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Cities are frontlines to tackle climate change challenges including the urban heat island (UHI) effect. The classification and mapping of local climate zones (LCZs) can effectively and consistently describe the urban surface structure across urban regions. This study pays attention to two mainstream methods in classifying LCZs, namely, by using geographic information system (GIS) data such as building footprints or remote sensing (RS) satellite images. Little has been done to compare the divergence and coherence of the abovementioned two methods in modeling UHI. Thus, by comparing pairwise LCZ classes of different urban form characteristics in Guangzhou, this study investigated how GIS- and RS-based approaches complement or conflict with each other in explaining the variance of UHI measured by land surface temperature (LST). First, while both GIS-based (R2 0.724) and RS-based (R2 0.729) approaches can effectively explain heat risks measured by LST, the RS-based method slightly outperforms the GIS counterpart. Second, the sizes of LCZs classified by two methods in urban core districts tend to converge but diverge in urban outskirts with disparities in low-rise urban forms. Both approaches found that LCZs with higher heights are all cooler among compact forms. LCZ E is always related to the highest average LST, and LCZ 7, 8, and 10 contribute significantly to heat islands from both GIS and RS results. This study has developed a comparable framework that is evident based for city planners, architects, and urban policy makers to evaluate which approaches can more accurately reveal relations between UHI and urban geometry with land cover.

Higher housing demand leads to housing price increasing rapidly, with lower housing affordability. Under these circumstances, renting a place has progressively become an alternative way for residents of cities. Urban transportation is one of the most significant influencing factors for housing rent, which needs to be examined at greater depth. This study used complex network analysis to explore how transportation centrality impacts housing rent. The conclusions are: transportation network shows significant impacts on housing rent, with higher impacts of public transport network than street network. Closeness in public transport has the highest impact on housing rent. Each centrality aspect influences housing rent differently, and relative accessibility in public transport network and transfer capacity in street network have the greatest impacts on housing rent. This article discussed two modes of urban transportation and their spatial characteristics. By refining the impacts of different transportation modes by network analysis, it provides a new perspective of urban transportation research and its spatial effects.