 1755278.305...
5	East Coast Bays	POLYGON ((1756125.006 5940268.048, 1756139.852...
6	Franklin-Beachlands-Hunua	MULTIPOLYGON (((1804302.354 5890738.079, 17905...
7	Franklin-Pukekohe	POLYGON ((1765085.620 5897344.807, 1765096.229...
8	Franklin-Waiuku	POLYGON ((1744829.308 5899882.633, 1744835.760...
9	Henderson-Massey	POLYGON ((1745963.138 5923457.510, 1745945.884...
10	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...
11	Howick	POLYGON ((1771447.460 5916900.636, 1771474.703...
12	Mangere-Otahuhu	MULTIPOLYGON (((1766142.746 5910838.845, 17661...
13	Manurewa	POLYGON ((1769432.488 5904664.673, 1769471.312...
14	Maungakiekie	POLYGON ((1759418.674 5915517.660, 1759596.351...
15	Mt Albert	POLYGON ((1756291.061 5918265.104, 1756266.530...
16	Mt Eden	POLYGON ((1751910.995 5920299.659, 1751917.632...
17	Orakei	POLYGON ((1759833.395 5920429.502, 1759837.143...
18	Otara	MULTIPOLYGON (((1765765.640 5909501.655, 17657...
19	Pakuranga	POLYGON ((1769523.742 5920466.582, 1769527.337...
20	Papakura	POLYGON ((1772188.699 5902202.011, 1772192.566...
21	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...
22	Puketapapa	POLYGON ((1753132.892 5915040.177, 1753141.101...
23	Rodney-Dairy Flat	POLYGON ((1746055.938 5948651.765, 1746067.112...
24	Rodney-Helensville	MULTIPOLYGON (((1710567.626 5967865.416, 17106...
25	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...
26	Rodney-Warkworth	MULTIPOLYGON (((1761692.564 5984690.018, 17617...
27	Rodney-Wellsford	MULTIPOLYGON (((1746318.204 6000215.757, 17464...
28	Tamaki	POLYGON ((1765594.677 5917986.938, 1765609.808...
29	Titirangi	POLYGON ((1749038.038 5910572.842, 1749034.230...
30	Upper Harbour Local Board Area	MULTIPOLYGON (((1743880.554 5928979.708, 17438...
31	Waiheke	MULTIPOLYGON (((1793981.864 5931917.843, 17940...
32	Waitakere	MULTIPOLYGON (((1743890.310 5905057.188, 17438...
33	Whau	MULTIPOLYGON (((1748168.244 5916597.058, 17481...

	Name	Coast_indicator
OBJECTID
1	Auckland Central	True
2	Beach Haven-Birkenhead-Northcote	True
3	Botany	True
4	Devonport-Takapuna	True
5	East Coast Bays	True
6	Franklin-Beachlands-Hunua	True
7	Franklin-Pukekohe	True
8	Franklin-Waiuku	True
9	Henderson-Massey	True
10	Hibiscus Coast	True
11	Howick	True
12	Mangere-Otahuhu	True
13	Manurewa	True
14	Maungakiekie	True
15	Mt Albert	False
16	Mt Eden	True
17	Orakei	True
18	Otara	True
19	Pakuranga	True
20	Papakura	True
21	Papatoetoe	True
22	Puketapapa	True
23	Rodney-Dairy Flat	True
24	Rodney-Helensville	True
25	Rodney-Kumeu-Riverhead	True
26	Rodney-Warkworth	True
27	Rodney-Wellsford	True
28	Tamaki	True
29	Titirangi	True
30	Upper Harbour Local Board Area	True
31	Waiheke	True
32	Waitakere	True
33	Whau	True

	Code	SA12018_V1_00	SA22018_V1_00	SA22018_V1_00_NAME	UR2018_V1_00	UR2018_V1_00_NAME	IUR2018_V1_00	IUR2018_V1_00_NAME	CB2018_V1_00	CB2018_V1_00_NAME	...	WARD2018_V1_00	WARD2018_V1_00_NAME	LANDWATER	LANDWATER_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry	Centroid_lon	Centroid_lat
543	0137200	7001157	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	11	Island	0.097567	0.097567	1356.404986	MULTIPOLYGON (((1761489.925 5985285.678, 17614...	174.797456	-36.265557
544	0170700	7001317	114300	Gulf Islands	1098	Other rural Auckland	22	Rural other	07602	Hibiscus and Bays Local Board Area	...	07602	Albany Ward	11	Island	0.005545	0.005545	321.185380	MULTIPOLYGON (((1753753.763 5954256.206, 17537...	174.718386	-36.545155
545	0438700	7001130	111800	Barrier Islands	1098	Other rural Auckland	22	Rural other	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	70.725594	70.725594	100201.649851	MULTIPOLYGON (((1815954.125 6007877.906, 18159...	175.382085	-36.108549
546	0439306	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.078233	0.078233	1494.370224	MULTIPOLYGON (((1823569.424 5979782.392, 18235...	175.489541	-36.302663
547	0439307	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.475337	0.475337	3408.621475	MULTIPOLYGON (((1824128.365 5979223.530, 18242...	175.494343	-36.309033
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
53301	4011883	7001154	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	12	Mainland	7.680889	7.680889	17076.458542	MULTIPOLYGON (((1756196.931 5982761.262, 17562...	174.752390	-36.303185
53333	4011925	7009199	156900	Baverstock	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.242189	0.242189	2352.598074	MULTIPOLYGON (((1770681.901 5909197.803, 17711...	174.920613	-36.949951
53358	4011969	7010810	168600	Miranda-Pukorokoro	1152	Other rural Hauraki District	22	Rural other	01299	Area Outside Community	...	01201	Plains Ward	12	Mainland	12.056901	12.056901	16956.858556	MULTIPOLYGON (((1803340.448 5889954.878, 18033...	175.282719	-37.140292
53359	4011971	7009313	158600	Ormiston East	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.487587	0.487587	3692.342983	MULTIPOLYGON (((1771223.196 5906518.069, 17715...	174.930444	-36.975090
53360	4011972	7009335	158900	Tuscany Heights	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.178460	0.178460	1798.203501	MULTIPOLYGON (((1772642.183 5906246.394, 17726...	174.940243	-36.976625

	geometry	legs	distance	duration	weight_name	weight
0	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 6623.4, 'duration':...	6623.4	471.2	routability	482.5
1	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1487.9, 'duration':...	1487.9	201.1	routability	212.4
2	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 11247.2, 'duration'...	11247.2	783.1	routability	794.4
3	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1022.2, 'duration':...	1022.2	135.0	routability	146.3

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	13473.000000	13473.000000	13473.000000	13473.000000	13473.000000
mean	21210.377540	21231.831062	21204.024983	21208.543769	21172.238098
std	15885.697753	15877.057240	15883.868212	15888.923830	15897.410854
min	59.500000	29.900000	110.700000	155.200000	109.600000
25%	11533.800000	11530.200000	11494.100000	11531.700000	11480.400000
50%	17220.600000	17205.400000	17221.400000	17180.600000	17173.500000
75%	24347.100000	24427.000000	24379.100000	24391.400000	24381.600000
max	136633.200000	136633.200000	136633.200000	136633.200000	136633.200000

	Location	Latitude	Longitude	geometry
0	West Harbour	-36.810902	174.645571	POINT (174.64557 -36.81090)
1	Hobsonville	-36.787563	174.672260	POINT (174.67226 -36.78756)
2	Beach Haven	-36.789800	174.678317	POINT (174.67832 -36.78980)
3	Birkenhead	-36.822961	174.733882	POINT (174.73388 -36.82296)
4	Northcote	-36.826891	174.746329	POINT (174.74633 -36.82689)
5	Bayswater	-36.821697	174.766296	POINT (174.76630 -36.82170)
6	Stanley Bay	-36.828009	174.781236	POINT (174.78124 -36.82801)
7	Downtown	-36.842912	174.766930	POINT (174.76693 -36.84291)
8	Devonport	-36.833343	174.795631	POINT (174.79563 -36.83334)
9	Matiatia	-36.780617	174.991460	POINT (174.99146 -36.78062)
10	Pine Harbour	-36.889582	174.989955	POINT (174.98996 -36.88958)
11	Half Moon Bay	-36.879913	174.897656	POINT (174.89766 -36.87991)
12	Gulf Harbour	-36.624279	174.787779	POINT (174.78778 -36.62428)
13	Avondale	-36.897302	174.699092	POINT (174.69909 -36.89730)
14	Baldwin Avenue	-36.877694	174.720462	POINT (174.72046 -36.87769)
15	Britomart	-36.844177	174.767766	POINT (174.76777 -36.84418)
16	Ellerslie	-36.898225	174.808085	POINT (174.80809 -36.89822)
17	Fruitvale Road	-36.910636	174.667006	POINT (174.66701 -36.91064)
18	Glen Eden	-36.910149	174.652854	POINT (174.65285 -36.91015)
19	Glen Innes	-36.878788	174.854118	POINT (174.85412 -36.87879)
20	Grafton	-36.865501	174.770008	POINT (174.77001 -36.86550)
21	Greenlane	-36.889649	174.797444	POINT (174.79744 -36.88965)
22	Henderson	-36.880903	174.630923	POINT (174.63092 -36.88090)
23	Homai	-37.013442	174.874655	POINT (174.87465 -37.01344)
24	Kingsland	-36.872443	174.744520	POINT (174.74452 -36.87244)
25	Manukau	-36.993769	174.877389	POINT (174.87739 -36.99377)
26	Manurewa	-37.023261	174.896139	POINT (174.89614 -37.02326)
27	Meadowbank	-36.866298	174.820777	POINT (174.82078 -36.86630)
28	Middlemore	-36.963044	174.838964	POINT (174.83896 -36.96304)
29	Morningside	-36.874913	174.735210	POINT (174.73521 -36.87491)
30	Mt Eden	-36.867955	174.758970	POINT (174.75897 -36.86796)
31	Mt. Albert	-36.884793	174.714007	POINT (174.71401 -36.88479)
32	New Lynn	-36.909357	174.684020	POINT (174.68402 -36.90936)
33	Newmarket	-36.869622	174.778879	POINT (174.77888 -36.86962)
34	Onehunga	-36.925383	174.786012	POINT (174.78601 -36.92538)
35	Orakei	-36.862352	174.809466	POINT (174.80947 -36.86235)
36	Otahuhu	-36.946888	174.833261	POINT (174.83326 -36.94689)
37	Panmure	-36.898136	174.849269	POINT (174.84927 -36.89814)
38	Papatoetoe	-36.977583	174.849376	POINT (174.84938 -36.97758)
39	Papakura	-37.064926	174.946311	POINT (174.94631 -37.06493)
40	Parnell	-36.854689	174.777457	POINT (174.77746 -36.85469)
41	Penrose	-36.910093	174.815687	POINT (174.81569 -36.91009)
42	Puhinui	-36.989788	174.856028	POINT (174.85603 -36.98979)
43	Pukekohe	-37.203246	174.910155	POINT (174.91016 -37.20325)
44	Ranui	-36.867874	174.603303	POINT (174.60330 -36.86787)
45	Remuera	-36.881313	174.785404	POINT (174.78540 -36.88131)
46	Sturges Road	-36.873433	174.620874	POINT (174.62087 -36.87343)
47	Sunnyvale	-36.896779	174.631982	POINT (174.63198 -36.89678)
48	Swanson	-36.866032	174.576258	POINT (174.57626 -36.86603)
49	Sylvia Park	-36.914619	174.842589	POINT (174.84259 -36.91462)
50	Takanini	-37.041127	174.919446	POINT (174.91945 -37.04113)
51	Te Mahia	-37.031118	174.906152	POINT (174.90615 -37.03112)
52	Te Papapa	-36.920079	174.801432	POINT (174.80143 -36.92008)
53	Hibiscus Coast Bus Station	-36.624647	174.667158	POINT (174.66716 -36.62465)
54	Constellation Bus Station	-36.752014	174.728166	POINT (174.72817 -36.75201)
55	Sunnynook Bus Station	-36.761481	174.737999	POINT (174.73800 -36.76148)
56	Smales Farm Bus Station	-36.784523	174.751077	POINT (174.75108 -36.78452)
57	Akoranga Bus Station	-36.797232	174.760938	POINT (174.76094 -36.79723)

	ID	ROADID	SH	INTERCHANG	DISPLACEME	RAMPNO	LENGTH	DESCRIPTIO	geometry
110	2577.0	2577	016	0004	0.00	1	120.0	016-0000/07.98-X8-R5-OFF	LINESTRING Z (1752063.871 5917928.917 0.000, 1...
64	243.0	243	016	0021	0.00	1	434.0	016-0000/08.66-X8-R2-ON	LINESTRING Z (1756258.136 5918885.543 0.000, 1...
66	242.0	242	016	0020	0.00	1	548.0	016-0000/08.53-X8-R4-ON	LINESTRING Z (1757391.210 5918772.795 0.000, 1...
103	2612.0	2612	016	0006	0.00	1	57.0	016-0000/08.95-X8-R6-OFF	LINESTRING Z (1756746.516 5919867.854 0.000, 1...
59	244.0	244	016	0022	0.00	1	748.0	016-0000/08.95-X8-R3-OFF	LINESTRING Z (1756301.521 5918962.985 0.000, 1...
...	...	...	...	...	...	...	...	...	...
377	6297638.0	3167	01N	0389	NaN	3	256.0	01N-0389-R3	LINESTRING Z (1759980.354 5916071.625 0.000, 1...
383	6297630.0	3165	01N	0389	NaN	1	244.0	01N-0389-R1	LINESTRING Z (1758616.127 5917564.314 0.000, 1...
376	6297639.0	3168	01N	0389	NaN	4	290.0	01N-0389-R4	LINESTRING Z (1758109.827 5917758.328 0.000, 1...
381	6297613.0	3163	01N	0381	NaN	1	676.0	01N-0381-R1	LINESTRING Z (1763894.661 5912486.002 0.000, 1...
378	6297619.0	3164	01N	0381	NaN	2	933.0	01N-0381-R2	LINESTRING Z (1750801.737 5955470.851 0.000, 1...

	DESCRIPTIO	geometry
0	Maioro street southbound	POINT (1753591.008 5914436.768)
1	Maioro street northbound	POINT (1753494.457 5914425.528)

	General_Electoral_Population	Maori_Electoral_Population	GED2020_V1_00	GED2020_V1_00_NAME	GED2020_V1_00_NAME_ASCII	MED2020_V1_00	MED2020_V1_00_NAME	MED2020_V1_00_NAME_ASCII	LAND_AREA_SQ_KM	AREA_SQ_KM
MB2020_V2_00
100	-999	9	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	157.497825	157.497825
200	9	66	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	120.503770	120.503770
300	12	48	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	7.481859	7.481859
400	6	9	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	83.342952	83.342952
501	-999	-999	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	0.000000	63.825713
...	...	...	...	...	...	...	...	...	...	...
4012026	24	-999	60	Wellington Central	Wellington Central	6	Te Tai Tonga	Te Tai Tonga	8.301605	8.301605
4012027	108	6	56	Waikato	Waikato	1	Hauraki-Waikato	Hauraki-Waikato	5.064385	5.064385
4012028	57	-999	51	Taupō	Taupo	1	Hauraki-Waikato	Hauraki-Waikato	2.612804	2.612804
4012029	-999	-999	47	Taieri	Taieri	6	Te Tai Tonga	Te Tai Tonga	0.000000	143.908480
4012030	-999	-999	8	Dunedin	Dunedin	6	Te Tai Tonga	Te Tai Tonga	0.000000	1016.346178

	SA22018_V1_00	SA22018_V1_00_NAME	REGC2018_V1_00	REGC2018_V1_00_NAME	TA2018_V1_00	TA2018_V1_00_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry
33	110900	Dome Valley-Matakana	02	Auckland Region	076	Auckland	84.749966	84.749966	70031.926112	MULTIPOLYGON (((1749212.212 5981874.734, 17492...
34	111100	Warkworth West	02	Auckland Region	076	Auckland	11.359752	11.359752	22629.664017	MULTIPOLYGON (((1748589.106 5974509.939, 17485...
35	111200	Puhoi Valley	02	Auckland Region	076	Auckland	236.529965	236.529965	142134.544465	MULTIPOLYGON (((1740248.959 5974374.891, 17402...
36	112600	Waikoukou Valley	02	Auckland Region	076	Auckland	52.854583	52.854583	42565.729322	MULTIPOLYGON (((1735693.112 5942203.564, 17359...
37	112800	Hatfields Beach	02	Auckland Region	076	Auckland	0.923255	0.923255	4314.440531	MULTIPOLYGON (((1750981.811 5952164.661, 17512...
...	...	...	...	...	...	...	...	...	...	...
1061	165800	Rooseville Park	02	Auckland Region	076	Auckland	1.345809	1.345809	6010.945714	MULTIPOLYGON (((1770089.518 5882192.248, 17701...
1062	166000	Pukekohe Central	02	Auckland Region	076	Auckland	2.939129	2.939129	9211.796424	MULTIPOLYGON (((1769469.158 5881504.407, 17694...
1063	166300	Bombay Hills	02	Auckland Region	076	Auckland	31.682020	31.682020	37482.345738	MULTIPOLYGON (((1777896.083 5888522.045, 17779...
1064	166400	Ararimu	02	Auckland Region	076	Auckland	91.308241	91.308241	57552.107386	MULTIPOLYGON (((1786544.313 5891035.131, 17865...
1079	168600	Miranda-Pukorokoro	03	Waikato Region	012	Hauraki District	81.340404	81.340404	59717.399740	MULTIPOLYGON (((1803927.880 5898880.697, 18039...

	Name	geometry	Centroid_lon	Centroid_lat	Area	Residential_area	SH_area	MHS_area	MHU_area	THA_area	...	RC_area	RP_area	RCZ_area	RMU_area	RCL_area	Hdist_skytower	Coast_indicator	Census2013_population	Census2013_dwellings	Census2013_avg_HH_income
OBJECTID
1	Auckland Central	POLYGON ((1755802.315 5921956.091, 1755861.443...	174.753763	-36.855194	1.942334e+07	1.108573e+07	7.062543e+05	1.370472e+05	2.942080e+06	2.939136e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1067.517983	True	77058	31506.0	86499.398108
2	Beach Haven-Birkenhead-Northcote	POLYGON ((1757287.966 5925962.738, 1757211.333...	174.721897	-36.794764	3.406603e+07	1.476243e+07	1.672204e+06	2.379868e+06	1.039881e+06	1.402369e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6948.094501	True	82431	28344.0	80154.552207
3	Botany	POLYGON ((1770748.846 5912611.168, 1770889.893...	174.917904	-36.957505	3.978830e+07	1.240583e+07	2.595033e+06	1.210010e+07	2.528557e+06	9.609651e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	1.019299e+06	1.261118e+07	18420.075506	True	46878	13893.0	93325.464764
4	Devonport-Takapuna	POLYGON ((1755276.581 5932026.336, 1755278.305...	174.769923	-36.789009	2.111121e+07	1.437879e+07	4.003298e+05	1.887588e+06	2.665899e+06	5.945951e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6621.239572	True	55398	20307.0	89560.517751
5	East Coast Bays	POLYGON ((1756125.006 5940268.048, 1756139.852...	174.733122	-36.701804	3.038568e+07	1.691256e+07	1.083351e+06	3.296486e+04	2.890852e+06	2.219014e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.124826e+05	16467.095662	True	44634	15354.0	96602.171362
6	Franklin-Beachlands-Hunua	MULTIPOLYGON (((1804302.354 5890738.079, 17905...	175.101754	-37.043410	7.778987e+08	2.928810e+06	4.415845e+05	8.490284e+06	0.000000e+00	4.426904e+05	...	2.404916e+07	3.006035e+08	3.077544e+04	1.060471e+08	3.312952e+07	37193.822526	True	21111	7332.0	97416.701031
7	Franklin-Pukekohe	POLYGON ((1765085.620 5897344.807, 1765096.229...	174.864884	-37.151426	2.717220e+08	1.622679e+07	6.467033e+06	5.660850e+06	3.079772e+06	3.792095e+05	...	3.318708e+07	9.112082e+07	0.000000e+00	7.168090e+07	1.518285e+07	34851.656145	True	31176	10863.0	78094.119279
8	Franklin-Waiuku	POLYGON ((1744829.308 5899882.633, 1744835.760...	174.666624	-37.164727	2.997611e+08	8.372005e+06	1.763932e+04	1.007797e+07	2.703557e+06	0.000000e+00	...	9.149757e+07	4.023886e+07	0.000000e+00	1.386627e+08	6.572328e+05	36121.662648	True	13581	5046.0	68150.358852
9	Henderson-Massey	POLYGON ((1745963.138 5923457.510, 1745945.884...	174.621864	-36.853375	5.321961e+07	3.686649e+06	4.685951e+05	2.303941e+06	2.243794e+05	1.096004e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6.921767e+04	12525.129414	True	107658	34461.0	68347.842761
10	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...	174.720160	-36.618022	7.969308e+07	5.101724e+06	1.985154e+04	6.294665e+06	2.723733e+06	3.034730e+04	...	5.061610e+06	9.811656e+01	7.936355e+06	0.000000e+00	5.208255e+04	25832.060623	True	45141	17397.0	70786.765722
11	Howick	POLYGON ((1771447.460 5916900.636, 1771474.703...	174.926939	-36.902134	1.461575e+07	9.096320e+06	3.916487e+04	3.137190e+06	1.552850e+06	8.686144e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.121832e+07	15854.353763	True	40299	13836.0	85281.267636
12	Mangere-Otahuhu	MULTIPOLYGON (((1766142.746 5910838.845, 17661...	174.794437	-36.972479	5.250979e+07	4.569329e+06	3.637463e+06	1.601068e+06	4.024164e+04	4.937933e+05	...	0.000000e+00	2.710565e+06	0.000000e+00	0.000000e+00	1.217632e+07	14070.305771	True	70998	17427.0	61855.553636
13	Manurewa	POLYGON ((1769432.488 5904664.673, 1769471.312...	174.886011	-37.018487	3.711616e+07	1.402263e+07	2.429714e+06	7.750997e+06	9.649423e+05	5.216074e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	7.860819e+06	21865.784349	True	82230	22584.0	69202.861326
14	Maungakiekie	POLYGON ((1759418.674 5915517.660, 1759596.351...	174.799976	-36.915243	1.740899e+07	1.873652e+07	1.811262e+06	7.010512e+06	3.663284e+06	4.167398e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	8151.195589	True	27780	10368.0	76915.212333
15	Mt Albert	POLYGON ((1756291.061 5918265.104, 1756266.530...	174.765299	-36.884922	1.385353e+07	8.308876e+06	3.715019e+06	2.058002e+06	1.566702e+06	1.360481e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	4067.283622	False	47913	15630.0	97670.686671
16	Mt Eden	POLYGON ((1751910.995 5920299.659, 1751917.632...	174.718360	-36.879882	1.448821e+07	1.602986e+07	9.520285e+05	6.066849e+06	6.248986e+06	9.614689e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	5244.560531	True	46752	16155.0	83313.629602
17	Orakei	POLYGON ((1759833.395 5920429.502, 1759837.143...	174.830542	-36.870391	3.234712e+07	5.002987e+06	3.103315e+05	9.023214e+06	1.117549e+06	4.997783e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6567.770945	True	79581	29001.0	110143.425140
18	Otara	MULTIPOLYGON (((1765765.640 5909501.655, 17657...	174.883562	-36.963798	1.213648e+07	2.547122e+07	1.531569e+07	6.760630e+06	1.640568e+06	2.513619e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	16766.406523	True	30033	6693.0	58229.733394
19	Pakuranga	POLYGON ((1769523.742 5920466.582, 1769527.337...	174.891901	-36.898057	1.527965e+07	1.060461e+07	3.809348e+06	2.479102e+06	1.340626e+06	1.366092e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	12812.703793	True	39924	13206.0	82096.046353
20	Papakura	POLYGON ((1772188.699 5902202.011, 1772192.566...	174.940390	-37.059940	4.022528e+07	5.122441e+06	2.915926e+06	1.082742e+06	7.851764e+05	3.947975e+05	...	0.000000e+00	6.405900e+00	0.000000e+00	3.953037e+05	3.637283e+07	28340.533206	True	45687	14919.0	68043.070707
21	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...	174.849291	-36.987020	2.497254e+07	2.098467e+07	4.506156e+06	8.916239e+05	3.291655e+06	1.161878e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	17234.007915	True	45633	13272.0	65055.631090
22	Puketapapa	POLYGON ((1753132.892 5915040.177, 1753141.101...	174.741502	-36.916029	1.871805e+07	6.114247e+06	3.264382e+06	2.464966e+06	9.761411e+05	9.691525e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	7732.493593	True	52959	16695.0	75460.348858
23	Rodney-Dairy Flat	POLYGON ((1746055.938 5948651.765, 1746067.112...	174.636168	-36.672259	1.132876e+08	7.835879e+06	6.069391e+06	1.187048e+07	4.001187e+06	1.177736e+05	...	0.000000e+00	2.450833e+07	0.000000e+00	1.832241e+07	6.414765e+07	22546.984200	True	6564	2130.0	106361.971831
24	Rodney-Helensville	MULTIPOLYGON (((1710567.626 5967865.416, 17106...	174.425342	-36.646179	7.974650e+08	1.808030e+07	1.423108e+06	6.562767e+06	8.037448e+04	0.000000e+00	...	2.941202e+08	3.795723e+08	9.270628e+05	1.086005e+07	2.243177e+06	37521.293694	True	17832	6381.0	78202.750119
25	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...	174.538408	-36.780645	5.536146e+07	1.304669e+07	1.231326e+06	6.202250e+06	3.943621e+06	0.000000e+00	...	0.000000e+00	1.278216e+07	1.490339e+01	1.147058e+07	8.818457e+06	21330.923764	True	7257	2472.0	86943.048780
26	Rodney-Warkworth	MULTIPOLYGON (((1761692.564 5984690.018, 17617...	174.613063	-36.444991	6.684679e+08	1.681370e+07	5.952212e+06	1.799798e+05	6.208502e+04	0.000000e+00	...	1.413502e+08	3.579461e+08	1.421499e+07	3.244393e+07	1.297497e+03	46699.922300	True	17637	6912.0	62686.367596
27	Rodney-Wellsford	MULTIPOLYGON (((1746318.204 6000215.757, 17464...	174.538530	-36.291690	6.404974e+08	1.680348e+06	1.152092e+06	0.000000e+00	0.000000e+00	0.000000e+00	...	1.386196e+08	4.322863e+08	1.141594e+05	1.738165e+04	3.800007e+06	64927.125388	True	5595	2115.0	51240.028490
28	Tamaki	POLYGON ((1765594.677 5917986.938, 1765609.808...	174.848247	-36.901670	1.896657e+07	7.599913e+06	4.391057e+05	5.160719e+06	1.111391e+05	4.705511e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9688.801624	True	42189	13551.0	64390.277469
29	Titirangi	POLYGON ((1749038.038 5910572.842, 1749034.230...	174.629790	-36.939520	4.058868e+07	1.989760e+07	5.073543e+06	6.196787e+06	3.677952e+06	1.380604e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	8.173086e+06	15542.090769	True	35958	12192.0	80713.729357
30	Upper Harbour Local Board Area	MULTIPOLYGON (((1743880.554 5928979.708, 17438...	174.672015	-36.760234	6.973017e+07	9.380759e+06	1.026630e+06	1.234376e+07	1.652101e+06	2.503432e+05	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.221011e+03	12662.494326	True	53571	17079.0	92354.882984
31	Waiheke	MULTIPOLYGON (((1793981.864 5931917.843, 17940...	175.055216	-36.799894	1.547768e+08	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	26689.652804	True	8310	3597.0	55066.946309
32	Waitakere	MULTIPOLYGON (((1743890.310 5905057.188, 17438...	174.527874	-36.931281	2.651435e+08	3.041467e+07	5.403257e+06	1.005111e+06	1.438878e+07	0.000000e+00	...	5.398078e+06	1.009224e+07	7.682505e+06	0.000000e+00	9.088142e+06	22820.718458	True	12477	4449.0	84773.121192
33	Whau	MULTIPOLYGON (((1748168.244 5916597.058, 17481...	174.684472	-36.906902	2.682152e+07	8.270683e+06	1.092566e+06	4.710662e+05	3.332548e+06	2.009848e+06	...	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9497.105591	True	72585	23928.0	65948.529781

	Mtime_skytower_center	Mtime_skytower_north	Mtime_skytower_west	Mtime_skytower_east	Mtime_skytower_south
count	13473.000000	13473.000000	13473.000000	13473.000000	13473.000000
mean	1601.251763	1600.538974	1599.116611	1599.186566	1597.301700
std	3357.815639	3357.480155	3357.857065	3356.292671	3357.474225
min	15.100000	7.200000	26.700000	38.900000	37.900000
25%	867.800000	864.300000	865.700000	863.300000	863.000000
50%	1164.100000	1162.300000	1161.900000	1161.600000	1160.600000
75%	1519.000000	1516.900000	1515.400000	1516.800000	1513.700000
max	70521.800000	70521.800000	70521.800000	70521.800000	70521.800000

	t50_fid	name_ascii	macronated	name	hway_num	rna_sufi	lane_count	way_count	status	surface	geometry
46	100120164	OHURA ROAD	N	OHURA ROAD	43	3063799	2	NaN	NaN	sealed	MULTILINESTRING ((1726407.913 5648240.420, 172...
56	100047464	STATE HIGHWAY 27	N	STATE HIGHWAY 27	27	3031731	2	NaN	NaN	sealed	MULTILINESTRING ((1844110.982 5802925.948, 184...
62	100047472	STATE HIGHWAY 29A	N	STATE HIGHWAY 29A	29A	3025693	2	NaN	NaN	sealed	MULTILINESTRING ((1876759.340 5818523.003, 187...
68	100047481	STATE HIGHWAY 2	N	STATE HIGHWAY 2	2	3045160	2	NaN	NaN	sealed	MULTILINESTRING ((1875605.931 5823398.039, 187...
77	100089761	WEST COAST ROAD	N	WEST COAST ROAD	73	3072232	2	NaN	NaN	sealed	MULTILINESTRING ((1490507.178 5234796.972, 149...
...	...	...	...	...	...	...	...	...	...	...	...
138626	100121061	STATE HIGHWAY 5	N	STATE HIGHWAY 5	5	3068040	2	NaN	NaN	sealed	MULTILINESTRING ((1884714.508 5730000.000, 188...
138635	100121070	CHRISTCHURCH SOUTHERN MOTORWAY	N	CHRISTCHURCH SOUTHERN MOTORWAY	76	3061191	4	NaN	NaN	sealed	MULTILINESTRING ((1555118.439 5176036.767, 155...
138636	100121071	WESTERN BELFAST BYPASS	N	WESTERN BELFAST BYPASS	1	3079158	4	NaN	NaN	sealed	MULTILINESTRING ((1567931.555 5188634.481, 156...
138637	100121072	CHRISTCHURCH NORTHERN MOTORWAY	N	CHRISTCHURCH NORTHERN MOTORWAY	74	0	4	NaN	NaN	sealed	MULTILINESTRING ((1571390.312 5190959.558, 157...
138646	100121096	RUSSLEY ROAD	N	RUSSLEY ROAD	1	3071341	4	NaN	NaN	sealed	MULTILINESTRING ((1563395.550 5183811.195, 156...

	t50_fid	name_ascii	macronated	name	status	track_type	rway_use	veh_type	geometry
0	100120519	PALMERSTON NORTH - GISBORNE LINE	N	PALMERSTON NORTH - GISBORNE LINE	disused	single	NaN	NaN	LINESTRING (2026018.548 5694000.000, 2026005.7...
1	7927728	NaN	N	NaN	disused	single	NaN	NaN	LINESTRING (1845787.041 5784240.726, 1845749.1...
2	2462744	PALMERSTON NORTH - GISBORNE LINE	N	PALMERSTON NORTH - GISBORNE LINE	disused	single	NaN	NaN	LINESTRING (1972000.000 5666908.227, 1971949.1...
3	2462838	TANEATUA BRANCH	Y	TĀNEATUA BRANCH	disused	single	NaN	NaN	LINESTRING (1948000.000 5782634.539, 1947964.5...
4	6225482	KINGSTON BRANCH	N	KINGSTON BRANCH	disused	single	NaN	NaN	LINESTRING (1261528.423 4961310.584, 1261546.2...
...	...	...	...	...	...	...	...	...	...
527	100118890	STRATFORD - OKAHUKURA LINE	N	STRATFORD - OKAHUKURA LINE	NaN	single	NaN	NaN	LINESTRING (1732000.000 5657175.250, 1732026.9...
528	100118892	NORTH ISLAND MAIN TRUNK	N	NORTH ISLAND MAIN TRUNK	NaN	single	NaN	NaN	LINESTRING (1794254.935 5696679.903, 1794254.8...
529	100121048	STILLWATER - NGAKAWAU LINE	Y	STILLWATER - NGĀKAWAU LINE	NaN	single	NaN	NaN	LINESTRING (1492000.000 5319908.176, 1491999.4...
530	100121049	STILLWATER - NGAKAWAU LINE	Y	STILLWATER - NGĀKAWAU LINE	NaN	single	NaN	NaN	LINESTRING (1484276.055 5375189.782, 1484283.2...
531	100121050	MURUPARA LINE	N	MURUPARA LINE	NaN	single	NaN	NaN	LINESTRING (1932349.458 5766011.454, 1932334.6...

	Code	Name	REGC2018_V1_00	REGC2018_V1_00_NAME	TA2018_V1_00	TA2018_V1_00_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry	Centroid_lon	Centroid_lat	Centroid_local_area
168	169800	Mangatangi	03	Waikato Region	013	Waikato District	257.805407	257.805407	136907.044348	MULTIPOLYGON (((1792445.658 5901172.505, 17925...	175.175802	-37.155006	NaN
629	111800	Barrier Islands	02	Auckland Region	076	Auckland	320.414101	320.414101	371260.805871	MULTIPOLYGON (((1815954.125 6007877.906, 18159...	175.387745	-36.198250	NaN

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	558.000000	558.000000	558.000000	558.000000	558.000000
mean	21911.780108	21955.061470	21906.781900	21905.859498	21886.108781
std	15934.400881	15952.867723	15940.149558	15930.945196	15952.271608
min	393.900000	487.300000	213.100000	401.800000	286.000000
25%	12005.425000	12202.525000	12124.725000	12033.725000	12054.425000
50%	18320.850000	18210.300000	18232.750000	18121.850000	18231.950000
75%	26917.775000	26952.325000	27317.075000	26914.500000	27079.075000
max	136328.400000	136328.400000	136328.400000	136328.400000	136328.400000

	Area_code_and_description	Area_description	Census_2006_usually_resident_population_count	Census_2013_usually_resident_population_count	Census_2018_usually_resident_population_count	Census_2006_census_night_population_count	Census_2013_census_night_population_count	Census_2018_census_night_population_count	Census_2018_Unit_record_data_source_11_2018_Census_individual_form_CURP	Census_2018_Unit_record_data_source_12_Individuals_on_the_household_listing_only_CURP	...	Census_2013_Maori_descent_04_Dont_know_CURP	Census_2013_Maori_descent_Total_stated_CURP	Census_2013_Maori_descent_99_Not_elsewhere_included_CURP	Census_2013_Maori_descent_Total_CURP	Census_2018_Maori_descent_01_Māori_descent_CURP	Census_2018_Maori_descent_02_No_Māori_descent_CURP	Census_2018_Maori_descent_04_Dont_know_CURP	Census_2018_Maori_descent_Total_stated_CURP	Census_2018_Maori_descent_99_Not_elsewhere_included_CURP	Census_2018_Maori_descent_Total_CURP
Area_code
7000000	SA1 7000000	7000000	165	144	141	177	150	138	114	6	...	3	132	12	144	135	6	3	141	0	141
7000001	SA1 7000001	7000001	93	105	114	117	108	120	87	3	...	3	87	15	105	96	18	0	114	0	114
7000002	SA1 7000002	7000002	0	0	0	0	0	0	C	C	...	C	C	C	0	C	C	C	C	C	0
7000003	SA1 7000003	7000003	216	171	225	219	168	222	171	12	...	3	144	24	171	210	15	0	225	0	225
7000004	SA1 7000004	7000004	90	102	138	90	105	129	117	3	...	0	75	27	102	102	30	3	138	0	138
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16	16 Tasman Region	Tasman Region	44625	47157	52389	48306	51258	55206	46428	1443	...	1074	43251	3903	47157	5589	45471	1329	52389	0	52389
17	17 Nelson Region	Nelson Region	42888	46437	50880	45372	48444	53082	44922	1680	...	1173	42528	3909	46437	6336	43167	1377	50880	0	50880
18	18 Marlborough Region	Marlborough Region	42558	43416	47340	46179	46302	50562	41457	1515	...	1104	39237	4179	43416	7341	38583	1416	47340	0	47340
99	99 Area Outside Region	Area Outside Region	618	600	669	660	600	822	618	33	...	24	546	54	600	432	210	27	669	0	669
total	Total NZ (Regional Council)	Total NZ (Regional Council)	4027947	4242048	4699755	4143282	4353198	4793358	3971892	203010	...	87234	3821445	420603	4242048	869850	3715050	114855	4699755	0	4699755

	Name	Census2018_population
1000	Ormiston South	5514
982	Papatoetoe Central	5085
1059	Pukekohe West	4980
706	Aorere South	4944
919	Point England	4923
137	Weymouth North	4911
927	Mount Wellington Central	4827
974	Papatoetoe North	4779
788	Sunnyvale East	4773
128	Papatoetoe West	4755
747	Birkdale South	4725
786	Glendene North	4686
837	Clover Park North	4683
1056	Pukekohe North West	4674
749	Birkenhead West	4653
844	New Lynn South	4650
1011	Homai West	4605
796	Point Chevalier East	4602
869	Mount Roskill White Swan	4596
732	Massey South	4554

	Unnamed: 0	Loss	Zero income	$1-$5,000	$5,001-$10,000	$10,001-$15,000	$15,001-$20,000	$20,001-$25,000	$25,001-$30,000	$30,001-$35,000	...	$50,001-$60,000	$60,001-$70,000	$70,001-$100,000	$100,001-$150,000	$150,001 or More	Total stated	Not stated	Median Household\n Income ($)	Mean Household \nIncome ($)	Total
Unnamed: 0
100100	100100 North Cape	3.0	6.0	3.0	12.0	12.0	45.0	66.0	12.0	21.0	...	48.0	39.0	69.0	60.0	42.0	525.0	105.0	49400.0	65300.0	630.0
100200	100200 Rangaunu Harbour	3.0	6.0	3.0	9.0	30.0	48.0	78.0	24.0	21.0	...	42.0	63.0	123.0	96.0	48.0	693.0	81.0	56400.0	67000.0	774.0
100300	100300 Inlets Far North District	0.0	0.0	0.0	3.0	0.0	3.0	6.0	0.0	0.0	...	0.0	0.0	3.0	0.0	3.0	30.0	30.0	24400.0	41300.0	60.0
100400	100400 Karikari Peninsula	3.0	0.0	6.0	0.0	12.0	12.0	54.0	12.0	18.0	...	30.0	27.0	57.0	51.0	24.0	393.0	69.0	47200.0	62800.0	465.0
100500	100500 Tangonge	3.0	3.0	0.0	0.0	6.0	12.0	30.0	9.0	6.0	...	24.0	24.0	54.0	69.0	36.0	330.0	57.0	67100.0	81600.0	390.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
400013	400013 Snares Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400014	400014 Oceanic Antipodes Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400015	400015 Antipodes Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400016	400016 Ross Dependency	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
Total	Total New Zealand	4974.0	10152.0	15312.0	14538.0	32031.0	59175.0	105777.0	45423.0	45849.0	...	107058.0	97539.0	242262.0	295119.0	272208.0	1526961.0	126831.0	75700.0	91300.0	1653792.0

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	409.000000	409.000000	409.000000	409.000000	409.000000
mean	22795.600244	22781.618093	22788.159902	22784.258191	22748.530073
std	16379.256603	16359.032083	16357.011292	16376.317853	16403.724191
min	557.400000	715.700000	707.700000	1143.700000	446.000000
25%	12472.900000	12402.700000	12501.100000	12532.200000	12441.400000
50%	18613.100000	18513.300000	18539.800000	18517.400000	18512.800000
75%	27316.300000	27355.900000	27221.000000	27385.600000	27368.200000
max	89091.100000	89018.800000	89160.200000	87888.800000	89163.300000

	Area_Code_and_Description	Code	Description	2001_Census_census_usually_resident_population_count(1)	2006_Census_census_usually_resident_population_count(1)	2013_Census_census_usually_resident_population_count(1)	2001_Census_census_night_population_count(2)	2006_Census_census_night_population_count(2)	2013_Census_census_night_population_count(2)	2001_Census_sex_for_the_census_usually_resident_population_count(1)_Male	...	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Don't_Know	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people_stated	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Not_Elsewhere_Included(14)	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Maori_Descent	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_No_Maori_Descent	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Don't_Know	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people_stated	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Not_Elsewhere_Included(14)	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people
0	MB 0000100	0000100	NaN	9.0	3.0	3.0	15.0	9.0	9.0	6	...	..C	..C	..C	3.0	..C	..C	..C	..C	..C	3.0
1	MB 0000200	0000200	NaN	90.0	87.0	84.0	99.0	96.0	84.0	42	...	0	81	6	90.0	69	3	3	75	9	84.0
2	MB 0000300	0000300	NaN	90.0	72.0	57.0	93.0	72.0	54.0	42	...	0	66	6	75.0	54	0	0	57	3	57.0
3	MB 0000400	0000400	NaN	30.0	21.0	30.0	45.0	39.0	30.0	18	...	0	21	3	21.0	12	6	3	21	6	27.0
4	MB 0000501	0000501	NaN	0.0	0.0	0.0	0.0	0.0	0.0	..C	...	..C	..C	..C	0.0	..C	..C	..C	..C	..C	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49028	Footnotes for specific variables or categories	13	Consists of people who were too young to talk ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49029	Footnotes for specific variables or categories	14	Consists of response unidentifiable and not st...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49030	Symbols	..C	confidential	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49031	Symbols	*	not able to be calculated	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49032	Source	Statistics New Zealand	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Census Night Area Unit (2013 Areas)	($) Mean Household Income
Census Night Area Unit (2013 Areas)
NaN	NaN	NaN
500100	500100 Awanui	59700.0
500202	500202 Karikari Peninsula-Maungataniwha	49500.0
500203	500203 Taipa Bay-Mangonui	50400.0
500204	500204 Herekino	63100.0
...	...	...
This	This data has been randomly rounded to protect...	NaN
Mean	Mean has been rounded to nearest $100.	NaN
..C	..C has been inserted in cells that have been ...	NaN
NaN	NaN	NaN
Source:	Source: Stats NZ	NaN

	Code	Name	AREA_SQ_KM	Area	Shape_Length	geometry	Centroid_lon	Centroid_lat	Centroid_local_area	Hdist_skytower	...	RC_area	RP_area	RCZ_area	RMU_area	RCL_area	Coast_indicator	Census2013_population	Census2013_dwellings	Census2013_avg_HH_income	Census2013_med_HH_income
30	505604	Kumeu East	13.475480	13.310426	24949.702400	MULTIPOLYGON (((1741052.061 5931333.839, 17409...	174.575870	-36.770909	Rodney-Kumeu-Riverhead	18712.837132	...	0.000000e+00	3.415839e+04	0.000000	2.501360e+07	3.583400e+06	True	1449.0	474.0	98300.0	87700.0
31	505605	Kumeu West	24.388271	24.388271	32928.601801	MULTIPOLYGON (((1734788.301 5932715.055, 17348...	174.524933	-36.774538	Rodney-Kumeu-Riverhead	22696.426769	...	0.000000e+00	2.181426e+07	0.000000	4.157821e-02	2.410624e+05	False	1725.0	609.0	106200.0	95300.0
32	505804	Hatfields Beach	0.908867	0.908867	5395.241593	MULTIPOLYGON (((1751816.238 5952425.382, 17518...	174.690104	-36.569595	Hibiscus Coast	31594.334161	...	5.407535e+06	0.000000e+00	45.869441	0.000000e+00	0.000000e+00	True	1380.0	492.0	81500.0	74100.0
33	505805	Orewa	5.572505	4.229676	19755.382361	MULTIPOLYGON (((1750518.569 5950940.087, 17505...	174.686690	-36.589013	Hibiscus Coast	29556.184420	...	0.000000e+00	7.095903e+07	217.527635	0.000000e+00	0.000000e+00	True	8520.0	3843.0	65000.0	47600.0
34	505902	Manly	3.188420	3.188420	12908.971485	MULTIPOLYGON (((1756245.400 5945964.220, 17562...	174.759445	-36.630419	Hibiscus Coast	24183.805546	...	0.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	True	6552.0	2535.0	78300.0	63800.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
967	525922	Bledisloe Park	9.643551	9.643551	16858.446790	MULTIPOLYGON (((1770838.870 5881130.225, 17709...	174.915328	-37.215688	Franklin-Pukekohe	42980.054511	...	0.000000e+00	6.398755e+07	0.000000	0.000000e+00	0.000000e+00	False	2934.0	1092.0	78400.0	70100.0
968	526103	Waiuku West	2.198567	2.198567	9682.515485	MULTIPOLYGON (((1753228.371 5877597.817, 17532...	174.724591	-37.248157	Franklin-Waiuku	44490.721194	...	0.000000e+00	0.000000e+00	0.000000	1.240984e+08	0.000000e+00	True	3102.0	1221.0	70700.0	56900.0
969	526104	Waiuku East	3.200239	3.158494	9009.196620	MULTIPOLYGON (((1753749.548 5877735.141, 17537...	174.738476	-37.247818	Franklin-Waiuku	44377.597242	...	0.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	True	3498.0	1248.0	73000.0	60500.0
970	526105	South Waiuku	2.247383	2.247383	9084.157884	MULTIPOLYGON (((1755346.188 5875198.493, 17551...	174.739741	-37.259971	Franklin-Waiuku	45719.614106	...	0.000000e+00	2.712553e+06	0.000000	2.030531e+05	0.000000e+00	False	1599.0	546.0	93200.0	85600.0
1962	616300	Browns Island	0.595336	0.595336	3430.690993	MULTIPOLYGON (((1769155.864 5922005.512, 17691...	174.894441	-36.830617	Waiheke	11959.563577	...	0.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	True	0.0	0.0	NaN	NaN