THEME

SPATIAL SCALE

PERIOD

ORIGINAL SOURCE HOLDER^*

COMMENTS

Air pollution (Escape)^T

Address, PC6

2009

Institute of Risk Assessment Sciences (IRAS), European Study of Cohort for Air Pollution Effects (ESCAPE)

Annual average outdoor air pollution concentrations (NO2, NOx, PM2.5, PM10, PM2.5 absorbance and oxidative potential (OP) of PM -  esr/dtt)

Air pollution (Escape)^T

Neighborhood

2009

Institute of Risk Assessment Sciences (IRAS), European Study of Cohort for Air Pollution Effects (ESCAPE)

Annual average outdoor air pollution concentrations (NO2, NOx, PM2.5, PM10, PM2.5 absorbance) per neighborhood

Air pollution^G

   PM_2.5

   PM₁₀

   NO₂

   SOOT (EC)

25x25 m resolution

Address, PC6, PC4

2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

(NO₂ not for 2013)

Institute for Public Health and the Environment (RIVM), Atlas Leefomgeving (ALO), http://www.atlasleefomgeving.nl

Annual average outdoor pollution concentrations modeled / interpolated on  the basis of measurement data, traffic data and the physical environment. All data can be viewed in https://www.atlasleefomgeving.nl/kaarten

Air pollution GCN^G

 C₆H₆ (benzeen)

 CO (koolmonoxide)

COP98 (koolmon. p98)

 PM_2.5 (fijnstof)

 PM₁₀ (fijnstof)

 NH₃ (ammoniak)

 NO₂ (stikstofdioxide)

 NO_x (stikstofoxiden)

 O₃  (ozon)

SOOT (EC - roet)

 SO₂ (zwaveldioxide)

1x1 km resolution

Address, PC6, PC4

2011, 2021-23 (C₆H₆)

2011 (CO)

2011 (COP98)

2007-2023 (PM_2.5)

1995-2023 (PM₁₀)

2011-2023 (NH₃)

1995-2023 (NO₂)

2011-2023 (NO_x)

2011-2023 (O₃)

2011-2023 (SOOT – EC)

2011-2023 (SO₂)

Institute for Public Health and the Environment (RIVM).  GCN large scaled concentration and deposition maps

Annual average outdoor pollution concentrations based on a combination of model calculations and measurements from official measurement locations. SOOT (EC) maps must be seen as indicative only.

Apart from ‘benzeen’ (C₆H₆) and ‘koolmonoxide’ (CO) modeled future concentrations of all variables are available for the years 2025 and 2030.

Accessibility^T as distance to highway exits/entrances and public transport stops (long distance)

PC6, PC4

2010, 2012, 2019, 2021

2009/2010, 2012, 2018/2019

TOP10 NL/Kadaster

Prorail/ESRI,  Rijks Universiteit Groningen

Euclidean distance to highway exits/entrances

Euclidean distance to long distance transport stops (train, metro) and for 2018 as well ferry

Accessibility^T

PC4

1998 till 2003 (yearly),

2005

ABF Research (SWING Vastgoedmonitor
2007)

Data on accessibility (e.g., number of jobs and green spaces that can be reached via the road or by train within a certain time (15, 30, 45, 60 minutes)

Accessibility of population^T
Accessibility of households^T

PC4

2013

ABF Research (Real Estate Monitor 2015)

Data on accessibility of population and households (e.g., number of individuals and households that can be reached  by  bike or by  car  within  a  certain  time  (15, 30,45,60 minutes)

Altitude (AHN)^G

Address, PC6

(raster 50 cm – 25 m)

Ca. 2000 (AHN 1)

Ca. 2010 (AHN 2)

Ca. 2018 (AHN 3)

Cooperation of provinces, central
government and water boards

The altitude map of the Netherlands is a raster product available on different horizontal scale levels ranging from 25 meter resolution (AHN-1) to 50 cm resolution (AHN-3)

Basisregistratie Adressen en Gebouwen (BAG)^G

Address, PC6 1:2.500

(vector – point/polygon)

2015, 2018, 2020, 2021

Kadaster
 

Vector dataset with houses, addresses and attribute data on utilization functions, construction year and area.

Bicycle paths BGT and TOP10^G

Address, PC6

1:5.000 - 1:10.000
(vector – polygon)

2019

Kadaster

Selections from polygons in the BGT and lines in the TOP10 concerning respectively separate bicycle paths (BGT), designated bicyle lanes or mixed roads (TOP10)

Bicycle path density^G

Neighborhood

2019 (for neighborhood boundaries 2016)

Landelijk Fietsplatform, Kadaster

This dataset combines bicycle paths from two lines sources (TOP10 and Landelijk fietsplatform) and one polygon input source (BGT). All data is transformed to polygons and summarized as area density per neighborhood.

Bicycle and walking networks^G

Address, PC6

1:10.000 (vector - line)

2019 (continuously updated by provider)

Landelijk Fietsplatform and Wandelnet
via https://www.routedatabank.nl

These datasets include cycling and walking routes, networks and transport nodes and are based on TOP10 NL road data.

Childcare facilities^T

PC4

2011 till 2015 (yearly)

National Childcare and Playgroup Register
(Real Estate Monitor 2015)

Data on a range of childcare facilities (e.g., number of KDV’s, BSO’s and playgroups)

Cultural facilities^T

PC4

2001 till 2007 (yearly)

Museum Association (SWING Vastgoed-

monitor 2007) Netherlands Theatre Institute (SWING Vastgoedmonitor 2007) Dutch Federation for Cinematography (SWING Vastgoedmonitor 2007) Adresdata / ABF Research (Real Estate Monitor 2015)

Data on a range of cultural facilities (e.g.,  number   of   museums, theatres, poppodia  and cinema’s)

Educational facilities^T

PC4

1996 till 2007 (yearly)

Centrale Financiën Instellingen (CFI) (SWING Vastgoedmonitor 2007)

Data on educational facilities (e.g., number of educational  facilities  and number  of students stratified for educational level, sex, and age)

Food environment (grouped)  ^{G, T}

Address, PC6

(in kernel density radii of 500, 1000 and 1500 m)

2004, 2006, 2008, 2010,

2012, 2014, 2016, 2018

Densities for other radii or years between 2004 and 2018 can be made on request

LOCATUS

This dataset contains the kernel density of different groups of aggregated of food retailers (local food shops, fast food restaurants, food delivery, restaurants, supermarkets, small grocery/convenience stores and all other food retailers).

Food environment healthiness-index
(kernel densities) ^{G, T}  

Address, PC6

(in kernel density radii of 500, 1000, 3000 and 5000 m)

2004*, 2006, 2008*, 2010,

2012, 2014*, 2016, 2018

* with additional radii of 3000 and 5000 m.

Densities for other radii or years between 2004 and 2018 can be made on request

LOCATUS

Kernel density of the total health score of food retailers within different kernel radii (500, 1000, 3000 and 5000 meter) according to the calculated food environment healthiness index (FEHI) for each retailer. The FEHI Index has values between -5 and + 5 according to the FEHI score list developed by Maartje Poelman (Timmermans et al., 2018). 

Food environment healthiness-index^{G, T}

Neighborhood

2016
Neighbourhood scores can be produced on request for all years with available Locatus
data between 2004 and 2018

LOCATUS

Index score (food environment healthiness index) between -5 and + 5 according to FEHI score list by Poelman et al., 2018. The data is aggregated to neighbourhoods using point density kernels to prevent MAUP (Modifiable Areal Unit Problem) issue.

Green space^G

   Green space

   Trees

   Tree height

   Shrubs

   Low vegetation

Address, PC6

(10 m raster)

2017

Institute for Public Health and the Environment (RIVM), Atlas Leefomgeving (ALO), http://www.atlasleefomgeving.nl

Different datasets related to green space were assembled by the RIVM on a 10x10 meter resolution expressed as percentage green, trees, shrubs or low vegetation per grid cell and is derived from the AHN2 and AHN3 files (“Actueel Hoogtebestand Nederland”, resolution of 0.5 m), the BAG buildings (“Basisregistratie Adressen en Gebouwen”) and the Infrared aerial photo (CIR file, resolution of 0.25 m).

Green space density^GT

(component walkability index)

Address, PC6,

PC4, 25 m raster and neighborhood

1989, 1993, 1996, 2000, 2003, 2006, 2008, 2010, 2012, 2015, 2017

Statistics Netherlands (CBS)

Greenspace density as Z-scores or as raw values based on CBS soilstatistics (BBG bodemgebruiks-bestanden). This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Health care facilities^T

PC4

2003-2007 (yearly)

Vestigingen en bedden in de zorg (SWING Vastgoedmonitor 2007)

Data on health care facilities

(e.g., number of several specific health care facilities)

House transactions and average house prices^T

PC4

2000 till 2015 (yearly)

Kadaster (Real Estate Monitor 2015)

Data on transactions and average house prices (e.g., number of transactions stratified for house type, and average house price stratified for house type)

Housing benefits^T

PC4

1998 till 2006 (yearly)

Belastingdienst (SWING Vastgoedmonitor 2007)

Data on housing benefits (e.g.,   data on recipients  and   height/sum   of   housing benefits)

Housing stock^T

PC4

1998 till 2007 (yearly)

ABF Research (SYSWOV 2007)

Data on housing stock. (e.g.,  number  of owner-occupied  and  rental  housing, social rent, and housing stock stratified for house type)

Income^T

PC4

2009, 2012

Statistics Netherlands (CBS)

Data on income (e.g., disposable income, capital and households in PC4 areas)

Key statistical figures^G

100x100 m.
(‘CBS vierkantstatistie-ken’) PC6 / PC4

2011 till 2021 (yearly)

2000 till 2010

Statistics Netherlands (CBS)

The CBS dataset vierkantstatistieken 100x100 meter contains basic statistics on number of inhabitants, dwellings, residential density and urbanity for all years and additional statistics from 2011 onwards including densities of and distances to several destination types. Number of available variables in postcode 4 and 6 areas depends of exact year. For older years from 2000 onwards some of the variables are only available on a 500x500 meter level, see e.g. urbanity in this list.

Key statistical figures^T

PC6

2004, 2008, 2010, 2012,

2014-2020 (yearly)

Statistics Netherlands (CBS)

Data on key figures (e.g., demographics, income, immigrants, housing stock)

Key statistical figures^G

PC4

1998 till 2020 (yearly)

Statistics Netherlands (CBS)

Data on key figures (sex and age of inhabitants, household composition, % immigrants) with additional statistics from 2015 onwards

Land use^G

Address, PC6

1:10.000
(vector – polygon)

1996, 2000, 2008, 2010, 2012, 2015, 2017

Statistics Netherlands (CBS)

Classification in 9 main land use classes and ca. 40 subclasses. Land use data also exists for 1989, 1993, 2003 and 2006. These datasets can be requested at CBS.

Land use mix / entropy index^GT

(component walkability index)

Address, PC6,

PC4, 25 m raster and neighborhood

1989, 1993, 1996, 2000, 2003, 2006, 2008, 2010, 2012, 2015, 2017

Statistics Netherlands (CBS)

Land use mix or entropy index as Z-scores or as raw values based on CBS soilstatistics (BBG bodemgebruiksbestanden). This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Land use^T

PC4

1996 till 2003 (yearly)

Statistics Netherlands (CBS)

Data on land use (e.g. hectares/percentages of urban/rural land use, green spaces,  forests, parks, traffic, public facilities, recreational areas, etc)

Light emission at night ^GT

PC6

(300 m raster)

2006 (DMSP OLS F16)

2012 (VIIRS)

2015 (VIIRS)

2018 (VIIRS)

Earth Observations Group (EOG) at NOAA/NCEI via direct data request atlasleefomgeving.nl / RIVM / Netherlands

The 2006 dataset (700 m res.) originates from satellite number F16 from the DMSP-OLS program and is not directly comparable to the VIIRS datasets from 2012 onwards.

The 2012-2018 datasets VIIRS Cloud Mask, Version 1 Nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) composites are annual composites (300 m res.) expressing light emission in 10-10 Watt per cm2 per steradian.

Livability^T
(Leefbaarometer 2.0 / 3.0)

100x100 m, PC4, neighbourhood

1998¹ 2002, 2008, 2010¹, 2012, 2014, 2016, 2018, 2020, 2022

¹only for Leefbaarometer 1.0

Dutch Ministry of the Interior and Kingdom Relations

Data on livability (Livability  scores in the Leefbaarometer 2.0 are based on ca. 100 factors on population, social cohesion, public space, safety, level of resources, and housing that are aggregated circular buffers around the central postcode  6 areas).

The score is divided in 9 livability classes from 1 (very insufficient) to 9 (excellent). A detailed description of the development of this instrument can be found here.

The years 1998 and 2010 are only available from the previous Leefbaarometer 1.0 with a different indicator set and scores cannot be compared directly to the new version.

Living environment typology^T

PC4

2006, 2015

ABF Research (SWING Vastgoedmonitor 2007)
Strabo – Bureau voor Ruimtelijk Marktonderzoek (Real Estate Monitor 2015)

Data on area types (e.g., center - urban, outside center, green-urban)

Neighbourhood characteristics^G

Neighbourhood

1995, 1997, 1999, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Statistics Netherlands (CBS)

Data on neighbourhood characteristics. This concerns data on for example urbanization, population, living, energy consumption, education, labor, income, social security, businesses, motor vehicles, area, land use, average distance to specific facilities / and average number of specific facilities within dedicated travel distances (calculated over the road network) from residence addresses per neighbourhood (available from 2008 onwards).

We have also neighborhood maps of 1988, 1993 and 1994, but these lack statistical data.

Noise railways 2016^G

Address, PC6

2016

Ministery of I&W

Data only available for ‘hoofdspoornet’ (main railway system) for day and night (noise in Lden)

Noise Schiphol airport 2016^G

Address, PC6

2016 (measurement period
nov. 2015-nov. 2016)

Ministery of I&W

Separate data available for day and night (noise in Lden)

Noise traffic - daily mean (mixed road, rail and air)^T

Address, PC6, PC4
(25 meter raster)

2000, 2004, 2005, 2007

and 2008

PBL Netherlands Environmental Assessment Agency

Modeled data with Empara noise tool with 25x25 m resolution on traffic noise (mixed road, rail and air traffic noise in dB) in 2000, 2004, 2005, 2007 and 2008.

Noise mixed cumulative yearly average from roads, rail, air, industry and wind turbines ^GT

Address, PC6
(10 meter raster)

2011 / 2017
(aggregated periods 2008-2012

and 2016-2020)

Atlasleefomgeving.nl / RIVM

(direct request)

Calculated with the standard method (RMV2012) using the following sources:

-mixed road data from 2011 / 2017

-rail traffic data from 2011 / 2016

-aviation data from 2011 / 2016

-industry data from 2008 / index numbers

-wind turbines data from 2012 / 2020

Noise traffic - daily mean (road only)^T

Address, PC6, PC4

(25 meter raster)

2000, 2004, 2007, 2008,

2010 and 2011

PBL Netherlands Environmental Assessment Agency

Modeled data with Empara noise tool with 25x25 m resolution on road noise in dB in 2000, 2004, 2005, 2007, 2008, 2010 and 2011. Several factors are accounted including traffic intensity, road types and sound barriers.

Noise traffic - national roads^G  (highways)

Address, PC6

2006, 2011 and 2016

Rijkswaterstaat

Separate data available for day and night (noise in Lden)

Offices, retail, and businesses^T

PC4

1990 till 2014 (yearly)

VROM – DG Ruimte/IBIS (SWING Vastgoedmonitor 2007)
Strabo  –  Bureau  voor  Ruimtelijk  Marktonderzoek  (SWING  Vastgoedmonitor 2007)

Data on offices, retail, and businesses

(e.g.,  number  of  leased  and  owned  properties  and price/m2)

Parking density^G

Neighborhood

2019

Kadaster / RDW

Derived from dataset ‘Parking places’. Statistical summaries have been made for the neighborhood borders of 2016. Variables include: total number of parking places, number of parking places per household, number of parking places per hectare, ratio number of cars / number of parking places.

Parking places^G

Address, PC6

1:2.500 – 1:10.000

2019 (public/paid parking space)
2015 (private built-up parking space)

Kadaster / RDW

This dataset is a combination of data from the BGT, TOP10, BAG and RDW. The BAG data for private built-up parking spaces concerns the year 2015, the other data concerns 2019. Data is produced as polygon data and as derived point data.

Population and Households^T

PC4

1998 till 2014 (yearly)

Statistics Netherlands (CBS)

Data on population and households (e.g., number of men and women stratified for age, number of households stratified for type, and information on immigrants)

Population density^GT

(component walkability index)

Address, PC6,

PC4, 25 m raster and neighborhood

Yearly from 2000 to 2020
(100 m res.)

1995, 1997, 1999 (neighborhood res.)

Statistics Netherlands (CBS)

Population density as Z-scores or as raw values based on CBS vierkantstatistieken (100x100 meter grid) for the years 2000 until 2020 and CBS buurtstatistieken for the years 1995, 1997 and 1999.  This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Post offices^T

PC4

2005

Postkantoren B.V. (SWING Vastgoedmonitor 2007)

Data on post offices (e.g., number of post offices (per 10000 residents))

Poverty^G

PC4 / neighbourhood

2017 (PC4 / neighbourhood level)

2013 (municipality level)

The Netherlands Institute of Social Research (SCP)

Percentage of ‘poor’ people according to SCP definitions per postcode 4 area and neighborhood in 2017

Primary education^T

PC4

2001 till 2015 (yearly)

The Education Executive Agency of the Dutch Ministry of Education, Culture, and Science (Real Estate Monitor 2015)

Data on facilities regarding primary education. (e.g., number of schools and number/percentage of pupils stratified for age and sex)

Public transport stops^G

Address, PC6
(points)

2015 (update 2018)

Geodienst Rijksuniversiteit Groningen /  databank Nationale Data Openbaar Vervoer (NDOV)

This is a point dataset with all public transport stops in the Netherlands (bus, ferry, metro, taxi, tram). Train stations are not part of this dataset. Public transport data is also available via OpenStreetMap, but is less complete.

Public transport stops density^G

(component walkability index)

2018

Geodienst Rijksuniversiteit Groningen /  databank Nationale Data Openbaar Vervoer (NDOV)

The PT stop point density as Z-scores or as raw values is calculated over circular buffers with radii of 150-, 250-, 350-, 500-, 750-, 1000-, 1650, 2000, 3000 and 5000 meters. The density is weighted with the number connecting lines per PT stop. Public transport density can be added as an additional component of the Gecco walkability index.

Railway stations / lines ^G

Address, PC6
(points/ lines)

2019

Prorail Database – made available by Esri Nederland Datasets.

This is a point and line dataset with respectively all railway stations in the Netherlands classified in different station types and the route network of all railways.

Retail and service destinations
density^GT

(component walkability index)

Address, PC6, PC4,

25 m raster and neighborhood

1989, 1993, 1996, 2000, 2003, 2006, 2008, 2010, 2012, 2015, 2017

Statistics Netherlands (CBS)

Retail and service destinations density as Z-scores  or as raw values based on CBS soilstatistics (BBG bodemgebruiksbestanden). This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Retail outlets^T

PC4

2004 till 2019 (yearly)

Locatus

Various exposure measures on retail outlets specific for category of retail (e.g., fastfood outlets, supermarkets, greengrocers)

Road density^G

Neighbourhood

2015

Kadaster

The (car)road density is derived from the dataset TOP10 NL 2015 (line feature layer WEGDEEL_HARTLIJN).

Road speed – national roads^G

Address, PC6
(lines)

2004-2012 (yearly)

2013-current (monthly)

Rijkswaterstaat, Ministry of Infrastructure and Water Management; WEGGEG-bestand RWS

This dataset contains the maximum speed per road section of the national roads

Road speed – all roads NWB^G

Address, PC6
(lines)

2016-current (monthly)

Rijkswaterstaat, Ministry of Infrastructure and Water Management; WKD - WegKenmerken Database

This dataset contains the maximum speed limits per road section of all roads in the NWB (Nationaal WegenBestand)

Secondary education^T

PC4

2005 till 2015 (yearly)

The Education Executive Agency of the Dutch Ministry of Education, Culture, and Science (Real Estate Monitor 2015)

Data on facilities regarding secondary education. (e.g., number of schools,  and number/percentage of students stratified for sex, age, and educational level)

Side walk density^GT

(component walkability index)

Address, PC6, PC4,

25 m raster and neighborhood

1989, 1993, 1996, 2000, 2003, 2008, 2012, 2015, 2019

Kadaster

Side walk density as Z-scores or as raw values based on Basisregistratie Grootschalige Topografie (BGT). This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Socio-economic status^T

PC6

2008

Statistics Netherlands (CBS)

Data on socio-economic status (e.g., high/low incomes, benefits)

Socio-economic status scores^G

PC4, neighborhood

PC4: 1998, 2002, 2006, 2010, 2014, 2016, 2017
Neighborhood: 2016

Note that SCP stopped producing the SES scores and to fill the resulting data gap CBS developed a new score per neighborhood, which will be (probably) called the WOA score which gives more importance to the social component of the score. This new score will be initially produced for the years 2014-2019 and subsequently on a yearly basis.

The Netherlands Institute of Social Research (SCP)

Socio-economic status scores from SCP are produced on a PC4 level and are based on: education, income and position in the labor market. A higher score indicates a higher status. Scores can be compared over time and are calculated over a cumulative dataset. On average the statusscore in the Netherlands is zero.

Special education^T

PC4

2004 till 2015 (yearly)

The Education Executive Agency of the Dutch Ministry of Education, Culture, and Science (Real Estate Monitor 2015)

Data on facilities regarding special education. (e.g., number of schools,  and number/percentage of students stratified for sex

Sport accommodations^G

Address, PC6

2011-2017 (data collection period)

Mulier Instituut

Point data on type and location of sport accommodations

Sport accommodations point and neighbourhood density^GT

Address, PC6

Point density radius

500, 1000, 3000 and 5000 m

2011-2017 (data collection period)

Mulier Instituut

Point density sport accommodations in 500, 1000, 3000 and 5000 meter radius and neighbourhood density 1000 meter radius.  Prior to the calculation of sport accommoda-tion densities, a selection was made of sports involving significant physical activity. This means that sports such as chess playing, bridge, dog sport and car sport, were removed from the database.

Neighborhood density 2016

Sport accommodations^T

Sport associations^T

Sport facilities^T

PC4

2010 till 2015 (yearly)

2006 till 2015 (yearly)

2001 till 2007 (yearly)

Royal Dutch Athletics Federation (Real Estate Monitor 2015) Voetbalgids.com (Real Estate Monitor 2015)
Skibanen in Nederland (Real Estate Monitor 2015)
Royal Dutch skating Association (Real Estate Monitor 2015) ZwembadGids (Real Estate Monitor 2015)
Royal Dutch Baseball and Softball Federation (Real Estate Monitor 2015)
Royal Dutch Korfball Union (Real Estate Monitor 2015)
Royal Dutch Lawn Tennis Federation (Real Estate Monitor 2015)
Dutch Rugby Association (Real Estate Monitor 2015)
Voetbalgids.com (Real Estate Monitor 2015)
BSvL – Nederlandse Sport Almanak (NSA) (SWING Vastgoedmonitor 2007)

Data on sport accommodations, sport associations, and sport facilities (e.g., number of specific sport accommodations, sport associations and sport facilities).

Street connectivity^GT

(component walkability index)

Address, PC6, PC4,

25 m raster and neighborhood

1989, 1993, 2001, 2003, 2012, 2015, 2019

Kadaster / DANS-KNAW

Street connectivity as Z-scores or as raw values based on TOP10 road intersection data. This is a component of the Gecco walkability index. The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).

Temperature (daily average, minimum and maximum)

1x1 km

1961-current (daily update)

KNMI (Koninklijk Nederlands Meteorologisch Instituut) DataCentrum

This dataset involves grid files of interpolated daily average temperature values for the Netherlands, based on 33-35 KNMI automatic observation stations.

Days from 1 October 2017 to 30 June 2018 were processed by Gecco and linked to PC6 centroids.

Temperature (monthly average, minimum and maximum)

1x1 km

2004-2020
(average temperatures)

Older years / minimum / maximum not yet processed by Gecco

KNMI (Koninklijk Nederlands Meteorologisch Instituut) DataCentrum

Gridded (25x25 m) interpolated (by Gecco) monthly average based on 10 KNMI automatic observation stations, corrected with the temperature difference in the RIVM dataset Stedelijk hitte-eiland effect (UHI).

Topography (TOP10 NL -  Basisregistratie Topografie (BRT)^G

Address, PC6

1:10.000

2003, 2005, 2010, 2011, 2012, 2013, 2015, 2019

Kadaster

Vektor data (points, lines, polygons) regarding road and water infrastructure, terrain features, built-up area, etc.

Note that specific data on roads is available in the NWB (Nationaal Wegen Bestand) from 1982 onwards.

Topography – large scale

(Basisregistratie Grootschalige Topografie - BGT)^G

Address, PC6

1:5.000

2017, 2019

Kadaster

Vector dataset with detailed topographic features, such as sidewalks, parking places, tree locations, street furniture, etc.

Traffic incidents^G

Address, PC6

2003 t/m 2017

Bestand geRegistreerde Ongevallen
Nederland (BRON)

Provided via ESRI Nl datasets

Travel time^T

PC4

2011

Object Vision B.V.

Data on travel time between all PC4 areas

Urban heat island effect (UHI) ^G

Address, PC6
(10 m raster)

2017

Institute for Public Health and the Environment (RIVM), Atlas

Leefomgeving (ALO), http://www.atlasleefomgeving.nl

The urban heat island effect (UHI) dataset shows the average yearly temperature difference between areas that are more rural or more urban. The UHI is caused by among others ‘waste heat’ from energy use in densely populated areas and urban elements like roads and buildings that retain the daily heat of especially hot summer days, which prevents cooling off at night. The map shows yearly average temperature differences up to 3 degrees, but in reality differences can be much higher, particular in hot summer periods. The UHI is calculated by the RIVM with the UrbClim model on high resolution (100 – 250 meter) based on a.o. landuse data, vegetation, soil sealing and climate data.

Urbanisation degree ^GT

Address

2020

Urbanisation degree can be calculated on request for other BAG years from 2012 onwards

Kadaster

Urbanization degree as density per km2 of BAG residence addresses (‘omgevings-adressendichtheid - OAD’) in a circular radius of 1000 meter. The same methodology is used as for the urbanisation degree in the CBS key statistical figures, but without summarizing figures to administrative areas.

Urbanisation degree ^G

500x500m rastercells

and PC4

2000 - 2021

CBS Vierkantstatistieken

Urbanization degree per 500 meter grid cell as density per km2 of BAG residence addresses (‘omgevings-adressendichtheid - OAD’) in a circular radius of 1000 meter.

Walkability Index^GT

Address, PC6, PC4,
neighborhood

1989, 1993, 1996, 2000, 2003, 2006, 2008, 2010, 2012, 2015, 2017

 VUmc Amsterdam, GECCO project

Composite score based on seven components: 1) Population density, 2) Density of retail and service destinations (retail environment), 3) Land-use mix, 4) Street connectivity (intersection density), 5) Green space, 6) Side walk surface area, 7) Public transport density.

The components are summed and normalized to a walkability score (0-100) with higher scores indicating higher walkability.

The address level data is produced for 8 different neighborhood radii (150-, 250-, 350-, 500-, 750-, 1000-, 1650 and 2000 meter).