Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model.

It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure.

https://www.mdpi.com/2220-9964/8/6/283/pdf

Detecting Urban Polycentric Structure from POI Data

Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event

Extracting the location of flooding events in urban systems and analyzing the semantic risk using social sensing data

The COVID-19 pandemic is the most difficult challenge that has affected humanity in recent decades. It has disrupted many features of development with domino effects in the social sphere. This study reviews the literature on the social effects of the COVID-19 pandemic and identifies the affected social areas that have undergone dramatic changes during the pandemic. We use inductive content analysis and thematic analysis to review the literature. The results show that there are seven major areas that have been negatively affected by the COVID-19 pandemic: health, social vulnerability, education, social capital, social relationships, social mobility, and social welfare. The literature has reported dramatic psychological and emotional effects, exacerbation of segregation and poverty, disruption in educational systems and formation of an information gap, as well as a declining trends of social capital among communities. We highlight some lessons that can be learned from the pandemic to enhance social resilience in future. Among others, to effectively respond to the pandemic and other potential future adverse events, governments should adopt fair policies, identify the required changes in the socially affected contexts and take necessary responsive actions, and adopt collaboratively designed approaches to increase social resilience. 

/pdf/impacts-of-the-covid-19-pandemic-on-the-social-sphere-and-27qez9g6.pdf

Impacts of the COVID-19 pandemic on the social sphere and lessons for crisis management: a literature review

The spatial structures of cities have changed dramatically with rapid socio-economic development in ways that are not well understood. To support urban structural analysis and rational planning, we propose a framework to identify urban functional regions and quantitatively explore the intensity of the interactions between them, thus increasing the understanding of urban structures. A method for the identification of functional regions via spatial semantics is proposed, which involves two steps: (1) the study area is classified into three types of functional regions using taxi origin/destination (O/D) flows; and (2) the spatial semantics for the three types of functional regions are demonstrated based on point-of-interest (POI) categories. To validate the existence of urban functional regions, we explored the intensity of interactions quantitatively between them. A case study using POI data and taxi trajectory data from Beijing validates the proposed framework. The results show that the proposed framework can be used to identify urban functional regions and promotes an enhanced understanding of urban structures.

Using Spatial Semantics and Interactions to Identify Urban Functional Regions

Disaster damage assessment based on fine-grained topics in social media

https://doi.org/10.1016/j.jag.2022.103007

Assessment of community vulnerability during the COVID-19 pandemic: Hong Kong as a case study

Exploring the nonlinear impact of visual environment on residents’ happiness: a computational framework integrating semantic and geometric features

A realistic and multilevel measurement of citywide spatial patterns of economic segregation based on human activities

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