The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborhood features is also detected at town level. GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market, which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present. The results provide references for local planning departments, helping to reveal the complicated relationship and spatial patterns between housing price and determinants over space.

Mountain cities are characterized by undulating terrain, complex road networks, and diverse road facilities, which makes accessing food more difficult than in cities with a flat terrain. This study proposes an enhanced two-step method based on the Baidu map service for the construction of supermarket–market–retail food sales architecture and for calculating food accessibility. The accessibility indices of seven major food categories (grains and oils, fruits, vegetables, seafood, meat, milk, and eggs) were calculated considering the principle of the fairest walking routes in Chongqing. The correlations between food accessibility and house price and house age in Chongqing were explored through local Moran’s analysis and geographically weighted regression. The correlations illustrated the fairness of the distribution of food accessibility in Chongqing among the poor and rich. The experiments showed generally well-developed food accessibility in the main urban areas of Chongqing. However, accessibility to fresh fruits and vegetables lagged in newly built urban areas.

This research assessed the influences of four band combinations and three types of pretrained weights on the performance of semantic segmentation in extracting refugee dwelling footprints of the Kule refugee camp in Ethiopia during a dry season and a wet season from very high spatial resolution imagery. We chose a classical network, U-Net with VGG16 as a backbone, for all segmentation experiments. The selected band combinations include 1) RGBN (Red, Green, Blue, and Near Infrared), 2) RGB, 3) RGN, and 4) RNB. The three types of pretrained weights are 1) randomly initialized weights, 2) pretrained weights from ImageNet, and 3) weights pretrained on data from the Bria refugee camp in the Central African Republic). The results turn out that three-band combinations outperform RGBN bands across all types of weights and seasons. Replacing the B or G band with the N band can improve the performance in extracting dwellings during the wet season but cannot bring improvement to the dry season in general. Pretrained weights from ImageNet achieve the best performance. Weights pretrained on data from the Bria refugee camp produced the lowest IoU and Recall values.