Information about the functions of urban buildings is helpful not only for developing a better understanding of how cities work, but also for establishing a basis for policy makers to evaluate and improve the effectiveness of urban planning. Despite these advantages, however, and perhaps simply due to a lack of available data, few academic studies to date have succeeded in integrating multi-source ‘big data’ to examine urban land use at the building level. Responding to this deficiency, this study integrated multi-source big data (WeChat users’ real-time location records, taxi GPS trajectories data, Points of Interest (POI) data, and building footprint data from high-resolution Quickbird images), and applied the proposed density-based method to infer the functions of urban buildings in Tianhe District, Guangzhou, China. The results of the study conformed to an overall detection rate of 72.22%. When results were verified against ground-truth investigation data, the accuracy rate remained above 65%. Two important conclusions can be drawn from our analysis: 1.The use of WeChat data delivers better inference results than those obtained using taxi data when used to identify residential buildings, offices, and urban villages. Conversely, shopping centers, hotels, and hospitals, were more easily identified using taxi data. 2. The use of integrated multi-source big data is more effective than single-source big data in revealing the relation between human dynamics and urban complexes at the building scale.

This research examines the consumer nutrition environment in the selected neighborhoods identified as food deserts, food swamps, and food oases in Austin, Texas, by considering food availability, food price, food quality, and food labeling. A food auditing instrument M-TxNEA-S (He Jin, San Marcos, TX, USA) was developed to capture the unique dietary culture and food preferences in Texas. A total of 93 food items in 14 grocery stores and supermarkets (GS) and 32 convenience stores (CS) were surveyed. The GS in food swamps and food oases were found to offer significantly more healthy foods than the CS. The availability of healthy food in the GS in the food swamps and food oases is significantly higher than that of the GS from the food deserts; CS in the three neighborhoods did not exhibit a significant difference in healthy food availability. There was no significant difference between the price for the healthy items (lower fat, lower calorie, and whole grain) and that for the regular food options. No significant difference was found for food quality or food labeling between the stores from the different types of neighborhoods. The GS in food deserts are small grocery stores carrying limited ranges of foods. The establishment of larger food stores in the food deserts might not be very rewarding, but opening more small grocery stores with healthier options may alleviate food issues.

Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome.

The retail food environment draws much attention from scholars because it can shape individuals’ eating behaviors and health outcomes. Although much progress has been made, current retail food environment assessments mainly use simple food accessibility measures while overlooking the role of multiple transportation modes. This research proposed a multiple-mode Huff-based Two-step Floating Catchment Area (2SFCA) method to measure geographical access to food outlets in Austin, Texas. The spatial accessibility score was calculated with low to high impedance coefficients. Our analyses revealed an urban core-and-peripheral disparity in spatial accessibility to food outlets. We also compared the proposed multiple-mode Huff-based 2SFCA with its single-mode counterpart using t-test and relative difference methods. The comparison illustrates that the difference between the two methods of calculating healthy and unhealthy food accessibility is significant when the impedance coefficient is set to be 1.4 and 1.5, respectively. Our proposed multi-mode Huff-based 2SFCA method accounts for the various transport means and the spatial heterogeneity in population demand for food services; this could support developing intervention strategies to target under-served healthy food areas and over-served unhealthy food areas.

China’s urban villages have distinct characteristics compared with the ones in western countries. Identifying urban villages provides a basis for policymakers to evaluate and improve the effectiveness of urban planning in China and other developing countries. However, perhaps due to limitations of data acquisition among others, few urban studies have successfully identified urban villages at the building level. To fill the research gap, this paper has fused multiple sources of data and utilized a three-stage model to identify urban villages in Haizhu District (Guangzhou, China). The first stage discriminates residential buildings, offices, shops, and restaurants based on various peak times of bike trajectories in different types of buildings. However, the first stage could not distinguish the regular residential buildings (in cities) and residential buildings within urban villages due to the similarity of human activities between them. It then utilized a second stage to identify residential buildings within urban villages based on the area, height, and density of buildings. In the third stage, we used correction rules to identify buildings with mixed-use and single-use buildings within urban villages. The results showed that urban villages were mainly concentrated in the western and central regions of the Haizhu District. Most of them were adjacent to shopping buildings or high-rise residential buildings. Building height and density played critical roles in the characterization of residential buildings in urban villages. Our accuracy rate was around 85% when verified against ground-truth data.

Most existing poverty research focused on the identification of households in absolute poverty; few studies attempted to identify households in relative poverty (HRP). In this paper, we first developed an improved Bayes’s theorem algorithm to sense individuals’ spatio-temporal behavior characteristics via integrating mobile phone signaling, electricity consumption, and Points of Interest. We then utilized the improved random forest to identify HRP based on individuals’ spatio-temporal behavior characteristics and building properties. A total of 29,370 urban households in Fengchan community, Zhengzhou, China, were selected to conduct this study. The accuracy rate was about 90% when it was verified against the household survey data. Three conclusions can be drawn from our analysis: (1) the individuals’ spatio-temporal behavior characteristics played a more critical role in identifying HRP than building properties, (2) the identification accuracy of multi-source data is higher than that of single-source data, (3) mobile phone signaling records and building footprints data are more important in identifying HRP in low-rise buildings, while electricity consumption data is more crucial in the identification in high-rise buildings. Our proposed methods can accurately identify urban HRP, which is helpful to target interventions in the most needed areas. Our findings can inform relief policies in similar cities.