Introduction
Task given: to calculate the proportion of population that has convenient access to green areas, by sex, age and persons with disabilities.
Solution: We analysed access to green areas from most of the mandatory aspects.
We found convenient access to green areas using 4 different data combinations:
1) Convenient access to Estonian Topographic Database (ETD) green areas in localities (national)
2) Convenient access to Estonian Topographic Database (ETD) green areas in urban and high-density (HD) clusters (European dataset)
3) Convenient access to Urban Atlas (UA) green areas in localities (national)
4) Convenient access to Urban Atlas (UA) green areas in urban and high-density (HD) clusters (European dataset)
We analysed both distances to green areas: 200 m and 500 m.
Convenient access to green areas among persons with disabilities was not analysed as we do not have this data for the population of 01.01.2017.
Data status
- For urban green areas, the data of land parcels from Estonian Topographic Database (NMCA 1.01.2017) and Urban Atlas data were used. Also cadastral units spatial data (NMCA) and data about owners of cadastral units from land register were used.
- In Estonia, population statistics are based on the administrative data of the population register. The address data of the register can be geocoded to building level using the Address Data System maintained by Estonian Land Board (NMCA – in Estonia: Maa-amet). Statistics Estonia has a “statistical copy” of the population register, made at the beginning of each year. Since 2016,instead of the place of residence recorded in the census, the place of residence recorded in the population register is used. Population data of 01.01.2017 was used in the current analysis.
- For urban and high-density cluster data, Eurostat clusters were used, which are based on GEOSTAT 2011 grid population. Downloaded from: http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/clusters
- Also, data about national localitites were used.
Processes
a) Geocoding the population data
The population database in Statistics Estonia is based on the data collected into the population register, transferred to Statistics Estonia as at 1 January. The population register was interfaced with Address Data System (ADS, maintained by NMCA) in June 2014, and today about 99% of addresses have been linked with ADS data. Each unit record in the population register includes standardized address data, an address ID and address object ID. Statistics Estonia has the copy of ADS address data, which is updated daily via X-Road (https://e-estonia.com/solutions/interoperability-services/x-road). Therefore, it is easy to geocode the address data from the population register. Each unit record data from the population register are linked with ADS to get the coordinates using the address object identifier as a key. The population database in Statistics Estonia is stored in an Oracle database. The address-points geometries are kept in eGEOStat, which is Statistics Estonia’s geodatabase (Oracle database, ArcGIS Server). The data are linked in the Data Warehouse Department. The data are available to internal users in accordance with the data usage rules.
In the current analysis, 01.01.2017 population data were used. About 96.7% of the population was automatically geocoded to the building level. As it is possible to have an incomplete address in the population register (place of residence data is known only at settlement unit or municipality level), 3% of the population was geocoded only to settlement, city district or municipality level (Table 1). These people are linked to the population weighted centroid of corresponding unit level. About 0.1% of the population was geocoded manually to building level. For these, the direct match was impossible due to outdated address data. In the geodatabase, each record has information about the matching type, describing the geocoding quality – whether the address has been geocoded to building, cadastral unit or municipality level. This enables to extract the data with lower quality depending on requirements of an analysis.
In the analysis all address-points were used.
Table 1: Metadata describing geocoding quality at unit record level, 01.01.2017
| Quality Code | Number of people geocoded | % |
| Direct match to building level | 1,272,733 | 96.7 |
| Direct match to cadastral unit | 559 | 0.0 |
| Direct match to city district unit | 15,659 | 1.2 |
| Direct match to settlement unit | 11,236 | 0.9 |
| Direct match to municipality unit | 14,730 | 1.1 |
| Indirect match | 718 | 0.1 |
| Total population | 1,315,635 | 100 |
b) Delimitation of urban areas
Two different urban areas have been tested:
- Urban cluster – clusters of urban areas in Europe formed on the basis of the population density grid map with Population and Housing Census 2011 data. Data downloaded from: http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/clusters
- Localities – localities have been calculated on the basis of the data of Population and Housing Census 2011 and represent national data.
Localities represent areas where the distance between buildings is less than 200 meters and the number of population in such building groups amounts to more than 200 persons. In Statistics Estonia, the localities were calculated for the first time on the basis of Population and Housing Census 2011 point-based data. The building data were received from Estonian Topographic Database, maintained by NMCA. The results were published in Statistics Estonia’s map application https://estat.stat.ee/StatistikaKaart/VKR#, where the data can be viewed and downloaded. Also metadata description can be found there. After the census, the localities have not been updated.
For current analysis, localities with population over 5,000 and over 50,000 were selected. The reason for this was that the same thresholds are used for European urban clusters and high-density clusters (Figure 1), making the data comparable.
Figure 1. Urban clusters (European dataset) and localities (national)
The above-mentioned national localities and European urban areas do not overlap (Figure 2, Table 2). Even though the total number of population in localities outnumbers the population of European urban clusters by about 53,000 inhabitants, this does not mean that localities everywhere cover the urban clusters. Differences can be found in both directions – localities do not include areas which have been included in urban clusters and vice versa. For instance, Kiviõli locality (population 5,687) has been left out from the European dataset. At the same time, Ihaste locality with a population of 3,481 has been left out from the current analysis, as it has not merged with a neighbouring locality, and the threshold for the analysis has been set to 5,000. In the European dataset, however, Ihaste locality has been combined with the neighbouring areas.
Figure 2. Mismatch of national localities (population ≥ 5,000) and Eurostat’s urban clusters
Table 2. Number of population in national and European urban spatial datasets
| Data source | Number of population in urban areas |
| Eurostat urban clusters | 829,568 |
| National localities (≥5,000 inhabitants) | 882,885 |
c) Selection and preparation of urban green areas
Determining green areas was complicated. Several questions arose about what should and should not be considered a green area.
- Green area land parcels from the Estonian Topographic Database (ETD) of the Land Board1) Green area, wasteland
2) Grassland, open area
3) Forest, shrub
4) Swamp/mire, bog, marshy grassland
5) Cemeteries - Urban atlas green areas1) Forest
2) Herbaceous vegetation association
3) Green urban areas
In the case of both green areas, cadastral units in private ownership were excluded. For identifying the ownership type of cadastral units, the following ownership categories were known:
1) Natural person
2) Legal person
3) Legal person in public law
4) Several owners
5) Owner unknown
Cadastral units that were owned by a legal person in public law or the owner of which was unknown were considered open areas. The cadastral units the owner of which was unknown were studied more closely, and these were mainly swamps/mires, bogs, forests and green areas.
For the cadastral units with a natural person owner, legal person owner or several owners, we checked whether the cadastral units contained building(s). For this, the Estonian Topographic Database layer of buildings was used (2017 status). If a cadastral unit contained a building, it was considered as private land and not included in the analysis of green areas.
Since 1.07.2018, the data of the Estonian Topographic Database (maintained by NMCA – in EE: Maa-amet) are open data that can be downloaded from the Estonian Land Board (NMCA) geoportal: https://geoportaal.maaamet.ee/est/Andmete-tellimine/Avaandmed-p487.html. The data are updated every weekend. Before that the data were available for Statistics Estonia under contract.
Figure 3. Estonian Topographic Database green areas
Figure 4. Urban Atlas green areas
d) Selecting green areas
First, in order to reduce the workload, from the layer of green areas, land parcels were selected which intersected with high-density areas / European clusters. Next, from the layer of cadastral units, land parcels were selected which intersected with the layer of green areas. These cadastral units were linked to type-of-owner data. From this layer, cadastral units were selected for which it was necessary to check whether they contained or did not contain a building, in order to identify whether it was private land or not. Next, a layer was combined (Arcmap – Union) of green areas and cadastral units with buildings, and thereafter, from the combined layer, green areas were selected excluding cadastral units with buildings. Then, bordering green areas were aggregated (Aggregate polygons). A rule was applied that if there were under 2 metres between green areas, they were aggregated. If the distance between green areas was over 2 metres, they remained separate. Thereafter, green area land parcels with area >= 0.5 ha were selected.
ArcMap calculated as green areas also grassy strips on road sides and rows of trees, which people generally do not see as green areas.
Figure 5. Total initial green areas (left) and final open space green areas (right) in Kuressaare based on Estonian Topographic Database
e) Calculation of population within green areas
Next, the centroids of buildings (01.01.2017 population data) within the
1) High-density (HD) clusters
2) Urban clusters
3) Localities
were selected and buffer zones (200m and 500m) were made around the centroids. After that, buildings in the buffer zone were selected by location that intersected the green areas with area >= 0.5 ha.
As we geocode population data every year, we did not have to do any extra work. Sex and age are linked to geocoded population data. Therefore, it was easy to calculate age groups and summarize persons living in high-density clusters, urban clusters and localitites with ArcMap (Spatial Join tool).
Results
Estonian Topographic Database green areas
In urban clusters, 828,295 persons (100%) had convenient access to green areas which were up to 500 m from home and 738,567 persons (89%) had convenient access to green areas which were up to 200 m from home.
In HD clusters, proportions were similar. 497,778 persons (100%) had convenient access to green areas which were up to 500 m from home and 437,830 persons (88%) had convenient access to green areas which were up to 200 m from home.
In national localities, the proportions were similar to European clusters. In localitites (>=5,000), 881,564 (100%) persons had convenient access to green areas which were up to 500 m from home and 790,865 persons (90%) had convenient access to green areas which were up to 200 m from home.
In localitites (>=50,000), 577,784 persons (100%) had convenient access to green areas up to 500 m from home and 513,758 persons (89%) had convenient access to green areas up to 200 m from home.
The proportions among men and women were similar to the total population.
Table 1. Proportion of population having convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 88% | 86% | 86% | 88% | 90% |
| HD clusters 500 m | 100% | 100% | 99% | 100% | 100% |
| Urban clusters 200 m | 89% | 88% | 88% | 89% | 90% |
| Urban clusters 500 m | 100% | 100% | 100% | 100% | 100% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 89% | 88% | 88% | 89% | 90% |
| Localities ( >=50,000 ) 500 m | 100% | 100% | 99% | 100% | 100% |
| Localities ( >=5,000 ) 200 m | 90% | 89% | 89% | 90% | 90% |
| Localities ( >=5,000 ) 500 m | 100% | 100% | 100% | 100% | 100% |
Table 2. Proportion of men having convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 87% | 86% | 87% | 88% | 89% |
| HD clusters 500 m | 100% | 100% | 100% | 100% | 100% |
| Urban clusters 200 m | 89% | 88% | 88% | 89% | 89% |
| Urban clusters 500 m | 100% | 100% | 100% | 100% | 100% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 89% | 88% | 88% | 89% | 90% |
| Localities ( >=50,000 ) 500 m | 100% | 100% | 100% | 100% | 100% |
| Localities ( >=5,000 ) 200 m | 89% | 89% | 89% | 90% | 90% |
| Localities ( >=5,000 ) 500 m | 100% | 100% | 100% | 100% | 100% |
Table 3. Proportion of women having convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 88% | 87% | 86% | 88% | 90% |
| HD clusters 500 m | 100% | 100% | 99% | 100% | 100% |
| Urban clusters 200 m | 89% | 88% | 88% | 89% | 90% |
| Urban clusters 500 m | 100% | 100% | 99% | 100% | 100% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 89% | 88% | 87% | 89% | 91% |
| Localities ( >=50,000 ) 500 m | 100% | 100% | 99% | 100% | 100% |
| Localities ( >=5,000 ) 200 m | 90% | 89% | 88% | 90% | 91% |
| Localities ( >=5,000 ) 500 m | 100% | 100% | 100% | 100% | 100% |
Urban Atlas green areas
In urban clusters, 597,782 persons (95%) had convenient access to green areas which were up to 500 m from home and 369,702 persons (59%) had convenient access to green areas which were up to 200 m from home.
In HD clusters, proportions were again similar. 472,371 persons (95%) had convenient access to green areas which were up to 500 m from home and 282,690 persons (57%) had convenient access to green areas which were up to 200 m from home.
In localities, the proportions were similar to European clusters. In localitites (>=5 000), 611,706 (95%) persons had convenient access to green areas which were up to 500 m from home and 379,410 persons (59%) had convenient access to green areas which were up to 200 m from home.
In localitites (>=50 000), 541,510 persons (95%) had convenient access to green areas up to 500 m from home and 329,219 persons (58%) had convenient access to green areas up to 200 m from home.
The proportions among men and women were similar to the total population.
Table 4. Proportion of population with convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 57% | 56% | 56% | 56% | 59% |
| HD clusters 500 m | 95% | 95% | 94% | 94% | 96% |
| Urban clusters 200 m | 59% | 59% | 59% | 59% | 60% |
| Urban clusters 500 m | 95% | 95% | 95% | 95% | 96% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 58% | 58% | 57% | 57% | 59% |
| Localities ( >=50,000 ) 500 m | 95% | 95% | 94% | 94% | 95% |
| Localities ( >=5,000 ) 200 m | 59% | 59% | 59% | 59% | 60% |
| Localities ( >=5,000 ) 500 m | 95% | 95% | 95% | 95% | 96% |
Table 5. Proportion of men with convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 56% | 56% | 56% | 56% | 58% |
| HD clusters 500 m | 94% | 95% | 94% | 94% | 95% |
| Urban clusters 200 m | 59% | 59% | 59% | 58% | 60% |
| Urban clusters 500 m | 95% | 95% | 95% | 95% | 96% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 57% | 57% | 57% | 57% | 59% |
| Localities ( >=50,000 ) 500 m | 94% | 95% | 94% | 94% | 95% |
| Localities ( >=5,000 ) 200 m | 59% | 59% | 59% | 58% | 60% |
| Localities ( >=5,000 ) 500 m | 95% | 95% | 95% | 95% | 96% |
Table 6. Proportion of women with convenient access to green areas by age
| TOTAL | 0–14 | 15–24 | 25–64 | 65 and over | |
| European classification | |||||
| HD clusters 200 m | 57% | 56% | 56% | 57% | 59% |
| HD clusters 500 m | 95% | 95% | 94% | 95% | 96% |
| Urban clusters 200 m | 59% | 59% | 59% | 59% | 60% |
| Urban clusters 500 m | 95% | 95% | 95% | 95% | 96% |
| National classification | |||||
| Localities ( >=50,000 ) 200 m | 58% | 58% | 57% | 58% | 59% |
| Localities ( >=50,000 ) 500 m | 95% | 95% | 94% | 95% | 95% |
| Localities ( >=5,000 ) 200 m | 59% | 59% | 59% | 59% | 60% |
| Localities ( >=5,000 ) 500 m | 95% | 95% | 95% | 95% | 96% |
Comparison between Estonian Topographic Database green areas and Urban Atlas green areas.
When comparing ETD green areas to UA green areas, there are some differences. When looking at access to ETD green areas which are up to 500 m from home in European clusters and localities, convenient access is 100%, and for those up to 200 m from home, it is 88–90%.
But when looking at UA green areas, convenient access to green areas which are up to 500 m from home, is 95% in European clusters and localitites. Convenient access to green areas which are up to 200 m from home is about 56–59%.
The reason for this contrast is probably the difference between ETD and UA green areas. UA green areas contain only green areas in Harju county, Tartu county and Narva city. ETD green areas cover the whole country. When calculating the access to UA green areas, for calculating accessibility, only Harju county, Tartu county and Narva city Eurostat clusters, localitites and the contained buildings were extracted.
Comparison between different classifications (European and national)
Using different classifications for urban areas, the results were quite similar.
Evaluation
The aim of the analysis was to test the suitability / applicability of ESGF principles for the calculation of geostatistical indicators. The tested common indicator was SDG indicator 11.7.1., which measures the accessibility to green areas in cities.
The analysis confirmed that the use of ESGF principles for the collection and storage of data is reasonable and helps to calculate geostatistical indicators. In particular, the applicability of principles 1, 2, 3 and 4 was assessed. Principle 5 was outside the scope of the grant.
As some ESGF principles have already been applied in Statistics Estonia, the analysis was easy to conduct because certain stages could be skipped.
In conclusion, the main strengths are:
- Using administrative data (population register) for population statistics enables to update the data annually.
- Development of the address standard (managed by the Land Board), development of various services on its basis and interfacing state registers with ADS have improved the quality of address data, due to which geocoding is easy and feasible on a yearly basis.
- Availability of open data of land parcels from a reliable source (Estonian Land Board) https://geoportaal.maaamet.ee/est/Andmete-tellimine/Avaandmed/Eesti-topograafia-andmekogu-ruumiandmed-p607.html
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