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There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.
G. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare 2021, 9, 1110 .
AMA StyleG. G. Md. Nawaz Ali, Mokhlesur Rahman, Amjad Hossain, Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, Jim Samuel. Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare. 2021; 9 (9):1110.
Chicago/Turabian StyleG. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. 2021. "Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics." Healthcare 9, no. 9: 1110.
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people’s socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
Mokhlesur Rahman; Kamal Chandra Paul; Amjad Hossain; G. G. Md. Nawaz Ali; Shahinoor Rahman; Jean-Claude Thill. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE Access 2021, 9, 72420 -72450.
AMA StyleMokhlesur Rahman, Kamal Chandra Paul, Amjad Hossain, G. G. Md. Nawaz Ali, Shahinoor Rahman, Jean-Claude Thill. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE Access. 2021; 9 (99):72420-72450.
Chicago/Turabian StyleMokhlesur Rahman; Kamal Chandra Paul; Amjad Hossain; G. G. Md. Nawaz Ali; Shahinoor Rahman; Jean-Claude Thill. 2021. "Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review." IEEE Access 9, no. 99: 72420-72450.
This study empirically investigates the causes of urban traffic congestion to bring into focus the variety of beliefs that provide support for policy interventions to mitigate traffic congestion in USt cities. We use a structural equation modeling (SEM) framework and position the analysis at the meso-scale (i.e., neighborhood shapes, sizes, density, land-use mix, street design, distribution of open space) to better align with policy and planning decisions and strategies. The analysis is carried out on 100 metropolitan areas in the USA, with three complementary metrics of urban traffic congestion and 25 factors representing the structural, socioeconomic, and behavioral aspects of urban areas. SEM results demonstrate that congestion is a complicated phenomenon where indirect effects are pathways powerful enough to offset direct effects under certain circumstances. We find that, beyond the role of urban population size, income and employment agglomeration lead to further traffic congestion. In contrast, the most influential tempering effects come from congestion’s own self-regulation impact, non-car mode choice behaviors, adequate highway transportation, focused community structures, urban density, and socioeconomic factors like car ownership. The article discusses the policy implications of this meso-scale empirical analysis.
Mokhlesur Rahman; Pooya Najaf; Milton Gregory Fields; Jean-Claude Thill. Traffic congestion and its urban scale factors: Empirical evidence from American urban areas. International Journal of Sustainable Transportation 2021, 1 -16.
AMA StyleMokhlesur Rahman, Pooya Najaf, Milton Gregory Fields, Jean-Claude Thill. Traffic congestion and its urban scale factors: Empirical evidence from American urban areas. International Journal of Sustainable Transportation. 2021; ():1-16.
Chicago/Turabian StyleMokhlesur Rahman; Pooya Najaf; Milton Gregory Fields; Jean-Claude Thill. 2021. "Traffic congestion and its urban scale factors: Empirical evidence from American urban areas." International Journal of Sustainable Transportation , no. : 1-16.
The way people allocate time across home and work activities determines their commuting patterns and frames much of the activities they undertake in the urban space. While inter-personal and intra-personal variability and repetitiveness in these activities have been documented, they remain largely underexplored. This study highlights the variations in and between individual home-work activity patterns by using information from metro smart card data as a proxy. To this end, the concept of individual space time usage matrix (STUM) is proposed and an analytical framework is developed in support of its use to depict how each rider allocates time in the vicinity of metro stations spatially and temporally. With this framework, we can classify space-time activity patterns that can be traced back to behavioral variability. By using Wuhan, China as a case study, variability in the number of home/work locations in personal activity patterns, and flexibility of work timeframes are investigated inter- and intra-personally. Our results show that about 25% of the population has a sophisticated home-work activity pattern that does not confirm to the ordinary 1-home 1-workplace pattern. Furthermore, even for this latter group, we find quite differentiated home and work timeframe patterns. The STUM is proved to be an effective and efficient concept to create a personal profile in analyzing the activity variability with big geo-spatial data.
Yang Zhou; Jean-Claude Thill; Yang Xu; Zhixiang Fang. Variability in individual home-work activity patterns. Journal of Transport Geography 2020, 90, 102901 .
AMA StyleYang Zhou, Jean-Claude Thill, Yang Xu, Zhixiang Fang. Variability in individual home-work activity patterns. Journal of Transport Geography. 2020; 90 ():102901.
Chicago/Turabian StyleYang Zhou; Jean-Claude Thill; Yang Xu; Zhixiang Fang. 2020. "Variability in individual home-work activity patterns." Journal of Transport Geography 90, no. : 102901.
This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries around the world during the early phase of the pandemic. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) framework is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing so as to reduce the risk of infection. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.
Mokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Early Evidence from 88 Countries. Sustainability 2020, 12, 9101 .
AMA StyleMokhlesur Rahman, Jean-Claude Thill, Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Early Evidence from 88 Countries. Sustainability. 2020; 12 (21):9101.
Chicago/Turabian StyleMokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. 2020. "COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Early Evidence from 88 Countries." Sustainability 12, no. 21: 9101.
In this paper, the determinants of the urban population in China are empirically investigated with a theoretical equilibrium model of city size. While much of the research on urban size focuses on the impact of agglomeration economies based on the “optimal city size” theory, this model is eschewed in our study due to its theoretical paradox in the real world, and we turn instead toward an equilibrium model proposed by Camagni et al. (Flux 10: 37–50, 2013). This equilibrium model is estimated on a sample of 262 prefectural cities in China with panel data analysis. Empirical results partially validate theory, but some estimates reflect the status of China as a developing country. The analysis also reveals determinants have impacts on Chinese cities that vary with the stage of economic development.
DaiDai Shen; Jean Claude Thill; Jiuwen Sun. The determinants of city population in China. Asia-Pacific Journal of Regional Science 2020, 5, 289 -304.
AMA StyleDaiDai Shen, Jean Claude Thill, Jiuwen Sun. The determinants of city population in China. Asia-Pacific Journal of Regional Science. 2020; 5 (1):289-304.
Chicago/Turabian StyleDaiDai Shen; Jean Claude Thill; Jiuwen Sun. 2020. "The determinants of city population in China." Asia-Pacific Journal of Regional Science 5, no. 1: 289-304.
This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.
Mokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Evidence from 88 Countries. 2020, 1 .
AMA StyleMokhlesur Rahman, Jean-Claude Thill, Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Evidence from 88 Countries. . 2020; ():1.
Chicago/Turabian StyleMokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. 2020. "COVID-19 Pandemic Severity, Lockdown Regimes, and People’s Mobility: Evidence from 88 Countries." , no. : 1.
This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.
Mokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People Mobility: Evidence from 88 Countries. 2020, 1 .
AMA StyleMokhlesur Rahman, Jean-Claude Thill, Kamal Chandra Paul. COVID-19 Pandemic Severity, Lockdown Regimes, and People Mobility: Evidence from 88 Countries. . 2020; ():1.
Chicago/Turabian StyleMokhlesur Rahman; Jean-Claude Thill; Kamal Chandra Paul. 2020. "COVID-19 Pandemic Severity, Lockdown Regimes, and People Mobility: Evidence from 88 Countries." , no. : 1.
It has been argued that internal and external contextualizations are important aspects of research intended to be relevant, valuable, and engaged in societal progress. In this article, we advocate for scholarship focus on the city and the region as a means to achieve this goal. The notion of systems and the spatial analytic perspective intersect to enrich research and avoid the pitfalls of scholarship that is disconnected from ground truth. We discuss the concepts of space, distance, region, and system within the logic of scientific discovery and present the main elements of spatial analytic research design. The article identifies some of the main research traditions consistent with this view and the main contributors to establishing the scientific robustness of this line of scholarship. The chapters of this edited volume are situated against this backdrop.
Jean-Claude Thill. Research on Urban and Regional Systems: Contributions from GIS&T, Spatial Analysis, and Location Modeling. Innovations in Urban and Regional Systems 2020, 3 -20.
AMA StyleJean-Claude Thill. Research on Urban and Regional Systems: Contributions from GIS&T, Spatial Analysis, and Location Modeling. Innovations in Urban and Regional Systems. 2020; ():3-20.
Chicago/Turabian StyleJean-Claude Thill. 2020. "Research on Urban and Regional Systems: Contributions from GIS&T, Spatial Analysis, and Location Modeling." Innovations in Urban and Regional Systems , no. : 3-20.
CO2 emissions embodied in trade (EET) may play an important role in reducing regional obligations toward carbon emission reduction within the context of a nation’s climate change and greenhouse gas emission policy. Based on a multi-region input-output (MRIO) analysis, this paper uses Shanghai’s 30 industrial sectors as a case to estimate 2007 carbon emissions embodied in domestic and international trade, so as to unravel sectoral and regional sources of emissions embodied in Shanghai’s domestic imports and exports, and to compare consumption-based and production-based emission accounting approaches. The MRIO framework implemented here overcomes many modeling and data limitations encountered in the existing literature, which provides more reliable and more disaggregated estimates in support of climate policy-making. We also discuss a policy option that potentially reduces the incidence of trade on the emissions of regional economies in the study area. One finding from this study is that Shanghai’s carbon emissions embodied in domestic trade with Chinese provinces are 82.60 million tons, which accounts for 50.67% of Shanghai’s overall emissions in 2007. In addition, the embodied emissions of import sources of Shanghai are mainly located in Northern and North Central China, while those of export flows are primarily observed in the coastal provinces of Eastern China. Another important finding is that, owing to the large scale of Shanghai’s EET, the incidence of inter-regional trade is largely curtailed if Shanghai participates in a coalition with neighboring provinces, instead of as an individual region. Therefore, the study suggests that one way to shield domestic trade from the stringent requirements of a national climate policy while preserving the economic benefits of inter-regional trade may be to encourage coalition formation in the Yangtze River Delta to boost cooperation around the Shanghai megacity. Therefore, we advocate a more regional approach to climate change and emissions mitigation policies.
Zhangqi Zhong; Jean-Claude Thill; Yi Sun; Zheng Wang. Carbon Emissions Embodied in Trade and Urban Regional Climate Policy-Making in the Shanghai Mega-Region. Innovations in Urban and Regional Systems 2020, 385 -416.
AMA StyleZhangqi Zhong, Jean-Claude Thill, Yi Sun, Zheng Wang. Carbon Emissions Embodied in Trade and Urban Regional Climate Policy-Making in the Shanghai Mega-Region. Innovations in Urban and Regional Systems. 2020; ():385-416.
Chicago/Turabian StyleZhangqi Zhong; Jean-Claude Thill; Yi Sun; Zheng Wang. 2020. "Carbon Emissions Embodied in Trade and Urban Regional Climate Policy-Making in the Shanghai Mega-Region." Innovations in Urban and Regional Systems , no. : 385-416.
One of the enduring issues of spatial origin-destination (OD) flow data analysis is the computational inefficiency or even the impossibility to handle large datasets. Despite the recent advancements in high performance computing (HPC) and the ready availability of powerful computing infrastructure, we argue that the best solutions are based on a thorough understanding of the fundamental properties of the data. This paper focuses on overcoming the computational challenge through data reduction that intelligently takes advantage of the heavy-tailed distributional property of most flow datasets. We specifically propose the classification technique of head/tail breaks to this end. We test this approach with representative algorithms from three common method families, namely flowAMOEBA from flow clustering, Louvain from network community detection, and PageRank from network centrality algorithms. A variety of flow datasets are adopted for the experiments, including inter-city travel flows, cellphone call flows, and synthetic flows. We propose a standard evaluation framework to evaluate the applicability of not only the selected three algorithms, but any given method in a systematic way. The results prove that head/tail breaks can significantly improve the computational capability and efficiency of flow data analyses while preserving result quality, on condition that the analysis emphasizes the “head” part of the dataset or the flows with high absolute values. We recommend considering this easy-to-implement data reduction technique before analyzing a large flow dataset.
Ran Tao; Zhaoya Gong; Qiwei Ma; Jean-Claude Thill. Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction. ISPRS International Journal of Geo-Information 2020, 9, 299 .
AMA StyleRan Tao, Zhaoya Gong, Qiwei Ma, Jean-Claude Thill. Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction. ISPRS International Journal of Geo-Information. 2020; 9 (5):299.
Chicago/Turabian StyleRan Tao; Zhaoya Gong; Qiwei Ma; Jean-Claude Thill. 2020. "Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction." ISPRS International Journal of Geo-Information 9, no. 5: 299.
The ease of mobility across the urban environment is known to be a major factor of the spatial organization of the city. It is commonplace to look at accessibility in this space as a structuring element of urban functional areas. In this tradition, the urban space is segmented into nodal regions or communities on the basis of trip origins and destinations recorded to uniformly sized grid cells or Voronoi polygons. However, this approach ignores the role of the layout of the transportation network in forming the regionalization of the urban structure. In this article, we argue that an effective approach to identify socioeconomic communities in an urban area is by means of its functionally critical elements. The proposed approach starts with the identification of functionally critical nodal points in the city’s transportation system, which allow us to capture people’s activity spaces on the aggregate. Then we construct a weighted directed graph based on these functionally critical locations, where each node in the graph denotes a functionally critical location and each edge denotes the presence of travel trajectories between pairs of critical locations; the weight of edges denotes the travel intensity. We introduce recent methods of network science to identify the socioeconomic communities of the urban region, and we examine and discuss interesting socioeconomic clusters. As a use case, we use a big data set that contains all the trajectories of over 11,000 taxis over a month in Wuhan, China. The results of the analysis suggest that (1) characteristics of socioeconomic clusters are very different from the administrative subdivisions of the city of Wuhan; (2) compared to regionalizations that account only for trip ends, the functional criticality approach provides us better ways to understand the regionalization structure of a city, especially how activity spaces are shaped by civil infrastructures such as bridges across major waterways; and (3) the functional criticality approach enhances urban communities outlined solely on the basis of physical transportation network topologies.
Yang Zhou; Jean-Claude Thill. Urban Nodal Regions Through Communities of Functionally Critical Locations in the Transportation Network. New Frontiers in Regional Science: Asian Perspectives 2020, 293 -311.
AMA StyleYang Zhou, Jean-Claude Thill. Urban Nodal Regions Through Communities of Functionally Critical Locations in the Transportation Network. New Frontiers in Regional Science: Asian Perspectives. 2020; ():293-311.
Chicago/Turabian StyleYang Zhou; Jean-Claude Thill. 2020. "Urban Nodal Regions Through Communities of Functionally Critical Locations in the Transportation Network." New Frontiers in Regional Science: Asian Perspectives , no. : 293-311.
Yasushi Asami; Jean-Claude Thill. Special feature section on spatial analysis and modeling: part II. Asia-Pacific Journal of Regional Science 2020, 4, 135 -138.
AMA StyleYasushi Asami, Jean-Claude Thill. Special feature section on spatial analysis and modeling: part II. Asia-Pacific Journal of Regional Science. 2020; 4 (1):135-138.
Chicago/Turabian StyleYasushi Asami; Jean-Claude Thill. 2020. "Special feature section on spatial analysis and modeling: part II." Asia-Pacific Journal of Regional Science 4, no. 1: 135-138.
Neighborhood development in the U.S. New South offers a rich historical diversity reflecting different growth patterns during their initial development and a longer history of African American residential experiences. Previous studies indicate certain neighborhood racial characteristics cancel conditions known to encourage gentrification, such as proximity to downtown. While recent gentrification literature has offered contradictory findings about the importance of neighborhoods with a majority African American population, a key aspect missing from this debate is how U.S. New South cities are set apart from other regions by displaying processes of industrialization, segregation, and immigration that form low-density spatial patterns of urbanization. To that end, Charlotte, NC is used as a case study to reveal how two types of African American neighborhoods near new growth centers, rim villages and streetcar suburbs, become gentrified. Our research reveals that while dynamics of economic change restructure neighborhoods in Charlotte, they also destabilize infrastructure that supports economically and socially struggling African American communities. As mid-sized U.S. New South cities continue to grow, our viewpoint argues that more thorough and geographically sensitive studies are needed to address localized impacts of gentrification on minority neighborhoods to form site specific anti-gentrification strategies.
Daniel Yonto; Jean-Claude Thill. Gentrification in the U.S. New South: Evidence from two types of African American communities in Charlotte. Cities 2019, 97, 102475 .
AMA StyleDaniel Yonto, Jean-Claude Thill. Gentrification in the U.S. New South: Evidence from two types of African American communities in Charlotte. Cities. 2019; 97 ():102475.
Chicago/Turabian StyleDaniel Yonto; Jean-Claude Thill. 2019. "Gentrification in the U.S. New South: Evidence from two types of African American communities in Charlotte." Cities 97, no. : 102475.
In this article, the socioeconomic determinants on urban population in China are empirically investigated with a theoretical equilibrium model for city size. While much of the research on urban size focuses on the impact of agglomeration economies based on “optimal city size” theory, this model is eschewed in our research due to its theoretical paradox in the real world, and we turn instead toward an intermediate solution proposed by Camagni, Capello, and Caragliu. This equilibrium model is estimated on a sample of 111 prefectural cities in China with multiple regression and artificial neural networks. Empirical results have shown that the model explains the variance in the data very well, and all the determinants have significant impacts on Chinese city sizes. Although sample cities have reached their equilibrium sizes as a whole, there is substantially unbalanced distribution of population within the urban system, with a strong contingent of cities that are either squarely too large or too small.
DaiDai Shen; Jean-Claude Thill; Jiuwen Sun. Are Chinese Cities Oversized? International Regional Science Review 2019, 43, 632 -654.
AMA StyleDaiDai Shen, Jean-Claude Thill, Jiuwen Sun. Are Chinese Cities Oversized? International Regional Science Review. 2019; 43 (6):632-654.
Chicago/Turabian StyleDaiDai Shen; Jean-Claude Thill; Jiuwen Sun. 2019. "Are Chinese Cities Oversized?" International Regional Science Review 43, no. 6: 632-654.
Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.
Daniel Yonto; L. Michele Issel; Jean-Claude Thill. Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments. Urban Science 2019, 3, 75 .
AMA StyleDaniel Yonto, L. Michele Issel, Jean-Claude Thill. Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments. Urban Science. 2019; 3 (3):75.
Chicago/Turabian StyleDaniel Yonto; L. Michele Issel; Jean-Claude Thill. 2019. "Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments." Urban Science 3, no. 3: 75.
Integrating both traditional and social media data, this study compares the performance of gravity, neural network, and random forest models of commuting trip distribution in New York City. Trip distribution modeling has primarily employed traditional data sources and classical methods such as the gravity. However, with the emergence of social media during the past decade, the potential for integrating traditional and social media data while utilizing new techniques has been identified. Our findings indicate that the random forest model outperforms the traditional gravity and neural network models. Population, distance, number of Twitter users, and employment were identified as the four most influential predictors of trip distibution by the random forest model. While Twitter flows did not enhance the models' performance, the importance of the number of Twitter users at work destinations implies the potential for using social media data in travel demand modeling to improve the predictive power and accuracy.
Nastaran Pourebrahim; Selima Sultana; Amirreza Niakanlahiji; Jean-Claude Thill. Trip distribution modeling with Twitter data. Computers, Environment and Urban Systems 2019, 77, 101354 .
AMA StyleNastaran Pourebrahim, Selima Sultana, Amirreza Niakanlahiji, Jean-Claude Thill. Trip distribution modeling with Twitter data. Computers, Environment and Urban Systems. 2019; 77 ():101354.
Chicago/Turabian StyleNastaran Pourebrahim; Selima Sultana; Amirreza Niakanlahiji; Jean-Claude Thill. 2019. "Trip distribution modeling with Twitter data." Computers, Environment and Urban Systems 77, no. : 101354.
Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.
Chunchun Hu; Jean-Claude Thill. Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories. ISPRS International Journal of Geo-Information 2019, 8, 295 .
AMA StyleChunchun Hu, Jean-Claude Thill. Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories. ISPRS International Journal of Geo-Information. 2019; 8 (7):295.
Chicago/Turabian StyleChunchun Hu; Jean-Claude Thill. 2019. "Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories." ISPRS International Journal of Geo-Information 8, no. 7: 295.
Research on the interaction amongst land use, transport, and the environment has a long history and can fill volumes of publications. Such research has contributed to urban and transportation planning and substantially reduced problems in transport, land use, and the environment. However, solving traditional problems caused by the spatial disorder of land use and transportation facilities still remains an elusive goal. For instance, we can easily find that severe traffic jams and high car dependencies cause various problems in many developed cities, while extensive suburbanization driven by high mobility has led to increased energy consumption and environmental impact. Similar problems have emerged in developing countries with a greater magnitude than in developed nations. Additionally, new problems related to sustainability, including social exclusion and climate change, are in effect around the world. The progress and emergence of new technologies in transportation, the potential of travel behavior monitoring, and the sensing of the earth surface are motivating the expansion of research topics and methodologies. The World Conference on Transport Research Society (WCTRS) is one of the oldest research communities studying land use/transport interactions. This virtual special issue consists of selected and fully reviewed papers presented at the 2016 World Conference on Transport Research (WCTR) in Shanghai. In this editorial, we place these published contributions and others made during the conference in the context of the broader literature, present an assessment of the progress of research in this domain, and finally propose an agenda for future research directions in the field of transport, land use, and environmental interactions.
Masanobu Kii; Rolf Moeckel; Jean-Claude Thill. Land use, transport, and environment interactions: WCTR 2016 contributions and future research directions. Computers, Environment and Urban Systems 2019, 77, 101335 .
AMA StyleMasanobu Kii, Rolf Moeckel, Jean-Claude Thill. Land use, transport, and environment interactions: WCTR 2016 contributions and future research directions. Computers, Environment and Urban Systems. 2019; 77 ():101335.
Chicago/Turabian StyleMasanobu Kii; Rolf Moeckel; Jean-Claude Thill. 2019. "Land use, transport, and environment interactions: WCTR 2016 contributions and future research directions." Computers, Environment and Urban Systems 77, no. : 101335.
Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping. Despite recent advances in flow data analytics, there is a lack of literature on detecting bivariate or multivariate spatial flow patterns. In this paper we introduce a new spatial statistical method called Flow Cross K-function, which combines the Cross K-function that detects marked point patterns and the Flow K-function that detects univariate flow clustering patterns. Flow Cross K-function specifically assesses spatial dependence of two types of flow events, in other words, whether one type of flows is spatially associated with the other, and if so, whether this is according to a clustering or dispersion trend. Both a global version and a local version of Flow Cross K-function are developed. The former measures the overall bivariate flow patterns in the study area, while the latter can identify anomalies at local scales that may not follow the global trend. We test our method with carefully designed synthetic data that simulate the extreme situations. We exemplify the usefulness of this method with an empirical study that examines the distributions of taxi trip flows in New York City.
Ran Tao; Jean-Claude Thill. Flow Cross K-function: a bivariate flow analytical method. International Journal of Geographical Information Science 2019, 33, 2055 -2071.
AMA StyleRan Tao, Jean-Claude Thill. Flow Cross K-function: a bivariate flow analytical method. International Journal of Geographical Information Science. 2019; 33 (10):2055-2071.
Chicago/Turabian StyleRan Tao; Jean-Claude Thill. 2019. "Flow Cross K-function: a bivariate flow analytical method." International Journal of Geographical Information Science 33, no. 10: 2055-2071.