GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Factor</td><td class="align_center">Variable</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Geographical location condition (D)</td><td class="align_center">Distance away from the CBD (d1)</td></tr><tr><td class="align_left">Transportation accessibility (T)</td><td class="align_center">Bus stop density (t1) Number of metro station (t2) Road weighted density (t3)</td></tr><tr><td class="align_left">Commercial service intensity (B)</td><td class="align_center">Commercial building density (b1) Financial institution density (b2) Restaurant density (b3) Retail store/supermarket density (b4)</td></tr><tr><td class="align_left">Public service intensity (p)</td><td class="align_center">Educational institution density (p1) Medical institution density (p2) Government agency density (p3) Park/square density (p4)</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Selection of the influencing factors and their variables.</div>

Complexity

tab1

Table 1

Table 1: GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China 

Table 1 | GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China 