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Green bold = +10% increase
Green = 0-10% increase
Red = 0-10% decrease
Red bold = -10% decrease
OpenStreetMap and Wikipedia: A Method for
Identifying Cultural Ecosystem ServicesProgramme: MSc Geographic Information Science
Name of Student: Christopher French
Supervisors: Muki Haklay and Gianfranco Gliozzo
UCL Department of Civil, Environmental
and Geomatic Engineering, Gower St,
London ,WC1E 6BT
Introduction
The development of web 2.0 technologies[1] has led to a valuable source of
geographic content called volunteered geographic information (VGI)[2] making way
for a multitude of open source applications. This project focuses on two of these,
OpenStreetMap (OSM) and Wikipedia, with an aim of using them to identify and
analyse distributions of cultural ecosystem services (CES’s). Ecosystem services
are the benefits that people experience from environments, material and
immaterial[3]. This work targets CESs, these include religious and spiritual
enrichment, recreational or aesthetic experiences, tourism and benefits with
cognitive, educational or scientific value. Research in the field is growing and this is
of great importance because there are strong linkages between CESs and health,
welfare and social relations; however CES supply is declining and demand is rising.
Aims and Objectives
To carry out spatial and temporal analysis similar to methods used in public
participatory ecosystem service research[4] but by implementing VGI data from
OSM and Wikipedia instead in order to build on current knowledge and test the
credibility of these data sources in this research field. A further aim was to improve
upon previous OSM data extraction and processing methods[5] in terms of time and
computational intensiveness, while delivering equally robust and valuable results.
The chosen study area is South Wales, the extent is diverse, encompassing major
coastal cities, The Valleys, the Brecon Beacons National Park and the Wye Valley,
which is an area of outstanding natural beauty (AONB).
References
[1] O’Reilly, T. (2005). What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html?page=1.
[2] Goodchild, M. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal. 69, 211-221.
[3] Millennium Ecosystem Assessment. (2005). Ecosystems and Huma Well-Being. Millennium Ecosystem Assessment. Washington, DC: World Resources Institute.
[4] Brown, G., Fagerholm, N. (2015). Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Ecosystem Services. 13, 119-133.
[5] Mooney, P., Corcoran, P. (2012a). Characteristics of Heavily Edited Objects in OpenStreetMap. Future Internet. 4, 285-305.
[6] Korner, P. (2011). GitHub: Source code for the OpenStreetMap history splitter. https://github.com/MaZderMind/osm-history-splitter.
[7] OSM (2016). Full OSM history dump. http://planet.openstreetmap.org/planet/full-history.
[8] Django, 2016. Documentation: GeoDjango. https://docs.djangoproject.com/en/1.10/ref/contrib/gis.
[9] MEA (Millennium Ecosystem Assessment). (2005). Ecosystem and human well-being: synthesis. Island Press, Washington, D.C., USA.
[10] Alessa, L., Kliskey, A., Brown, G. (2008). Social-ecological hotspots mapping: a spatial approach for identifying coupled social–ecological space. Landsc. Urban Plann. 85, 27-39.
Methodology
The osm-history-splitter extraction
software[6] was complied on the
Elastic Compute Cloud (EC2) from
Amazon Web Services, allowing
faster download and processing of
the file[7]. This was made faster by
using Python code to split the edits
into sub-bounding boxes based on
their associated tag, grouping ones
with the same tag (natural=peak).
This allowed faster importing into
the database instead of loading
them individually as has been done
previously. GeoDjango[8] was also
implemented to create and
populate the databases to avoid
writing lengthy SQL.
Selecting CES categories was done
by reviewing key papers to find the
most commonly used categories.
These were: recreation, aesthetics,
spiritual and religion, education and
cultural heritage. Grouping tags into
categories was challenging as no
classification scheme exists
therefore it had to be carried out
subjectively. This process would
have benefited from a sensitivity
analysis, principle component
analysis or peer review to add
validity to the decision making. Methodological framework of the data preparation and analysis.
Kernel density estimation (KDE) of all cultural ecosystem services
ResultsFigure 2 - Total yearly OSM edits relating to
CES’s, June 2006 - June 2016
Figure 4 - Total OSM edits relating to each of
the CES’s, split into 2 year time periods, June
2006 - June 2016
Figure 6 - Percentage of OSM edits relating to
each CES’s in each category, June 2006 -
June 2016
Figure 3 - Total Wikipedia article revisions
relating to CES’s, June 2006 - June 2016
Figure 5 - Total Wikipedia article revisions
relating to each of the CES’s, split into 2 year
time periods, June 2006 - June 2016
Figure 7 - Percentage of Wikipedia article
revisions relating to each CES’s in each
category, June 2006 - June 2016
Figure 8 - Kernel density estimation raster surface displaying all
OSM edits relating to CES’s, June 2006 - June 2016
Figure 9 - Kernel density estimation raster surface displaying all
Wikipedia article revisions relating to CES’s, June 2006 - June 2016
KDE and hotspots of each cultural ecosystem service category - OSM Recreation Aesthetics Spiritual and Religion
Education Cultural Heritage
KDE and hotspots of each cultural ecosystem service category - Wikipedia Recreation Aesthetics Spiritual and Religion
Education Cultural Heritage
OSM
Area Recreation Aesthetics Spiritual and Religion Education Cultural Heritage
Overall study area (%) 10.0 20.1 18.5 24.2 27.2
Urban and suburban areas (%) 12.7 13.6 41.8 19.5 12.4
Brecon Beacons National Park (%) 8.7 18.6 5.4 20.4 46.9
Wye Valley (AONB) (%) 9.4 18.8 9.9 33.1 28.7
Wikipedia
Area Recreation Aesthetics Spiritual and Religion Education Cultural Heritage
Overall study area (%) 20.4 22.8 12.6 27.7 16.5
Urban and suburban areas (%) 25.8 18.7 20.8 21.2 13.4
Brecon Beacons National Park (%) 25.3 27.4 10.5 23.2 13.7
Wye Valley (AONB) (%) 13.6 37.3 1.7 39.0 8.5
Figure 10 - Kernel
densities and hotspots
of the individual CES
categories for OSM,
June 2006 to June 2016
Figure 12 - The proportion of CES related OSM edits and Wikipedia
revisions in urban areas, national parks and AONB’s compared to
the total study area.
• Population is not the main contributing factor for the number of edits/revisions,
many other complex factors relating to CES values and benefits are involved.
• Correlations exist between the spatial and temporal trends of OSM edits relating
to aesthetic and education (fig 10); and Wikipedia revisions relating to aesthetics
and recreation (fig 11). Comparing results to literature reveals mixed outcomes[9].
• Anomalous cultural heritage OSM edits made between June 2014 and June
2016 by an individual user have skewed results greatly (figures 4 and 6).
• The difference in datasets is partially causing the contrasts in spatial distributions
of CESs. There were 10x the number of OSM points (even after grouping into
sub-bounding boxes) than the number of Wikipedia articles, but the range in
number of article revisions was far higher. This meant the IDW and KDE maps
have very different results depending on the search radius used.
• KDE outperformed IDW*, producing clearer and more natural areas of high
values and is more fitting for analysing social data of this kind as expected[10].
• VGI data has great potential for further ecosystem service research in a
direction, offering easy access to larger datasets than achievable through PPGIS
method, but does lack information on contributor demographics.
Future work
• Focus on analysing trends in the number of contributors instead of edits
themselves, and also implement contributor edit limits or another method for
dealing with vandalism and anomalous edits/revisions.
• Develop Python code further to split data into square bounding boxes by different
horizontal and vertical scale factors, for accurate overlay with external datasets.
• Implement a principle component analysis and/or sensitivity analysis into the
categorising of OSM tags and Wikipedia articles into different CES groups.
• Utilise the Wikipedia article additional metadata provided by the Geonames API
(i.e. rank) to further the analysis. This was not possible due to time constraints.
Key Findings
• OSM edits and Wikipedia
revisions relating to spiritual
and religion are far lower in the
Brecon Beacons and the Wye
Valley (fig 12) most likely due to
their reliance on religious
buildings mostly found in
populated areas.
* The 2 IDW maps that were inferior to figures 8 and 9 were not included in this poster
Figure 11 - Kernel
densities and hotspots
of the individual CES
categories for Wikipedia,
June 2006 to June 2016