Land Use Modelling Group, Sustainability Assessment Unit (DG JRC) Development of historical time series based on MURBANDY/MOLAND and European Urban Atlas datasets Pass Present Future Land Use Modelling Group, Sustainability Assessment Unit (DG JRC) Ricardo Barranco ricardobarranco@jrc.ec.europa.eu 18 May 2019

Introduction Urban areas accommodate more than three-quarters of the European population. These areas have grown rapidly; Many of the indicators are focused on the thematic and temporal dimension, and do not include the spatial distribution; Lack of comparable datasets on urban areas in Europe. Datasets used: MOLAND (50’s to late 90’s) and Urban Atlas (2006) Different resolutions; Vectorial detail; Spatial coverage; Land use classified using diverse criteria. 18 May 2019

Table 1. List of historical timeseries cities. Harmonizing Selected cities Starts by selecting which cities have the required data available and which years are represented by it. Table 1. List of historical timeseries cities. Cities Dates   MURBANDY/ MOLAND Urban Atlas Vienna 1958 1971 1986 1997 2006 Bratislava 1949 1969 1985 Brussels 1955 1970 Dresden 1953 1962 1998 Helsinki 1950 1966 1984 Kopenhagen 1954 Milan 1963 1980 Munich 1990 Oporto 1968 1983 Palermo 1989 Prague 18 May 2019

Projection of MURBANDY/MOLAND and Urban Atlas: - Lambert Azimuthal Equal Area (ETRS 1989 LAEA). Figure 1. Vienna’s MURBANDY/MOLAND 1997 data (blue) and Urban Atlas 2006 (red) 18 May 2019

Study area: Main High Density Cluster (according to Cities in Europe: The New OECD-EC Definition (Dijkstra and Poelman, 2011) Figure 2. Vienna’s study area (black), MURBANDY/MOLAND 1997 (blue), Urban Atlas 2006 (red) 18 May 2019

Land use classification and corresponding legend MOLAND: 101 land-use codes AGG: 8 common land-use codes Urban Atlas: 22 land-use codes CLC Code CLC Description AGG Code AGG Description 111 Continuous urban fabric 1 Artificial 112 Discontinuous urban fabric 121 Road and rail networks and associated land 122 Sport and leisure facilities 123 Mine, dump and construction sites 124 Port areas 2 Ports 141 Airports 3 142 Green urban areas 4 Green Urban 13 Water bodies 5 Water Bodies 31 Forests Agricultural, scrubs, herbaceous areas, open spaces and wetlands 99 Industrial or commercial units 18 May 2019

Figure 3. Vienna’s MOLAND 1958 data. Historical time series The common classification and corresponding legend allows to replicate temporal changes. Figure 3. Vienna’s MOLAND 1958 data. 18 May 2019

Figure 4. Vienna’s MOLAND 1971 data. 18 May 2019

Figure 5. Vienna’s MOLAND 1986 data. 18 May 2019

Figure 6. Vienna’s MOLAND 1997 data. 18 May 2019

Figure 7. Vienna’s Urban Atlas 2006 data. 18 May 2019

Land-use quantification Table 3. Urban areas calculated with previous land-use classification. Calculate land use areas; Historical trends. Table 4. Graphic representing Vienna time series calculated areas. 18 May 2019

Identification and quantification Each vectorial dataset was converted to a 100x100m raster grid; Then were detected the cells from 2 rasters that were logically different on a cell-by-cell basis. If the values on the two inputs were different, the value on the most recent raster was outputted. If the values on the two inputs were the same, the output was 0. All detected differences between MOLAND rasters (1958, 1971, 1986, 1997) were kept due to common resolution and methodology; The differences between Urban Atlas (2006) and the latest MOLAND (1997) where filtered to exclude region groups with less than 15 cells size. Urban Atlas 2006 MOLAND 1997 Mean Polygon Area (hectares) 3.33 14.80 18 May 2019

Identification and quantification Figure 8. Vienna 1971 – 1958 changes. Figure 9. Vienna 1986 – 1971 changes. 18 May 2019

Identification and quantification Figure 10. Vienna 1997 – 1986 changes. Figure 11. Vienna 1997 – 2006 changes. 18 May 2019

Identification and quantification Calculate amount of changes by class; Locate areas more dynamic and with higher changes; Quantify the overall mean of change. Table 5. Graphic representing Vienna’s class changes (hectares). 18 May 2019

Validation 18 May 2019 Figure 11. Vienna (Google Maps) Urban Atlas 2006 MOLAND 1997 Differences 2006 - 1997 Mean Green Urban Area (hectares) Green Urban (River) / Total (hectares) 3.66 9.51 587 / 1408 Total Green Urban (hectares) Port (Refinery) / Total (hectares) 3203 1626 114 / 194 Figure 11. Vienna (Google Maps) Figure 12. Vienna River and Port (1997) Figure 13. Vienna River and Port (2006) 18 May 2019

Figure 14. Quantification of class changes (2006 – 1958). Results analysis Convertion of each of the 4 difference rasters to a binary classification (1: change; 0: no change); Adding all 4 binary rasters quantifies the number of changes each cell was converted during the period 2006 – 1958. Figure 14. Quantification of class changes (2006 – 1958). 18 May 2019

Results analysis Convertion of each of the 4 rasters to a binary classification (1: change; 0: no change); Adding all 4 binary rasters quantifies the number of changes each cell was converted during the period 2006 – 1958. Applying a 300m x 300m aggregation and calculating the mean of its composing cells. Mean 0.38 Min Max 2.78 Std Dev 0.45 Figure 15. Aggregated quantification of class changes (300m x 300m; 2006 – 1958). 18 May 2019

Figure 16. MSPA of the quantification of class changes (2006 – 1958). Results analysis GUIDOS: Morphological Spatial Pattern Analysis (MSPA); Automated description of pattern and connectivity; Defines change cores and possible future development areas; Islets were filtered from the analysis. CORE: interior area LOOP: connects same core BRIDGE: connects different cores PERFORATION: interior perimeter EDGE: external perimeter BRANCH: perimeter extension Figure 16. MSPA of the quantification of class changes (2006 – 1958). 18 May 2019

“Indicators for mapping ecosystem services” project Possible applications “Indicators for mapping ecosystem services” project By using the common land use classification and aggregating population data is possible to calculate new indicators. The possibility of a historical time of the “Recreational Potential Indicator” is being analysed. In figure 13 Map A shows the overall recreational service pattern, while Map B shows how the potential impact of recreational opportunities on the population. Especially small urban parks and play gardens face the problem of overcrowding, this kind of approach is useful to see if the population is equally served by this ecosystem services. Each block receives a quota of service proportional to a function of the distance. Munich (2006) Recreational service pattern Munich (2006) Potential capacity of urban greening to serve urban population Potential service pattern

“Green Urban Infrastructures” project Possible applications “Green Urban Infrastructures” project Studies the changes of green spaces in cities; It’s very sensitive to spatial detail and has been only applied to MOLAND data; Further spatial adjustments could allow the integration of Urban Atlas data. Vienna

Processing diagram Changes Identification and Quantification Pre-processing Harmonization (Geo-thematic) Changes Identification and Quantification Validation (manual) Final historical timeseries Post-processing Results Analysis 18 May 2019

Conclusions It’s possible to harmonize these spatial datasets together with their thematic and temporal dimensions; Identify/quantify changes and problematic areas which may require further analysis (e.g. Validation/Remote Sensing); Creation of indicators characterizing changes and which allow comparison with other cities; Following steps: Aggregation/analysis of social/economic indicators (e.g. historical population records); Continuation with Urban Atlas 2012. 18 May 2019