ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

QNEAT3 (Qgis NEtwork Analysis Toolbox 3) is a QGIS plugin that complements the processing functions with advanced algorithms for network analysis (origin destination matrices, shortest path algorithm, isochron calculation). The aim of the development was to provide advanced algorithms for solving more complex problems based on the existing possibilities and functionalities of QGIS. The plugin was developed and tested using Open Government Data data sets and can be used worldwide.

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

This data package includes two related data files that can be used as input for habitat network analyses on amphibians using a specific habitat network analysis tool (HNAT; v0.1.2-alpha):

1. AmphibianHabitatNetwork_Parameters.xlsx
2. BiotopeMap_GothenburgRegion_withPondsRoadsAndBuildings.tif

HNAT is a plugin for the open-source Geographic Information System QGIS (https://qgis.org/en/site/). HNAT can be downloaded at https://github.com/SMoG-Chalmers/hnat/releases/tag/v0.1.2-alpha. To run the habitat network analyses based on the input data provided in this package one must install the plugin HNAT into QGIS. This software has been created by Chalmers within a research project financed by the Swedish government research council for sustainable development, Formas (FR -2021/0004), within the framework of the national research program "From research to implementation for a sustainable society 2021". The Excel-file contains the parameters for amphibians and the GeoTiff-file is representing a biotope raster map covering the Gothenburg region in western Sweden. SRID=3006 (Sweref99 TM). Pixel size =10x10 metres. The pixel values of the biotope map correspond to the biotope codes listed in the in the parameter file (see column “BiotopeCode”). For each biotope the parameter file holds biotope specific parameter values for two alternative amphibian models denoted “Amphibians_NMDWater_ponds” and Amphibians_NMDWater_ponds_NoFriction”. The two alternative parameter settings can be used to demonstrate the difference in model prediction with or without the assumption that amphibian movements are affected by barrier effects caused by roads, buildings and certain biotopes biotope types. The “NoFriction” version assumes that amphibian dispersal probability declines exponentially with increasing Euclidian distance whereas the other set assumes dispersal to be affected by barriers. Read the readme file for details on each parameter provided in the parameter file.

The GeoTiff-file is a biotope mape which has been created by combining a couple of publicly available geodata sets. As a base for the biotope map the Swedish land cover map NMD was used (https://geodata.naturvardsverket.se/nedladdning/marktacke/NMD2018/NMD2018_basskikt_ogeneraliserad_Sverige_v1_1.zip). To achieve a greater cartographic representation of small ponds, streams, buildings and transport infrastructure relevant for amphibian dispersal, reproduction and foraging, NMD was complemented by information from a number of vector layers. In total, 20 new biotope classes representing buildings of different height ranging from less than 5 m up to 100 m, were added to the basic land cover map. The heights were obtained by analyzing the LiDAR data provided by Swedish Land Survey (for details see Berghauser Pont et al., 2019). The data was rasterized and added on top of existing pixels representing buildings in the Swedish land cover map. The roads were separated into 101 new biotope classes with different expected number of vehicles per day. Instead of using statistics from the Swedish Transport Administration on observed number of vehicles per day relative traffic volumes were predicted based on angular betweenness centrality values calculated from the road network using PST (Place Syntax Tool, Stavroulaki et al. 2023). PST is an open-source plugin for QGIS (https://www.smog.chalmers.se/pst). Traffic volumes are expected to be correlated to the centrality values (Serra and Hillier, 2019). The vector layer with the centrality values was buffered by 15 m prior to rasterization. After that the new pixel values were added to the basic Land cover raster in sequence following the order of centrality values. Information on small streams with a maximum width of 6 m was added from a vector layer of Swedish streams (https://www.lantmateriet.se/en/geodata/geodata-products/product-list/topography-50-download-vector/). These lines where rasterized and added to the land cover raster by replacing the underlaying pixel values with new class specific pixel values. Small pondlike waterbodies was identified from the NMD data selecting contiguous fragments of the original NMD biotope class 61 with a smaller area than 1 hectare. Pixels representing the smaller water bodies was then changed to 201.

References Berghauser Pont M, Stavroulaki G, Bobkova E, et al. (2019). The spatial distribution and frequency of street, plot and building types across five European cities. Environment and Planning B: Urban analytics and city science 46(7): 1226-1242. Serra M and Hillier B (2019) Angular and Metric Distance in Road Network Analysis: A nationwide correlation study. Computers, Environment and Urban Systems 74: 194-207. Stavroulaki I, Berghauser Pont M, Fitger M, et al. (2023) PST Documentation_v.3.2.5_20231128, DOI:10.13140/RG.2.2.32984.67845.

- The extent of the datasets refers to the Urban morphological zones of Nicosia and a buffer of 20 km around them. We downloaded and cleaned the street network from OSM on 4.12.2024 using Cityseer Python packages (https://github.com/benchmark-urbanism/cityseer-api/ ) release 4.17.0. After downloading the whole cleaned network, we created the motorised and the non-motorised network by extracting road/path classifications from the “highway” attribute.

- For the non-motorised network, we excluded roads with only one classification type, specifically [motorway], from the “highway” attribute generated by the cleaned network. Furthermore, we made sure that the new network is connected in QGIS and there are no network islands after the exclusion.

- For the unlinks, it was created using the Place syntax tool within QGIS. Creating a segment map and choosing the network as coming from the road centreline.

In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

QNEAT3 (Qgis NEtwork Analysis Toolbox 3) ist ein QGIS Plugin, das die Processing Funktionen um fortgeschrittene Algorithmen zur Netzwerkanalyse (Origin-Destination Matrizen, Kürzester Wege Algorithmus, Isochronen Berechnung) ergänzt. Ziel bei der Entwicklung war es, anhand der vorhandenen Möglichkeiten und Funktionalitäten von QGIS, erweiterte Algorithmen zur Lösung komplexerer Fragestellungen bereitzustellen. Das Plugin wurde unter Nutzung von Open Government Data Datensätzen entwickelt und getestet und ist weltweit einsetzbar.

These data are the urban woodland habitat networks of eleven different cities: Nottingham, Plymouth, Stoke-on-Trent, Milton Keynes, Coventry, Wolverhampton, Northampton, Birkenhead, Derby, Luton and Kingston-Upon-Hull.Three types of data are used to create the shape files:The OS MasterMap Topography (EDINA Digimap Ordnance Survey Service, 2024) ‘Natural Environment’ layer.This was overlain upon the latest version of the LandCover Map (EDINA Environment Digimap Service, 2022) for each urban area using QGIS (https://qgis.org/). Urban area boundaries were determined and clipped using the experimental urban extent polygons for the UK (ONS, 2019).ReferencesEDINA Digimap Ordnance Survey Service (2024) OS MasterMap® Topography Layer [GeoPackage geospatial data], Scale 1:1250, Tiles: GB, Updated: 1 February 2024, Ordnance Survey (GB). Available at: https://digimap.edina.ac.uk (Accessed: 10 July 2024).EDINA Environment Digimap Service (2022) Land Cover Map 2021 [FileGeoDatabase geospatial data], Scale 1:250000, Tiles: GB, Updated: 10 August 2022, CEH. Available at: https://digimap.edina.ac.uk (Accessed: 10 July 2024).ONS (2019) Experimental urban extent for UK - Office for National Statistics. Available at: https://www.ons.gov.uk/aboutus/transparencyandgovernance/experimentalurbanextentforuk (Accessed: 26 August 2024).

QNEAT3 (Qgis NEtwork Analysis Toolbox 3) ir QGIS spraudnis, kas papildina apstrādes funkcijas ar uzlabotiem algoritmiem tīkla analīzei (izcelsmes mērķa matricas, īsākā ceļa algoritms, izohronais aprēķins). Izstrādes mērķis bija nodrošināt progresīvus algoritmus sarežģītāku problēmu risināšanai, pamatojoties uz esošajām QGIS iespējām un funkcijām. Spraudnis tika izstrādāts un testēts, izmantojot valdības atvērto datu kopas, un to var izmantot visā pasaulē.

This layer shows urban bike corridors for York County, Pennsylvania. They are dedicated or prioritized pathways designed to enhance cyclist safety, accessibility, and connectivity within city transportation networks. Bike corridors may include protected bike lanes, shared roadways, multi-use trails, and greenways, often integrated with public transit and urban land uses. They are typically designed with features such as traffic calming, signage, pavement markings, and physical barriers to promote safe, continuous, and comfortable cycling for all age and ability levels. The analysis incorporates spatial data layers such as road networks, digital elevation models (DEM), land use/land cover (LULC), vehicular traffic volumes, cycling crash incidents, and demographic data within GIS platforms like ArcGIS Pro and QGIS. Advanced network analysis, including least-cost path modeling and service area analysis, identifies optimal routing options based on factors such as slope, traffic density, and road hierarchy. Kernel Density Estimation and hotspot analysis help locate high-risk zones for cyclists, informing the placement and design of safer corridors. Suitability analysis is conducted through a weighted overlay of criteria—proximity to key destinations (schools, parks, employment centers), existing cycling infrastructure, equity indicators, and environmental considerations. GIS tools such as buffering, intersecting, and spatial joins are used to model catchment areas and potential demand. A Multi-Criteria Decision Analysis (MCDA) framework within GIS enables the prioritization of corridor segments based on user-defined planning goals. The output includes thematic maps, spatial prioritization models, and a comprehensive geodatabase of recommended bike corridor alignments. This GIS-based approach offers city planners and policymakers a robust, data-driven toolset for designing bike-friendly cities that promote active transportation, reduce carbon emissions, and support healthier, more equitable communities.

Supplementary S1: Raw social-ecological data and 4th tier SES analysis. Contains one zip file: Supplementary.S1.SESF.Analysis.zip Supplementary S2: details of data, GIS analysis and survey documents used for constructing the modeling the structure and dynamics of the hedgerow network in the study site. Supplementary.S2.Model.Application.Study.Site.pdf Supplementary S3: Raw physical and ecological data, GIS analysis and survey used for modeling the structure and dynamics of the hedgerow network in the two sites. Contains three types of files: Spreadsheet presenting the physical, GIS and ecological survey of the hedgerows present in the study sites during summer 2021: Hedgerows.Geo.Phys.Ecol.data.Peri.Urban.and.Rural.1989.2000.2019.xlsx Files summarizing information used for the GIS analysis (with QGIS) of all the hedgerows present in the study, and the spreadsheets allowing the estimation of the transition rates between hedgerow types: QGIS.data.full.zip pdf file showing a QGIS picture output of the evolution of the hedgerow network for rural and urban sites in between the four years (1946, 1989, 2000, 2019): Supplementary.S2.Pictures.GIS.Land.Use.Hedgerows.Rural.and.PeriUrban.1946.1989.2000.2019.pdf Supplementary S4: Octave or Matlab code used to reproduce the results Supplementary.S4.Octave-Matlab.Code.zip Supplementary S5: supplementary results Supplementary.S5.Results.pdf

The Science Case in the Caribbean region presents records on landslides, precipitation, maps used as inputs of hazard models and drone imagery over the region of interest.
For the Carribean study-case, an analysis of open and proprietary satellite based dataset was used to facilitate the setup and evaluation of physically-based multi-hazard models. These allow for qualification and quantification of spatio-temporal multi-hazard patterns. These form a crucial input into the general hazard and risk assessment workflow.

Presented here are the datasets employed for Case Study 4 in Deliverable D3.1 with a short description, produced and saved within the following folders:

Dominica_landslide: the landslides datasets mapped by ITC using high-resolution satellite imagery. It is intended to calibrate and validate the flood and landslide modelling. The folder contains four shapefiles:

· Landslide_Part.shp - Shapefile containing landslide extent, flash flood extents, and their attributes.

· Cloud.shp – Shapefile represents the cloud-filled areas in the satellite imagery where no mapping was possible.

· The other two shapefiles are self-explanatory.

GPM_Maria: NASA Global Precipitation Mission (GPM) precipitation maps processed for model input in LISEM. GPM is a hybrid fusion with satellite datasets for precipitation estimates. Mean as input data to represent precipitation in the landslide and flood modelling.

Maps_Models_Input : Soil and land use and channels, lots of custom work, SOILGRIDS, and SPOT image classification; all the datasets are ready for model input for OpenLISEM and LISEM Hazard or FastFlood. The dataset is meant to calibrate and validate the flood and landslide modelling.

The raster files are either in Geotiff format or PCraster map format. Both can be opened by GIS systems such as GDAL or QGIS. The projection of each file is in UTM20N.

Some key files are:

• dem.map -elevation model, the height of the landscape in meters above sea level.
• lai.map - leaf area index, estimated using empirical relationships based on NDVI (Normalized Difference Vegetation Index)). The source data to calculate NDVI is Sentinel-2.
• KSat.map - Saturated hydraulic conductivity of the soil, estimated based on a combination of SOILGRIDS soil texture, Saxton et al. (2006) Pedotransfer functions, and a national soil map for Dominica.
• clay.map - Clay texture fraction, SoilGrids resampling
• silt.map - Silt texture fraction, SoilGrids resampling
• sand.map - Sand texture fraction, SoilGrids resampling
• cover.map - Vegetation cover as a fraction, estimated using linear correlation with NDVI.
• lu_new.map - Spot satellite image classification at 10 meters resolution for predominant land use types.
• n.map - Mannings surface roughness coefficient, specific value based on the land use type.
• ndvi.map - Normalized Differential Vegetation Index, based on Sentinel-2 images in summer.
• ldd.map - Drainage network map for the island, which can be used for flow accumulation and streamflow detection
• catchments.map - Catchment ID's based on the ldd.map drainage network.
• Channelldd.map - Channel-only drainage network map, calibrated manually to have all channels on the island represented correctly.
• Soildepth - Soil depth in meters, based on a physically-based soil depth model in meters and observational data obtained from landslide-sites during fieldwork in 2018.
• Slope.map - Slope map in gradient of the elevation model (m/m) in the steepest direction

StakeholderQuestionnaire_Survey_ITC: The stakeholder questionnaires particularly relating to the tools developed partly by this project on rapid hazard modelling. Stakeholder Engagement survey and Stakeholder Survey Results prepared and implemented by Sruthie Rajendran as part of her MSc Thesis Twin Framework For Decision Support In Flood Risk Management supervised by Dr. M.N. Koeva (Mila) and Dr. B. van den Bout (Bastian) submitted in July 2024.

·Drone_Images_ 2024: Images captured using a DJI drone of part of the Study area in February 2024. The file comprises three different regions: Coulibistrie, Pichelin and Point Michel. The 3D models for Coulibistrie were generated from the nadir drone images using photogrammetric techniques employed by the software Pix4D. The image Coordinate System is WGS 84 (EGM 96 Geoid0), but the Output Coordinate System of the 3D model is WGS 84 / UTM zone 20N (EGM 96 Geoid). The other two folders contain only the drone images captured for that particular region's Pichelin and Point Michel. The dataset is used with other datasets to prepare and create the digital twin framework tailored for flood risk management in the study area.

This dataset comprises simplified URN data in spatial representation from 35 selected cities. The data record encompasses the road network and the geometric details of crossings, stored in the “Spatial Geometric Dataset” in GeoJSON format. These datasets are readily applicable for spatial analysis in GIS software such as QGIS or ArcGIS.Spatial Geometric Dataset. Include link and node geometry data of 35 cities.1. RoadSegments (GeoJSON format):Link_id: A self-generated unique identifier for each road segment, utilized in the construction of customized graphs.Length: The length of the road segments.Intersected_node_set: Represents the node_id of the crossings intersected by this road segment, used in the construction of a bipartite graph.Interested_link_set: Represents the node_id of the crossings intersected by this road segment, used in the construction of a dual graph.2. Junctions (GeoJSON format):Node_id: A self-generated unique identifier for each intersection.Interested_link_set: Represents the link_id of road segments intersecting at this crossing.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

In a geographic information system (GIS) study, a spatial join was conducted using QGIS to integrate three datasets: Afstand tot Koelte (ATK), CBS Buurten 2021, and BAG Verblijsobjecten (VO) across three provinces. The resulting output comprised a point dataset which included the ATK and buurten for each VO.It was observed that approximately 5% of the VOs lacked ATK data. Consequently, a field calculation was initiated to ascertain the direct distance, as the crow flies, to the nearest cool place ("koele plek"). A key distinction is that ATK data is based on walking distances along road networks, whereas direct distance measurements do not incorporate such networks. Comparative analysis revealed marginal differences between the two methods, with ATK data generally showing distances 50-100 meters greater.Subsequently, a selection process isolated the VOs categorized as residential ("woning,") which were then exported to an Excel format for further analysis. This was followed by the creation of a distinct list of neighborhoods ("buurten.") Utilizing the 'COUNTIFS' formula, a summation of distances per neighborhood was calculated, leading to the computation of their respective percentages. This methodology integrates spatial data analysis and quantitative techniques to understand geographic proximity and distribution.