This study compares global and local analyses of non-car commuting modes and the probability of increasing modes use in different urban and rural areas for a case study in Yorkshire, UK, with commuter residence areas used as the response variable. The analyses compared Generalized Linear Models of commuting by bus, cycling and walking to estimate the probability of increasing sustainable modes use in commuters in urban areas, relative to rural areas. The three variables were found to be significant predictors for the models and indicate differential odds of commuting from urban areas relative to rural ones. An analysis of the non-stationarity of was undertaken using a Geographically Weighted Regression analysis, which showed how the probability of residing in a particular type of urban and rural area, as described by commuting patterns, varied spatially within the study region. The local analyses provide a critical information able to support and guide local policy in its ambition to increase sustainable transport modes and to reduce car dependence in both rural and urban areas.
It is necessary to standardize the legends of the geological map interpreted with remote sensing images in various application fields duly to achieve standardization and automation of the legends. This paper analyzes main contents and expression forms of legends based on the principle of legend compilation and expansion, and discusses generation method, compilation and expression of various legends. 455 legends of the geological map interpreted with remote sensing images were designed and produced by using Font Creator and ArcGIS software. These legends can be applied to remote sensing survey of regional geology and mineral geology, remote sensing monitoring of fundamental geology environment, remote sensing monitoring of geological disasters, remote sensing monitoring of mineral resources development and others, which can basically meet needs of actual work, and can provide technical support for standardized research of the geological map interpreted with remote sensing images.
Cities in tropical regions are experiencing high heat risks by overlaying the urban heat island (UHI) effect. Urban green space (UGS) can provide local cooling effect and reduce UHI. However, there still lack a comprehensive exploration of the characteristics of UHI and cooling effect of UGS due to high cloud coverage and limited number of available remote sensing observations. In this study, the enhanced spatial and temporal adaptive reflectance data fusion method was employed to develop an enhanced land surface temperature data in winter seasons in three tropical megacities, Dhaka, Kolkata, and Bangkok. The spatiotemporal variations of surface urban heat island (SUHI) were explored from 2000 to 2020 with a 5-years interval. The optimal size of UGS associated with its cooling effects was assessed by using the threshold value of efficiency (TVoE). The relationship between the intensity and range of urban cooling island (UCI) and four landscape metrics of green space patches, total area (P_Area), shape index (P_SI), normalized difference vegetation index (P_NDVI), and land surface temperature (P_LST), were analyzed. The results show that the average SUHI intensity increased by 0.98°C, 1.42°C, and 0.73°C in Dhaka, Kolkata, and Bangkok, respectively, from 2000 to 2020. The maximum intensity of UCI ranges from 4.83°C in Bangkok to 8.07°C in Kolkata, and the maximum range of UCI varies from 300 m in Bangkok to 420 m in Kolkata. The optimal size of green space is 0.37 ha, 0.77 ha, and 0.42 ha in Dhaka, Kolkata, and Bangkok, respectively. The P_NDVI and P_Area had significant positive effects on UCI intensity and range, while the background temperature had significant negative effects. With higher background temperature, the optimal patch size of UGS is larger. This study provides useful information for developing effective heat mitigation and adaptation strategies to enhance climate resilience in tropical cities.
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