Neighborhood features in geospatial machine learning: the case of population disaggregation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Neighborhood features in geospatial machine learning: the case of population disaggregation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neighborhood features in geospatial machine learning: the case of population disaggregation

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cartography and Geographic Information Science

ISSN

1523-0406

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

79-94

Kód UT WoS článku

000474044200001

EID výsledku v databázi Scopus

2-s2.0-85068501817