Sentiment affects every aspect of people’s lives and has strong impact on their mental health. This paper explores the sentiments extracted from Geo-tweets data from January to December 2016, analyzed in the spatial and temporal perspective. Considering the noisy data from the Geo-tweets, a workflow is created for extracting the tweets which are posted by local users. The workflow makes it convenient for other researchers to reproduce, replicate or extend the procedures using similar Geo-tweet dataset. The workflow is available at Harvard Dataverse. Using the cleaned data, each tweet’s sentiment is classified according to the content. Then, the overall temporal variations of total number of positive, neural, and negative sentiments are analyzed on a monthly, daily and hourly level. From a spatial perspective, the Local Indicators of Spatial Association (LISA) statistical method is employed to discover the spatial clusters. In order to explore the content of positive sentiments, this paper applies the Latent Dirichlet Allocation (LDA) model to classify the Geo-tweets with positive sentiments into different topics. Combining the geospatial information with the topics, some patterns are found which demonstrate the associations between the nature of Twitter content and the characteristics of places and users. For example, weekend events and friend and family gatherings are the time that users prefer to post positive tweets. In the western part of US, users tend to post more photos to share the great moment on Twitter than other parts of the US.

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection.

Understanding the health-related quality of life (HrQoL) of hospitalized COVID-19 survivors is an emerging global challenge arising from the current pandemic. A qualitative study of the experiences of sixteen hospitalized COVID-19 survivors from Nanning City, China, was conducted using semi-structured telephone interviews in May 2020. These first-hand accounts were critically and empirically analysed to identify emerging health and social issues, and provide potential solutions to improve survivors’ quality of life. This in-depth, qualitative study of HrQoL for hospitalized COVID-19 survivors provides the first empirical evidence and conceptual framework with eight dimensions (physical symptoms, anxiety, trauma, economic loss, place-based identity, self-stigma, health self-interventions, and changing lifestyle) for understanding the physiological, psychological, socio-economic and health behavioral aspects of their daily lives. We argue that local and global governments should provide integrated healthcare, social and digital infrastructure to support this vulnerable group. More comparative and multi-disciplinary studies in this area are needed to generate academic standards of assessing health-related quality of life and produce good practice guidelines for promoting urban resilience in response to public health disasters.

Urban green spaces are essential for regulating land surface temperature (LST), but current research frequently neglects their structural complexity and perceived accessibility by humans. To bridge this gap, our study utilizes two complimentary metrics: the satellite-derived Normalized Difference Vegetation Index (NDVI) and the street-level Green View Index (GVI), both employed to assess Guangzhou’s urban thermal environment. Distinct statistical and spatial distribution patterns of NDVI and GVI were identified among districts in Guangzhou, China. NDVI values varied between 0.12 and 0.64, whereas GVI values ranged from 0.18 to 0.47. The LST varied from 27.61 to 41.99 °C, with a global Moran’s I of 0.96 signifying robust spatial autocorrelation. To evaluate the impact of urban morphology on LST, we employed three regression models, with the multiscale geographically weighted regression (MGWR) demonstrating superior performance, with R² = 0.727, AICc = 2185.43, and RSS = 328.11. Regression results revealed that building density (BD) and average building volume (BV) are positively connected with LST. In contrast, GVI and NDVI exhibit negative associations. This study integrates vertical (NDVI) and horizontal (GVI) greenery viewpoints with urban morphological characteristics, offering actionable insights for urban planners to enhance green infrastructure and more effectively offset the urban heat island effect.

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.