Computational urban science最新文献

The Community-Group-Buying Points (CGBPs) flourished during COVID-19, safeguarding the daily lives of community residents in community lockdowns, and continuing to serve as a popular daily shopping channel in the Post-Epidemic Era with its advantages of low price, convenience and neighborhood trust. These CGBPs are allocated on location preferences however spatial distribution is not equal. Therefore, in this study, we used point of interest (POI) data of 2,433 CGBPs to analyze spatial distribution, operation mode and accessibility of CGBPs in Xi'an city, China as well as proposed the location optimization model. The results showed that the CGBPs were spatially distributed as clusters at α = 0.01 (Moran's I = 0.44). The CGBPs operation mode was divided into preparation, marketing, transportation, and self-pickup. Further CGBPs were mainly operating in the form of joint ventures, and the relying targets presented the characteristic of 'convenience store-based and multi-type coexistence'. Influenced by urban planning, land use, and cultural relics protection regulations, they showed an elliptic distribution pattern with a small oblateness, and the density showed a low-high-low circular distribution pattern from the Palace of Tang Dynasty outwards. Furthermore, the number of communities, population density, GDP, and housing type were important driving factors of the spatial pattern of CGBPs. Finally, to maximize attendance, it was suggested to add 248 new CGBPs, retain 394 existing CGBPs, and replace the remaining CGBPs with farmers' markets, mobile vendors, and supermarkets. The findings of this study would be beneficial to CGB companies in increasing the efficiency of self-pick-up facilities, to city planners in improving urban community-life cycle planning, and to policymakers in formulating relevant policies to balance the interests of stakeholders: CGB enterprises, residents, and vendors.

The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index's relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas.

The COVID-19 pandemic caused lifestyle changes and has led to the new electricity demand patterns in the presence of non-pharmaceutical interventions such as work-from-home policy and lockdown. Quantifying the effect on electricity demand is critical for future electricity market planning yet challenging in the context of limited smart metered buildings, which leads to limited understanding of the temporal and spatial variations in building energy use. This study uses a large scale private smart meter electricity demand data from the City of Austin, combined with publicly available environmental data, and develops an ensemble regression model for long term daily electricity demand prediction. Using 15-min resolution data from over 400,000 smart meters from 2018 to 2020 aggregated by building type and zip code, our proposed model precisely formalizes the counterfactual universe in the without COVID-19 scenario. The model is used to understand building electricity demand changes during the pandemic and to identify relationships between such changes and socioeconomic patterns. Results indicate the increase in residential usage , demonstrating the spatial redistribution of energy consumption during the work-from-home period. Our experiments demonstrate the effectiveness of our proposed framework by assessing multiple socioeconomic impacts with the comparison between the counterfactual universe and observations.

The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals' perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.

The bath industry has multiple attributes, such as economic, health, and cultural communication. Therefore, exploring this industry's spatial pattern evolution is crucial to forming a healthy and balanced development model. Based on POI (Points of Interest) and population migration data, this paper uses spatial statistics and radial basis function neural network to explore the spatial pattern evolution and influencing factors of the bath industry in mainland China. The results show that: (1) The bath industry presents a strong development pattern in the north, south-northeast, and east-northwest regions and weak development in the rest of the country. As a result, the spatial development of new bath space is more malleable. (2) The input of bathing culture has a guiding role in developing the bath industry. The growth of market demand and related industries has a specific influence on the development of the bath industry. (3) Improving the bath industry's adaptability, integration, and service level are feasible to ensure healthy and balanced development. (4) Bathhouses should improve their service system and risk management control during the pandemic.

The use of telehealth has increased significantly over the last decade and has become even more popular and essential during the COVID-19 pandemic due to social distancing requirements. Telehealth has many advantages including potentially improving access to healthcare in rural areas and achieving healthcare equality. However, there is still limited research in the literature on how to accurately evaluate telehealth accessibility. Here we present the Enhanced Two-Step Virtual Catchment Area (E2SVCA) model, which replaces the binary broadband strength joint function of the previous Two-Step Virtual Catchment Area (2SVCA) with a step-wise function that more accurately reflects the requirements of telehealth video conferencing. We also examined different metrics for representing broadband speed at the Census Block level and compared the results of 2SVCA and E2VCA. Our study suggests that using the minimum available Internet speed in a Census Block can reveal the worst-case scenario of telehealth care accessibility. On the other hand, using the maximum of the most frequent available speeds reveals optimal accessibility, while the minimum of the most frequent reflects a more common case. All three indicators showed that the 2SVCA model generally overestimates accessibility results. The E2SVCA model addresses this limitation of the 2SVCA model, more accurately reflects reality, and more appropriately reveals low accessibility regions. This new method can help policymakers in making better decisions about healthcare resource allocations aiming to improve healthcare equality and patient outcomes.

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 - 10 km) and neighborhood (order of 0.1 - 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This 'DownScaleBench' tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

People express opinions, make connections, and disseminate information on social media platforms. We considered grocery-related tweets as a proxy for grocery shopping behaviors or intentions. We collected data from January 2019 to January 2022, representing three typical times of the normal period before the COVID-19 pandemic, the outbreak period, and the widespread period. We obtained grocery-related geotagged tweets using a search term index based on the top 10 grocery chains in the US and compiled Google Trends online grocery shopping data. We performed a topic modeling analysis using the Latent Dirichlet Allocation (LDA), and verified that most of the collected tweets were related to grocery-shopping demands or experiences. Temporal and geographical analyses were applied to investigate when and where people talked more about groceries, and how COVID-19 affected them. The results show that the pandemic has been gradually changing people's daily shopping concerns and behaviors, which have become more spread throughout the week since the pandemic began. Under the causal impact of COVID-19, people first experienced panic buying groceries followed by pandemic fatigue a year later. The normalized tweet counts show a decrease of 40% since the pandemic began, and the negative causal effect can be considered statistically significant (p-value = 0.001). The variation in the quantity of grocery-related tweets also reflects geographic diversity in grocery concerns. We found that people in non-farm areas with less population and relatively lower levels of educational attainment tend to act more sensitively to the evolution of the pandemic. Utilizing the COVID-19 death cases and consumer price index (CPI) for food at home as background information, we proposed an understanding of the pandemic's impact on online grocery shopping by assembling, geovisualizing, and analyzing the evolution of online grocery behaviors and discussion on social media before and during the pandemic.