Computational urban science最新文献

Redlining is a discriminatory practice of systematically denying loans or mortgages to residents in specific neighborhoods based on racial or ethnical composition. In current literature research, there is a lack of understanding of the public perceptions of impacts of historical redlining practices at large geographic scales. Although some social groups and organizations conducted surveys or interviews to obtain public perceptions of it on small groups of people in certain areas, our knowledge of the impacts of redlining is limited and may reflect bias. This study used geotagged tweets from 2011 to 2023 to investigate public perceptions of redlining practices in U.S. counties. Multiscale geographically weighted regression (MGWR) was performed to explore both spatial heterogeneity and varying scales of associations between percentage of redlining-related geotagged tweets with negative sentiment and potential explanatory shaping factors in U.S. counties. Counties with a higher average household size, a higher percentage of people aged 45+, a lower homeownership rate, and a higher mobile home percentage have a significant association nationwide with more negative-sentiment expression in redlining-related tweets. However, counties with a lower insurance coverage are less likely to express negative sentiment in redlining-related tweets in some eastern U.S. counties, indicating a local significant association. The findings help people better understand the relationship between public perceptions of redlining practices and potential shaping factors. This study's methodology can also be applied to investigate public perspectives or perceptions on other controversial social topics.

This study investigates how the collective mobility (including movement and visiting) of residents and non-residents affects neighbourhood burglary levels. While past research has linked mobility to urban crime, this study explores how these relationships vary across population groups and social contexts at the neighbourhood level. Using mobile phone GPS data, we distinguished between residents and non-residents based on daily movement patterns. We then measured their mobility within defined spatial and temporal units. An explainable machine learning method (XGBoost and SHAP) was used to assess how mobility patterns influence burglary in London's LSOAs from 2020 to 2021. Results show that increased collective mobility is generally associated with higher burglary levels. Specifically, non-resident footfall and residents' stay-at-home time have a stronger influence than other variables like residents' travelled distance. The impact also varies across neighbourhoods and shifts during periods of COVID-19 restrictions and relaxations. These findings confirm the dynamic link between mobility and crime, highlighting the value of understanding population-specific patterns to inform more targeted policing strategies.

This study empirically compares multiple eXplainable Artificial Intelligence (XAI) techniques to interpret short-term (weekly) machine learning-based burglary predictions at the micro-place level in Ghent, Belgium. While previous research predominantly relies on SHAP to interpret spatiotemporal crime predictions, this is the first study to systematically evaluate SHAP alongside other XAI techniques, offering both global and local model interpretability within the context of crime prediction. Using data from 2014 to 2018 on residential burglary, repeat and near-repeat victimization, environmental features, socio-demographic indicators, and seasonal effects, we trained an XGBoost model with 76 features to predict weekly burglary hot spots. This model serves as a basis for comparing the interpretative power of different XAI techniques. Our results show that built environment and land use characteristics are the most consistent global predictors of burglary risk. However, their influence varies substantially at the local level, revealing the importance of spatial context. While global feature importance rankings are broadly aligned across XAI techniques, local explanations, especially between SHAP and LIME, often diverge. These discrepancies highlight the need for careful method selection when translating predictions into crime prevention strategies. In addition, this study demonstrates that short-term burglary risks are influenced by complex interactions and threshold effects between environmental and social disorganization features. We interpret these findings through the lens of criminological theory, and argue for more integrated approaches that go beyond examining the isolated effects of specific crime predictors. Finally, we call for greater attention to the methodological implications that arise from applying different interpretability techniques, particularly when machine learning model outputs are used to inform crime prevention and policy decisions.

Supplementary information: The online version contains supplementary material available at 10.1007/s43762-025-00185-x.

This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and public awareness through a publicly accessible dashboard. Unlike platforms developed primarily for wildland fire applications, FireCom is specifically designed to account for urban complexities such as building canyon effects on smoke dispersion and the heightened exposure risks associated with dense populations. This study contributes a scalable, replicable framework for municipalities seeking data-driven tools for proactive disaster management, with implications for broader climate resilience planning in urban areas.

The recent COVID-19 pandemic has underscored the need for effective public health interventions during infectious disease outbreaks. Understanding the spatiotemporal dynamics of urban human behaviour is essential for such responses. Crowd-sourced geo-data can be a valuable data source for this understanding. However, previous research often struggles with the complexity and heterogeneity of such data, facing challenges in the utilisation of multiple modalities and explainability. To address these challenges, we present a novel approach to identify and rank multimodal time series features derived from mobile phone and geo-social media data based on their association with COVID-19 infection rates in the municipality of Rio de Janeiro. Our analysis spans from April 6, 2020, to August 31, 2021, and integrates 59 time series features. We introduce a feature selection algorithm based on Chatterjee's Xi measure of dependence to identify relevant features on an Área Programática da Saúde (health area) and city-wide level. We then compare the predictive power of the selected features against those identified by traditional feature selection methods. Additionally, we contextualise this information by correlating dependence scores and model error with 15 socio-demographic variables such as ethnic distribution and social development. Our results show that social media activity related to COVID-19, tourism and leisure activities was associated most strongly with infection rates, indicated by high dependence scores up to 0.88. Mobility data consistently yielded low to intermediate dependence scores, with the maximum being 0.47. Our feature selection approach resulted in better or equivalent model performance when compared to traditional feature selection methods. At the health-area level, local feature selection generally yielded better model performance compared to city-wide feature selection. Finally, we observed that socio-demographic factors such as the proportion of the Indigenous population or social development correlated with the dependence scores of both mobility data and health- or leisure-related semantic topics on social media. Our findings demonstrate the value of integrating localised multimodal features in city-level epidemiological analysis and offer a method for effectively identifying them. In the broader context of GeoAI, our approach provides a framework for identifying and ranking relevant spatiotemporal features, allowing for concrete insights prior to model building, and enabling more transparency when making predictions.

The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world.

The rapid growth of marginal settlements in the Global South, largely fueled by the resettlement of millions of internally displaced people (IDPs), underscores the urgent need for tailored housing solutions for these vulnerable populations. However, prevailing approaches have often relied on a one-size-fits-all model, overlooking the diverse socio-spatial realities of IDP communities. Drawing on a case study in Medellin, Colombia, where a significant portion of the population consists of forced migrants, this interdisciplinary study merges concepts from human geography and urban theory with computational methods in remote sensing and exploratory spatial data analysis. By integrating socio-spatial theory with quantitative analysis, we challenge the conventional housing paradigm and propose a novel framework for addressing the housing needs of IDPs. Employing a three-phase methodology rooted in Lefebvre's theoretical framework on the production of space, including participatory mapping, urban morphology characterization, and similarity analysis, we identify distinct patterns within urban IDP settlements and advocate for culturally sensitive housing policies. Our analysis, focusing on Colombia, the country with the largest IDP population globally, reveals the limitations of standardized approaches and highlights the importance of recognizing and accommodating socio-cultural diversity in urban planning. By contesting standardized socio-spatial practices, our research aims not only to promote equality but also to foster recognition and inclusivity within marginalized communities.

This paper proposes a framework to examine how neighborhood factors influence criminal justice (CJ) contact and contribute to disparities across multiple stages of the justice process. By conceptualizing the punishment process as a dynamic set of decision-making points, this study highlights the role of neighborhood context in shaping offenders' CJ trajectories and post-CJ residential inequality. Using Harris County, Texas, as a case study, this research considers individual-, neighborhood-, and event-level variables to understand the cumulative effects of neighborhood characteristics on CJ outcomes. This study underscores the critical need to investigate neighborhood mobility and its broader implications for community development and public policy. The findings can be supported by extensive data from the Federal Statistical Research Data Centers and the Criminal Justice Administrative Records System, offering a robust analysis of offenders' spatial patterns and economic transitions.