Spatial modelling of population concentration using geographically weighted regression method
Апстракт
This paper presents possibilities of applying the geographically weighted regression method in mapping population change index. During the last decade, this contemporary spatial modeling method has been increasingly used in geographical analyses. On the example of the researched region of Timočka Krajina (defined for the needs of elaborating the Regional Spatial Plan), the possibilities for applying this method in disaggregation of traditional models of population density, which are created using the choropleth maps at the level of statistical spatial units, are shown. The applied method is based on the use of ancillary spatial predictors which are in correlation with a targeted variable, the population change index. For this purpose, spatial databases have been used such as digital terrain model, distances from the network of I and II category state roads, as well as soil sealing databases. Spatial model has been developed in the GIS software environment using commercial GIS applicati...ons, as well as open source GIS software. Population change indexes for the period 1961-2002 have been mapped based on population census data, while the data on planned population forecast have been used for the period 2002-2027.
Кључне речи:
Geographically weighted regression / population change index / dasymetric method / GIS / open source / Timočka KrajinaИзвор:
Zbornik radova Geografskog instituta "Jovan Cvijić", SANU, 2011, 61, 3, 151-167Издавач:
- Geographical Institute "Jovan Cvijić" SASA
Финансирање / пројекти:
- Просторни, еколошки, енергетски и друштвени аспекти развоја насеља и климатске промене - међусобни утицаји (RS-36035)
- Улога и имплементација државног просторног плана и регионалних развојних докумената у обнови стратешког истраживања, мишљења и управљања у Србији (RS-47014)
Група
RAUmPlanTY - JOUR AU - Bajat, Branislav AU - Krunić, Nikola AU - Kilibarda, Milan AU - Samardžić-Petrović, Mileva PY - 2011 UR - https://raumplan.iaus.ac.rs/handle/123456789/196 AB - This paper presents possibilities of applying the geographically weighted regression method in mapping population change index. During the last decade, this contemporary spatial modeling method has been increasingly used in geographical analyses. On the example of the researched region of Timočka Krajina (defined for the needs of elaborating the Regional Spatial Plan), the possibilities for applying this method in disaggregation of traditional models of population density, which are created using the choropleth maps at the level of statistical spatial units, are shown. The applied method is based on the use of ancillary spatial predictors which are in correlation with a targeted variable, the population change index. For this purpose, spatial databases have been used such as digital terrain model, distances from the network of I and II category state roads, as well as soil sealing databases. Spatial model has been developed in the GIS software environment using commercial GIS applications, as well as open source GIS software. Population change indexes for the period 1961-2002 have been mapped based on population census data, while the data on planned population forecast have been used for the period 2002-2027. PB - Geographical Institute "Jovan Cvijić" SASA T2 - Zbornik radova Geografskog instituta "Jovan Cvijić", SANU T1 - Spatial modelling of population concentration using geographically weighted regression method VL - 61 IS - 3 SP - 151 EP - 167 DO - 10.2298/IJGI1103151B ER -
@article{ author = "Bajat, Branislav and Krunić, Nikola and Kilibarda, Milan and Samardžić-Petrović, Mileva", year = "2011", abstract = "This paper presents possibilities of applying the geographically weighted regression method in mapping population change index. During the last decade, this contemporary spatial modeling method has been increasingly used in geographical analyses. On the example of the researched region of Timočka Krajina (defined for the needs of elaborating the Regional Spatial Plan), the possibilities for applying this method in disaggregation of traditional models of population density, which are created using the choropleth maps at the level of statistical spatial units, are shown. The applied method is based on the use of ancillary spatial predictors which are in correlation with a targeted variable, the population change index. For this purpose, spatial databases have been used such as digital terrain model, distances from the network of I and II category state roads, as well as soil sealing databases. Spatial model has been developed in the GIS software environment using commercial GIS applications, as well as open source GIS software. Population change indexes for the period 1961-2002 have been mapped based on population census data, while the data on planned population forecast have been used for the period 2002-2027.", publisher = "Geographical Institute "Jovan Cvijić" SASA", journal = "Zbornik radova Geografskog instituta "Jovan Cvijić", SANU", title = "Spatial modelling of population concentration using geographically weighted regression method", volume = "61", number = "3", pages = "151-167", doi = "10.2298/IJGI1103151B" }
Bajat, B., Krunić, N., Kilibarda, M.,& Samardžić-Petrović, M.. (2011). Spatial modelling of population concentration using geographically weighted regression method. in Zbornik radova Geografskog instituta "Jovan Cvijić", SANU Geographical Institute "Jovan Cvijić" SASA., 61(3), 151-167. https://doi.org/10.2298/IJGI1103151B
Bajat B, Krunić N, Kilibarda M, Samardžić-Petrović M. Spatial modelling of population concentration using geographically weighted regression method. in Zbornik radova Geografskog instituta "Jovan Cvijić", SANU. 2011;61(3):151-167. doi:10.2298/IJGI1103151B .
Bajat, Branislav, Krunić, Nikola, Kilibarda, Milan, Samardžić-Petrović, Mileva, "Spatial modelling of population concentration using geographically weighted regression method" in Zbornik radova Geografskog instituta "Jovan Cvijić", SANU, 61, no. 3 (2011):151-167, https://doi.org/10.2298/IJGI1103151B . .