Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities
2. Material and Methods
2.1. Review of Current Research and Developments on Assessing Urban Green Spaces
Main Findings on State-of-the-Art Research and Development on Assessing Urban Green Space
- Complexity and difficulty in calculating this indicator.
- Definition and availability of data sources ensuring international data comparability.
- Availability of data sources with ownership information (public vs. private).
- Integration of the above geospatial data sources with typically very different properties, quality and uses.
- Difficulty in delineating built-up areas and open space for public use.
- Information on public vs. private areas is a big challenge. Contributing countries have no figures for this indicator. Some countries may rely on a rich set of cadastral information and tax data, the complexity of urban land use prevents accurate classification of some areas in binary categories of public vs. non-public space.
- Identification of urban footprint.
- Integration of satellite EO data with statistical information in production workflows is challenging.
- Use of EO data for statistical production is evolving, but applications are mainly case-based and yet somewhat experimental.
- Spatially disaggregated data are not widely adopted because the associated cost for generating data using traditional methods remains significant, technical capacity and skills on geospatial analysis is still lacking in many countries and due to difficulties to adopt new practices.
2.2. Proposal for and Implementation of a New Method
- Generate a maximum NDVI dataset (April–August) over the urban areas.
- Delimit built-up area of the city.
- Create a mask of public vs. private areas using a mix of solutions (e.g., cadaster, Volunteered geographic information (VGI)).
- Overlay the mask with the NDVI dataset to identify only vegetated and public areas. This gives an estimation of the total public green space.
- Model the physical accessibility of the population to the nearest public green space, and compute accessibility coverage.
2.2.1. Compute Maximum NDVI Time-Series Using Sentinel-2 Data to Get Vegetated Areas
2.2.2. Delimitation of the Built-Up Area/City
2.2.3. Creation of a Public/Private Mask
- Definitely open to the public although a fee may be required (Light green).
- Probably open to the public (Dark green).
- No accessibility information given or implied (Black).
- Probably not open to the public (Dark blue).
- Definitely not open to the public (Light blue).
- The tag combination is illegal or discouraged (land use = wood or a village green area that is tagged as being private, for example) (Red).
2.2.4. Overlay and Clip Mask with NDVI
2.2.5. Model Physical Accessibility and Coverage of Urban Green Areas
- Accessibility analysis: Compute the traveling time surface, informing the time needed to reach the nearest urban green space.
- Zonal Statistics: Obtain the accessibility coverage for each geographical division.
2.3. A Short Comparison of OSM and Cadaster Data
- Scenario “Fast”:
- 5 km/h walking in town and on roads/footpaths.
- 2 km/h walking in rural areas off-road.
- Scenario “slow”:
- 3 km/h walking in town and on roads/footpaths.
- 2 km/h walking in rural areas off-road.
3.1. Accessibility Maps
3.2. Accessibility Statistics
3.3. Comparing the Centroid-Based Approach with Large Park Entrance
3.4. Testing the New Worldpop “Constrained” Data
- For a potential scaling-up of the methodology at the global level, and specifically for the identification of built-up areas, the GHSL dataset should be considered instead of Urban Atlas, which covers only Europe. This is important for having a consistent approach to extract built-up areas and to allow comparison between countries/regions.
- A key element for measuring this indicator is the identification of private/public spaces. Alternative data sources such as OSM have proved to be a valuable resource in cases where an official land cadaster is not freely available (or at least at an acceptable cost). Purchasing cadaster data from the cities whose green space accessibility needs to be modelled can be expensive and become a major barrier to an effective monitoring of this indicator. Yet, cadaster data would still be required in cases where information of the private/public nature of the parks in OSM is very incomplete.
- Working with the centroids of green spaces can be a valid approach for a first approximation, but it can have an impact on the estimation of accessibility, especially if we consider short traveling time (e.g., 5 min) towards large urban green areas. In that case, it would be interesting to have information on the location(s) of parks’ entrances to provide an enhanced view on accessibility but noting that dedicated entrances may imply fences around the park that should also be considered. For the large parks that can be entered from everywhere, with no restrictive entry point, a least-cost path algorithm considering polygons instead of points for the target areas may be preferred. More work is needed to better understand the impacts of alternative accessibility algorithms given various park configurations.
- Our accessibility modelling requires various input raster datasets. We used mostly global datasets whose resolution is typically 100 m or more, which could be considered too coarse to adequately model barriers to movement in some cities. To get more accurate physical accessibility estimates at higher spatial resolution, national datasets should be considered.
- The travel scenario (modes and speeds on various roads and off-road), as well as barriers to movements, can have specificities in the different cities considered, depending on country-specific and city-specific policies (e.g., soft mobility). More work is needed to understand to which extent a single baseline travel model could be used for sets of cities (e.g., European cities), or if city-specific constraints should always be considered.
- An assumption of the least-cost path approach is that the population always reaches the closest area in terms of travel time. But, by-passing the nearest area to reach one further away could be a reality for a subset of the population. For example, where kid-friendly and playground infrastructures are needed, this target population may have a limited choice of green areas to access. Stratifying the analyses by considering alternative target populations could be possible if sufficient information on the park infrastructures is available.
- Our presented methodology that aims to be in line with the SDG indicators framework only considered cities. A valuable addition in the future would be to also consider rural areas and take into consideration objects such as forests, vineyards, riverbanks, etc., that are also vegetated areas in or around which people like to walk. For these particular areas, the population is generally willing to travel for a longer time and distance (by car, bike or public transport), and then walk. Such behaviours/cases can also be adequately modelled using AccessMod, that can combine several modes of transport in a single analysis.
- The methodology could be tested on other cities/countries in different socio-economic-environmental contexts (e.g., megacities, developing countries) and in cities that have existing data we can validate results against.
- We only considered walking in the travel scenarios. Other types of scenarios of movement, such as bike, public transport or car, could be considered to inform about additional accessibility patterns.
- Comparisons could be done with alternative scenarios such as the one provided by UN-Habitat that proposes to use 400 m walking distance along the road network as delineation of the catchment area. In that specific case, 400 m at a walking speed of 5 km/h corresponds to a travel time of 4.8 min, which more or less corresponds to the computed 5 min distance we used. However, results could be different depending on how one treats off-road movements, and the configuration of barriers to movements in the cities considered.
- Explore the use of freely and openly available cadaster to identify public/private space.
- With the current implementation, green areas smaller than 10 × 10 m could not be identified. A possible solution is to use satellites that can provide higher spatial resolution images such as WorldView or Pleiades sensors.
- The current methodology used the Urban Core layer (from the Urban Atlas 2018) to delimit cities. This was selected for replicability concerns, ensuring that the methodology can be replicated in any European city. However, local data can be even more precise and/or have a different city delimitation. Such data could be easily used to re-compute the zonal statistics on the share of the population that has access to urban green areas.
- For the potential scaling-up of the methodology at a global level, the GHSL dataset should be considered instead of Urban Atlas (that only covers Europe) for the identification of built-up areas. This is important in order to have a consistent approach to extract built-up area and to allow comparison between countries/regions.
- We used the GEE platform to process Sentinel-2 data. However, in the EU context, it would have been preferable to use EU resources such as the Copernicus DIAS platforms. Nevertheless, there is an associated cost for using DIAS and currently, the business model is not yet entirely clear and processing costs should be carefully assessed. To ensure reliable estimations, the use of Sentinel-2 is recommended to reduce biases and uncertainties.
- The validation process is an important step that should be properly handled. Citizen science approaches complemented with the use of aerial and/or on-the-ground imagery can be used to validate the identification of green areas as well as identifying private and public space from LUCAS database pictures [77,78]. It could help to build a tool able to efficiently and accurately classify and differentiate private green space from public green space. This can be a valuable addition, when land parcel data is not available (thus, without ownership information). Validation should also ideally be performed on travel behaviour and travel speeds through population surveys and citizen science approaches, which are yet to be developed and made available at the appropriate scale.
- To complement the private/public mask and further identify public green areas, valuable alternatives exist such as using patterns of mobile phone data (e.g., number and pattern over time of mobile phone users in green spaces) to detect if space is accessible to the public or not. Alternatively, pictures from public databases with geo coordinates (Flickr, Mapillary)  or public information on green spaces (e.g., in Hamburg, Germany)  are also additional useful sources of information when available. This can help to estimate visitation rates and can potentially help to understand which elements of urban green areas attract people.
- Artificial intelligence, and particularly Machine Learning (ML) techniques, can also be a valuable option to complement the differentiation of private green space from public green space [81,82], if land parcel data (without ownership information) is not available. Such an approach has been tested in the UK to map urban green spaces (such as residential gardens, steps, patios and paths) . In this regard, it would be interesting to test this against the LUCAS database to understand if it can be a reliable source of information for classifying/identifying private green areas.
- Automatising our full workflow could be possible, especially if OSM data are used. Workflow using R could automatically download the appropriate dataset, prepare the data for ingestion in AccessMod (or other libraries), run it and output the appropriate maps and statistics.
- Considering the use of the WorldPop “constrained” dataset may improve the realism of the spatial distribution of the population.
- An issue that warrants further consideration is how to deal with forest areas/cropland/grassland in the periphery of urban areas. Indeed, rural parts might have large, vegetated areas that can be publicly accessible and therefore represent important areas for leisure.
Data Availability Statement
Conflicts of Interest
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|Non-public areas||Access = no/private/official/military/agricultural/forestry/destination/restricted/delivery|
|Public areas||Access = yes/permissive/public|
|Unknown access status||Access = unknown|
|Free access||Fee = no|
|Access available on payment of a fee||Fee = yes|
|Access is sometime free and sometimes for a fee||Fee = interval, unknown|
|Tourism||‘Tourism’ = camp_site|
|Amenity||‘Amenity’ = grave_yard|
|Leisure||‘Leisure’ = garden, nature_reserve, park, pitch, golf_course, common, dog_park|
|Land use||‘Landuse’ = forest, grass, meadow, orchard, greenfield, vineyard, recreation_ground, village_green, cemetery|
|Natural||‘Natural’ = scrub, fell, grassland, heath, wood, moor|
|Digital Elevation Model (Shuttle Radar Topography Mission (SRTM))||https://srtm.csi.cgiar.org/srtmdata/|
|Population grid (WorldPop or Center for International Earth Science Information Network (CIESIN)/Facebook)||https://data.humdata.org/organization/facebook?groups=che&q=&ext_page_size=25|
|Land cover (CORINE)||https://land.copernicus.eu|
|Vector Data||URL or OSM tag(s)|
|Roads (OpenStreetMap)||Highway = motorway, trunk, primary, secondary, tertiary, residential, unclassified|
|Rivers (OpenStreetMap)||Waterway = river|
|Lakes (OpenStreetMap)||Natural = water|
|Other barriers (airport, railways) (OpenStreetMap)||Railway = rail; aeroway = aerodrome|
|Centroids (urban green areas)||Provided by Section 2.2.4|
|Administrative boundaries (Urban Atlas)||https://land.copernicus.eu/local/urban-atlas|
|Travel scenario file||Used to inform the model on the modes and speeds of travel for the population willing to reach the nearest green public space. In this study, we only consider walking as the means of transport, and therefore do not consider other means such as car, bike, or public transportation.|
|Geneva Original||Geneva Shifted|
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Giuliani, G.; Petri, E.; Interwies, E.; Vysna, V.; Guigoz, Y.; Ray, N.; Dickie, I. Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities. Remote Sens. 2021, 13, 422. https://doi.org/10.3390/rs13030422
Giuliani G, Petri E, Interwies E, Vysna V, Guigoz Y, Ray N, Dickie I. Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities. Remote Sensing. 2021; 13(3):422. https://doi.org/10.3390/rs13030422Chicago/Turabian Style
Giuliani, Gregory, Ekkehard Petri, Eduard Interwies, Veronika Vysna, Yaniss Guigoz, Nicolas Ray, and Ian Dickie. 2021. "Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities" Remote Sensing 13, no. 3: 422. https://doi.org/10.3390/rs13030422