Neighborhood features in geospatial machine learning: the case of population disaggregation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F20%3A10397272" target="_blank" >RIV/00216208:11310/20:10397272 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=83qHDVcvdZ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=83qHDVcvdZ</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/15230406.2019.1618201" target="_blank" >10.1080/15230406.2019.1618201</a>
Alternative languages
Result language
angličtina
Original language name
Neighborhood features in geospatial machine learning: the case of population disaggregation
Original language description
High-resolution population density data are crucial for advanced geographical analysis but are difficult to obtain owing to personal data protection. This paper presents a method to obtain these data through spatial disaggregation of aggregate data using random forests. Ancillary topographic data are used from open data sources, namely OpenStreetMap, Urban Atlas, and the NASA Shuttle Radar Topography Mission (SRTM). An attempt to increase disaggregation accuracy is made through a systematic conceptualization of proximity, neighborhood features. The method is implemented as a toolbox for Python and PostGIS and is tested on three cities in Central and Eastern Europe: Prague, Maribor, and Tallinn. It is shown that this approach produces more accurate predictions than other comparable approaches.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10508 - Physical geography
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Cartography and Geographic Information Science
ISSN
1523-0406
e-ISSN
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Volume of the periodical
47
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
79-94
UT code for WoS article
000474044200001
EID of the result in the Scopus database
2-s2.0-85068501817