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Mounting awareness of the discriminatory distribution of environmental factors has increasingly placed environmental justice at the forefront of discussions on sustainable development, but responses to these disparities are often too little, too late. Remote sensing has emerged as a potential solution to this problem, capitalizing on the ability to capture high-resolution, spatially explicit data in near-real time. However, a conventional reliance on physical measurements and surface-level analyses risks overlooking the experiences and perceptions of affected communities. It is against this backdrop that the potential integration of remote sensing imagery and socially sensed big data such as social media data assumes a novel and promising role. This study aims to discern the feasibility, opportunities, and implications of integrating the spatial insights provided by remote sensing with the experiential narratives shared on social media platforms, bridging the gap between objective environmental data and community-driven perspectives. We explore this subject in two ways, analyzing the geographic relationship between environmental justice Tweets and environmental justice factors, and reviewing Tweets produced during an extensive wildfire. Remote sensing indexes for green and blue space were reviewed and tested, selecting the measures of best fit to act as independent variables alongside traditional environmental justice factors in the broader analysis. Results from regression models indicate a negative relationship between the number of Tweets utilizing environmental justice relevant terms and the presence of ecosystem services as captured by an NDMI, suggesting a broad awareness of injustice and a relationship between remote sensing and social media. However, there is simultaneously a negative relationship between socially vulnerable populations and Tweets with environmental justice words. This suggests that generally, there is discussion on Twitter about injustice when resources are not present, but the voices of vulnerable populations are often less visible, either as a result of urban bias or a lack of concern for injustices due to habitual ignorance. Our study demonstrates the potential for integrating remote sensing imagery and social sensing data to play a substantial role in detecting injustices and corroborating data collected through community science initiatives. [ABSTRACT FROM AUTHOR]

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources. [ABSTRACT FROM AUTHOR]

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing "big data" and modern urban study communities. [ABSTRACT FROM AUTHOR]

Investigating urban vibrancy and factors that impact urban vibrancy aids the understanding of urban land use policies, provides solid foundation for scientific urban planning. The boom in information and communication technologies and the advancement of big data extraction provides new sources of data and make it possible to measure and analyze urban vibrancy at a finer spatial and temporal scale. This study aims to portray the spatiotemporal variation patterns of urban vibrancy in 24 h and investigate the potential influence mechanism of it. The central districts of Beijing consisting of 135 subdistricts are selected as the study area. Massive and spontaneous geo-tagged check-in data released from social media platforms has attracted increasing attentions in urban vibrancy studies because it reflects well people's activities at a certain time, which is a good proxy for urban vibrancy. This study hence uses the check-in data from Weibo, the largest microblogging platform in China, to proxy urban vibrancy. We also extract from multisource spatial big data to explore potential determinants of urban vibrancy. This study seeks to reveal the global and local varying impacts of different factors on urban vibrancy by employ spatial lag model (SLM) and multiscale geographically weighted regression (MGWR) model. Results show that the increase in the number of different point of interests (POIs) improves urban vibrancy. Their effects on vibrancy vary at different times but have no obvious spatial scale variation. Splitting effect and attraction effect of land use diversity are introduced to explain its significantly negative effect on the intensity and fluctuation of urban vibrancy. It requires the wisdom of urban planners to balance these two effects of land use diversity in the process of urban construction. The guidance strategy of "highlighting the main functions and enriching the auxiliary functions" is helpful to build vibrant cities. Socioeconomical conditions, location and accessibility have different spatial scale effects on urban vibrancy at subdistrict level. These findings enable us to have a deeper understanding of the variation patterns and influence mechanism of urban vibrancy in China's megacities and benefit the urban land use policy research and management community. • Weibo check-ins are used to represent urban vibrancy in Beijing at subdistrict level. • We investigated point of interests, socioeconomic and locational factors' impact on urban vibrancy. • Spatial big data provides in-depth understanding of urban vibrancy and its determinants. • Multiscale geographically weighted regression analysis was applied. • Determinants' impacts varying over places and scales. [ABSTRACT FROM AUTHOR]