1. 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
18 May 2019

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. Figure 4. Vienna’s MOLAND 1971 data.
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9. Figure 5. Vienna’s MOLAND 1986 data.
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10. Figure 6. Vienna’s MOLAND 1997 data.
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11. Figure 7. Vienna’s Urban Atlas 2006 data.
18 May 2019

12. 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

13. 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
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14. Identification and quantification
Figure 8. Vienna 1971 – 1958 changes.
Figure 9. Vienna 1986 – 1971 changes.
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15. Identification and quantification
Figure 10. Vienna 1997 – 1986 changes.
Figure 11. Vienna 1997 – 2006 changes.
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16. 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

17. Validation 18 May 2019 Figure 11. Vienna (Google Maps)
Urban Atlas 2006
MOLAND 1997
Differences
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

18. 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

19. 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

20. 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

21. “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

22. “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

23. 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

24. 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