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The objective of this study is to understand the spatial impact of the pandemic on various kinds of food retailers with a focus on population change. To achieve this goal, we examined the spatial similarity between the distribution of food retail outlets and that of the space-time population before and during the COVID-19 pandemic in a central area of Yokohama, Japan. The similarity was measured by using the kernel density estimation on a street network and the Kullback–Leibler divergence. The results showed that the impact of business closures varied by the type of retail outlets.

Additional file 1: Table S1. Best-fit model posterior estimates of key epidemiological parameters for January 6 to March 9, 2020. Table S2. Gamma distributions for monthly reporting delay (Dr). Table S3. Comparison of the results of DIC based on different spatial weight matrices. Table S4. The contribution of direct effects and SSE of all NPIs on COVID-19 cases in 48 regions and Washington D.C. Table S5. The coefficients of different NPI estimates under the dependent variable multiplied by 100. Table S6. The coefficients estimate of direct effects under the dependent variable multiplied by 100. Table S7. The coefficients estimate of spatial spillover effects under the dependent variable multiplied by 100. Table S8. The coefficients estimate of direct effects from spatial panel durbin model. Table S9. The coefficients estimate of spatial spillover effects from spatial panel durbin model. Table S10. The coefficient estimated by our spatial panel model with different weight matrix during Phase A. Table S11. The coefficient estimated by our spatial panel model with different weight matrix during Phase B. Figure S1. The connectivity structure of different spatial weight matrix. Figure S2. Comparison of the change of human mobility estimated by our spatial panel model versus the factual human mobility change of 48 states and Washington D.C. Figure S3. The scatterplot of the change of human mobility estimated by our spatial panel model versus the factual human mobility change in all states. Figure S4. The direct and spatial spillover effect estimates of NPIs in 48 states and Washington D.C. from the spatial panel model. Figure S5. Model fitting to daily case numbers (blue dots) by S-SEIR model in 48 states and Washington D.C. Figure S6. The estimated cases by different model in 48 states (regions) and Washington D.C. Notes: S-SEIR (Direct effects): similar to the S-SEIR model, but only the direct effects of NPIs are considered. S-SEIR (SSE): similar to the S-SEIR model, but only the spatial spillover effects of NPIs are considered. S-SEIR: both the direct and spatial spillover effects are considered. The line and shaded area represent the median and 95% CI, respectively. Figure S7. The estimated national cumulative confirmed cases by changing the intensity of all NPIs in different regions. Figure S8. The national cumulative confirmed cases estimated by changing the intensity of diffferent NPIs in different regions. Figure S9. The estimated national cumulative confirmed cases by restricting the flows of r2, r4, or r5, including all travel flow with these targeted regions (denoted as ’OD’), travel flow from these targeted regions (denoted as ’O’) and travel flow to these targeted regions (denoted as ‘D’). Figure S10. The median differences in national cumulative confirmed cases at t = 30 week are estimated by the complete lockdown (OD), origin-based lockdown (O), destination-based lockdown (D) in r4 or r5 and baseline scenario. Figure S11. The change of intrastate human mobility in each state. Figure S12. The distribution of the dependent variables in different weeks. Figure S13. Estimation results of the change of intrastate human mobility and spatial spillover effects of NPIs by panel SAR model. The matrix during introduction phase is flow weight (8 neighbors) matrix, during lifting phase is the inverse distance matrix with a cut-off distance of 600 km. Figure S14. Estimation results of the change of intrastate human mobility and spatial spillover effects of NPIs by panel SAR model. The matrix during introduction phase is the inverse distance matrix with a cut-off distance of 600 km, and during lifting phase is flow weight (8 neighbors). Figure S15. Estimation results of the change of intrastate human mobility and spatial spillover effects of NPIs by panel SAR model. The matrix during introduction phase is the inverse distance matrix with a cut-off distance of 600 km, and during lifting phase is the inverse distance (the max distance is 800 km). Figure S16. Estimation results from the S-SEIR model, taking changes in actual human mobility as intervention effects. Figure S17. Estimation results from the Sp-SEIR model, the initial seed is the number of cases in the five days from T0 to T0+4. Figure S18. Estimation results from the Sp-SEIR model. During initialization, symptomatic patients are not assigned an initial value. Figure S19. Estimation results from the Sp-SEIR model, the range of movement fixed factor (θ) is from 1 to 10. Figure S20. Estimation results from the Sp-SEIR model. We assume that the infection of asymptomatic patients relative to symptomatic patients also varies over time and region.

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences.

The datasets simulate 100 points of sensitive locations that need to be geomasked and 1,000 points of fictitious residential locations. The simulations are conducted within a hypothetical study area with a size of 5 km by 4 km. Three spatial patterns are generated seperately for the sensitive and residential locations (random patterns for Cul-de-sacs; regular pattern for the conventional grid; clustered pattern to simulate the residential groups separated by parks and other landuse types).

Traditionally urban structure and development are monitored using infrequent high-quality datasets such as censuses. However, human culture is accelerating and aggregating, leading to ever-larger cities and an increased pace of urban development. Our modern interconnected world also provides us with new data sources that can be leveraged in the study of cities. However, these often noisy and unstructured sources of big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which is produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing for example the emergence and absorption of subcenters. In the future this method will allow the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency.

This paper examines the neighborhood effect averaging problem (NEAP) in the evaluation of sociodemographic disparities in people’s air pollution exposures in Los Angeles using GIScience methods and activity-travel diary data of 3790 individuals. Spatial regression model results indicate that assessments of sociodemographic disparities in people’s air pollution exposures are erroneous when people’s daily mobility is ignored because of the different manifestations of neighborhood effect averaging for different social/racial groups. Therefore, to avoid erroneous conclusions in environmental inequality research, it would be critical to consider the NEAP in studies of

Additional file 1: Table S1. Distribution of type of physical activities by the time of COVID-19 outbreak among 242 Hong Kong older men. Table S2. Association between period of COVID-19 outbreak and physical activity levels according to sum of vector magnitudes.

Additional file 1: Table S1. Measures, data sources, scoring, and reliability of park environmental characteristics, moderator, park-based MVPA, and covariates.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2–3 years from its release.

With the completion of the Beijing Five-year Clean Air Action Plan by the end of 2017, the annual mean PM2.5 concentration in Beijing dropped dramatically to 58.0 µg m−3 in 2017 from 89.5 µg m−3 in 2013. However, controversies exist to argue that favourable meteorological conditions in 2017 were the major driver for such a rapid decrease in PM2.5 concentrations. To comprehensively evaluate this 5-year plan, we employed a Kolmogorov–Zurbenko (KZ) filter and WRF-CMAQ (Weather Research and Forecasting and the Community Multi-scale Air Quality) to quantify the relative contribution of meteorological conditions and the control of anthropogenic emissions to PM2.5 reduction in Beijing from 2013 to 2017. For these 5 years, the relative contribution of emission reduction to the decrease in PM2.5 concentrations calculated by KZ filtering and WRF-CMAQ was 80.6 % and 78.6 % respectively. KZ filtering suggested that short-term variations in meteorological and emission conditions contributed majorly to rapid changes in PM2.5 concentrations in Beijing. WRF-CMAQ revealed that the relative contribution of local and regional emission reduction to the PM2.5 decrease in Beijing was 53.7 % and 24.9 % respectively. For local emission-reduction measures, the regulation of coal boilers, increasing use of clean fuels for residential use and industrial restructuring contributed 20.1 %, 17.4 % and 10.8 % to PM2.5 reduction respectively. Both models suggested that the control of anthropogenic emissions accounted for around 80 % of the PM2.5 reduction in Beijing, indicating that emission reduction was crucial for air quality enhancement in Beijing from 2013 to 2017. Consequently, such a long-term air quality clean plan should be continued in the following years to further reduce PM2.5 concentrations in Beijing.

To comprehensively evaluate the effects of the recent “2+26” regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentration 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentration 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly, and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contribution of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 %, respectively, indicating that the recent “Coal to Gas” project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing–Tianjin–Hebei region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 %, respectively. The relative contribution of local emissions to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intentionally reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

To comprehensively evaluate the effects of the recent 2+26 regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentrations of 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentrations of 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contributions of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 % respectively, indicating that the recent Coal to Gas project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing-Tianjin-Hebei Region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 % respectively. The relative contribution of local emission to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intendedly reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general.

The Geoinformatics conference series was initiated by the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS) in 1992. This international conference series has provided a unique forum for exchanging novel ideas and cutting-edge knowledge on geographic information sciences and technologies among GIS professionals worldwide. The 23th International Conference on Geoinformatics (Geoinformatics 2015) will be held on June 19–21, 2015 at the China University of Geosciences, Wuhan, Hubei (China). The conference is co-organized by CPGIS and the China University of Geosciences (CUG).

The objective of the paper is to discuss the development of an alternative representation of the transportation network using object-oriented GIS. This representation is important for the supply side of transportation planning and modeling. Object-orientation provides a way of solving the problem in a planar network for routing. It can facilitate the calculation of detailed network characteristics using properties such as inheritance and polymorphism. This representation is also closer to human perception of a transportation network. It is argued that by using an object-oriented GIS we can facilitate path selection using different criteria. We experiment with the design of the object-oriented system by developing an object-oriented representation of a transportation network and incorporating different path selection algorithms based on various behavioral assumptions. It is especially useful in the design for a versatile ATIS.

The successful development of Intelligent Transportation Systems (ITS) depends on the capability of incorporating a vast amount of information about the location of facilities which generate travel as well as a realistic representation of elements of the transportation network in which travel occurs. An integral part of this system is an Advanced Traveler Information System (ATIS). Such a system can be based on an innovative and comprehensive Geographic Information System (GIS). Whereas current ITS primarily use simplified transportation networks as their basis, using an object-oriented GIS allows us to provide a more realistic representation of elements of the network and the ways that people perceive them. We can represent the network by defining roads or street hierarchies and by storing environmental data as layers which can be overlain, aggregated, or decomposed at will. Storing the transportation network as a hierarchy facilitates the calculation of different paths through the network and allows the introduction of different path selection criteria. A long-run aim of ITS is to develop a real time multi-strategy travel decision support system over a multi-modal network. We examine the advantages of an object-oriented system over the link-node system in pursing such a goal. We also identify, the shortcomings of link-node technology that are overcome by using an object-oriented data model. And finally, we discuss some of the theoretical and applied implications of our suggestions.

In this paper we review attempts to develop CPM of individual travel behavior. CPM represent a linked set of computer programs together with appropriate databases which are designed to capture the essence of human decision making in different spatial situations. Used primarily for wayfinding and to simulate and predict travel behavior, CPMs bypass the difficult problems of IIA typically attributed to discrete choice models. They also allow greater emphasis on the cognitive components of decision making including cognitive maps, preferences, and departure from utility maximizing and linearity in the considerations of alternative paths and alternative destinations. The CPM illustrated herein focuses on multiperson households and models travel behavior before and after telecommuting takes place in the household° Sets of feasible alternative destinations for travel purposes are derived using GIS procedures such as buffering and path selection. Shortcomings and possible future developments of such models are then discussed.

The objective of this paper is to review object-oriented (OO) approaches to data modeling and data handling and their usefulness in transportation planning and modeling in general and Intelligent Transportation Systems in particular. The paper begins with a discussion of the current GIS data model for representing a transportation network and the most common database management systems used in the context of transportation planning. We then discuss object-orientation, its different properties and its usefulness in representing a multi-modal, multi-scale network with hierarchical road types. We also discuss alternative database management schemes. Finally we review some existing systems and discuss the implications of adopting an object-oriented perspective.

Intelligent Vehicle Highway Systems (IVHS), which aim at the utilization of advanced information processing and communication technologies for improving travel efficiency and safety, have become an important policy measure in recent years. One of their major components, Advanced Traveler Information Systems (ATIS), has been developed specifically to assist drivers in trip planning and decision making on destination selection, departure time, route choices and congestion avoidance.

Transportation planning has been turning away from the solutions of building highways and transit routes to changing people's travel choices and making more efficient use of existing facilities. With the recent research focus on Advanced Traveler Information Systems (ATIS), it is imperative to understand the travel behavior of people and the information required to change their travel choices.

Household travel behavior entails interdependent deliberate decisions, as well as the execution of routines not preceded by deliberate decisions. Furthermore, travel decisions are dependent on choices to participate in activities. Because of the complexity of the decision-making process in which individuals are engaged, computational-process models (CPMs) are promising means of implementing behavioral principles which unlike other disaggregate modelling approaches do not rely on a utility-maximizing framework. A conceptual framework is proposed as the basis of a CPM interfaced with the geographical information system ARC/INFO. How to model households' travel behavior is illustrated in a case study of a single household in which one member started telecommuting.

Travel behavior entails several interrelated decisions made by people, as well as the execution of routines not preceded by deliberate decisions. Furthermore, travel decisions are dependent on choices to participate in activities. A conceptual framework is proposed as the basis of a computational-process model (CPM). Because of the complexity of the decision-making process in which individuals are engaged, CPMs are promising alternatives to disaggregate discrete choice modelling with its limited ability to account for interrelated decisions and its reliance on an unrealistic utility-maximizing framework. Empirical support for the proposed conceptual framework is presented in the paper from case studies of telecommuting households in Sacramento, CA. The value of geographical information systems (GIS) in these empirical tests is demonstrated.

Travel behavior entails several interrelated decisions made by people, as well as the execution of routines not preceded by deliberate decisions. Furthermore, travel decisions are dependent on choices to participate in activities. After a brief review of research aiming at describing activity/travel patterns and approaches in which activity/travel decisions are modelled by means of discrete-choice modelling techniques, a conceptual framework is proposed as a background to an evaluation of several computational-process models. Further needed developments of these models are discussed, as well as the use of geographical information systems in their operationalization and application to traffic planning.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2-3 years from its release.

Urban mobility and transport of people has been rising constantly for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as main contributor of greenhouse gas emissions and traffic jams. In the future an increasing number of people will be living in large urban settings and therefore these problems must be solved to assure livable environments. The rapid progress of Information and Communication, and Geographic Information Technologies has paved the way for Urban Informatics and Smart Cities, which allow for both large-scale urban analytics as well as supporting people in their complex mobile decision-making. This chapter demonstrates how Geosmartness, a combination of novel spatial data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as investigate people’s mobile decision-making. Mobility pattern analysis is necessary for evaluating real-time situations but also for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples will cover movement data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction., The Urban Book Series, ISSN:2365-757X, Urban Informatics, ISBN:978-981-15-8983-6, ISBN:978-981-15-8982-9

Visual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.

International audience; Cartographic generalization seeks to summarize geographical information from a geo-database to produce a less detailed and readable map. The specifications of a legible map are translated into a set of constraints to guide the generalization process and evaluate it. The global evaluation of the map, or of a part of it, consisting in aggregating all the single constraints satisfactions, is still to tackle for the generalization community. This paper deals with the use of the social welfare theory to handle the aggregation of the single satisfactions on the map level. The social welfare theory deals with the evaluation of the economical global welfare of a society, based on the individual welfare. Different social welfare orderings are adapted to generalization, compared and some are chosen for several generalization use cases. Experiments with topographic maps are carried out to validate the choices.