International Journal of Geo-Information

Online ISSN: 2220-9964
Publications
Article
Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.
 
The cartogram showing the spatial distribution of global OSM elements for 
individual countries: the lager the size, the more the OSM elements.
The co-contribution network for the top five hierarchical levels involving 477 nodes and 80,957 edges. The scaling hierarchy of far more small nodes than larger ones is indicated by the size of red dots.
Article
OpenStreetMap (OSM) constitutes an unprecedented, free, geographic information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements, and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power-law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavily edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration. Keywords: OpenStreetMap, big data, power laws, head/tail breaks, ht-index
 
Abduction in GIS. Source: [Bhatt, 2012]
Branching / Hypothetical Situation Space. Source: [Bhatt, 2012]
An Urban Narrative 
Article
The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systems
 
Article
This study analyses the 60 years land cover and land use changes and the implications on environmental change of the Koga catchment located in North Western Ethiopia. The data used include 1:50,000 scale aerial photographs, Landsat MSS, TM and ETM images, and ASTER images together with ground truth data collected through fieldwork survey and community elders' interview. Historical aerial photographs are an important source of data for long term land cover change analysis and have high spatial resolution for detailed land use and land cover classification, though do not provide such good spectral resolution as satellite images. Many land use land cover change studies are based on comparing the changes generated from data with different spatial scales and resolutions which makes the comparison difficult. This study applied image fusion techniques to bring the data sources in to a relatively similar scale for better land use and land cover classification. This bridged the gap of the different spatial scales of the different data sources and also produced images with relatively better spectral resolution than the aerial photographs and better spatial resolution for some of the satellite images for improved land use and land cover change detection. It has been discussed by different researchers that land use and land cover change is increasingly being recognized as an important driver of environmental change in all spatial and temporal scales and current rates, extents and intensities of changes far greater than ever in history. This is especially true in the African context due to over dependence on primary resources. This study quantified the land use land cover changes and analyzed the implications on environmental change.
 
Comparison of the ZY3-02 DSM and SRTMGL1 at the locations of the GCPs.
Article
Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.
 
Article
Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient () and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654, 679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551, 1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654, 679) and V-RR-DSI(551, 1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.
 
Article
Agenda 2030 pursues a universal approach and identifies countries in the Global South and in the Global North that are in need of transformation toward sustainability. Therefore, countries of the Global North such as Germany have signed the commitment to implement the Sustainable Development Goals (SDGs). However, the SDGs need to be “translated” to the specific national context. Existing sustainability indicators and monitoring and reporting systems need to be adjusted as well. Our paper evaluates how three different initiatives translated SDG 11 (“Make cities and human settlements inclusive, safe, resilient, and sustainable”) to the German context, given the specific role of cities in contributing to sustainable development. These initiatives included the official ‘National Sustainable Development Strategy’ of the German Government, a scientific initiative led by the ‘German Institute for Urban Affairs’, and a project carried out by the ‘Open Knowledge Foundation’, a non-governmental organization (NGO). This article aims to analyze how global goals addressing urban developments are contextualized on a national level. Our findings demonstrate that only a few of the original targets and indicators for SDG 11 are used in the German context; thus, major adjustments have been made according to the main sustainability challenges identified for Germany. Furthermore, our results show that the current contextualization of SDG 11 and sustainable urban development in Germany are still ongoing, and more changes and commitments need to be made.
 
Article
Despite the worldwide studies on urban agglomeration (UA), the effects of intra-UA interaction patterns have not been thoroughly elucidated to date. To fill the research gap, first, this study utilized the Baidu Internet search data to quantify the internal interaction patterns of 11 main UAs in China. Rail-way data were referenced for verification. Based on building intercity interaction network, the node symmetry index (NSI) was calculated. Considering the estimated interaction strength and mutuality, the intra-interaction patterns were classified into symmetrical and asymmetrical mutualism, where the former indicates that the interactions of cities are mutually beneficial and the latter means that the interactions are unbalanced. The socio-economic development levels of cities and UAs were estimated by the entropy-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. Finally, the impacts of intra-UA interaction were explored through ordinary least square regression. This study obtained two findings. Firstly, at the city scale, symmetrical mutualism had a greater impact than asymmetrical mutualism on the city’s socio-economic development level. Secondly, at the regional scale, both symmetrical and asymmetrical mutualism were related with regional socioeconomic development level; however, only symmetrical mutualism showed a correlation with regional coordinated development level. Respondent suggestions and implications to promote regional coordinated development were then offered based on the results of the analysis. Limitations of this study include that exogenous interactions between UAs and their backlands, and other relationships, such as competition, were not discussed. These issues can be considered in future researches. This study characterizes the interaction pattern of intra-urban agglomeration and offers advice and suggestion for implementing regional sustainable development.
 
Article
Indicator 11.3.1 of the UN Sustainable Development Goals (SDG 11.3.1) was designed to test land-use efficiency, which was defined as the ratio of the land consumption rate (LCR) to the population growth rate (PGR), namely, LCRPGR. This study calculates the PGRs, LCRs, and LCRPGRs for 333 cities from 1990–2000 and 391 cities from 2000–2015 in four geographical divisions in Eurasia according to the method given by UN metadata. The results indicate that Europe and Japan have the lowest PGR and LCR, indicating that this region’s level of urbanization is the highest. South and Central Asia have the lowest values of LCRPGR, indicating relatively lower urban land supply during the measurement periods. Compared with the mean LCRPGR in a region, the average values from SDG 11.3.1 by different types of cities in a region can have more guiding significance for urban sustainable development. While paying attention to the urban land-use efficiency of mega and extra-large cities, more attention should be paid to the coordination relationship between urban land supply and population growth in large, medium, and small cities. Additionally, the method from UN metadata works well for most urban expansion cities but is not suitable for cities with small changes in urban populations.
 
Article
Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities.
 
Article
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
 
Article
The rapid development of the Chinese economy has stimulated consumer demand and brought huge opportunities for the retail industry. Previous studies have emphasized the importance of estimating regional consumption potentiality. However, the determinants of retail sales are yet to be systematically studied, especially at the micro level. As a result, the realization of sustainable development goals in the retail industry is restricted. In this paper, we studied the determinants of retail sales from two aspects—location-based socioeconomic factors and spatial competition between shops. Using 12,500 retail shops as our sample and by adopting a grid-division strategy, we found that regional retail sales can be positively impacted by nearby population, road length, and most non-commercial points of interest (POIs). By contrast, the number of other commercial facilities, such as catering facilities and shopping malls, and the area of geographic barriers often caused negative impacts on retail sales. As to the competition effects, we found that the isolation and decentralization of shops in one area have a marginally positive effect on sales performance within a threshold distance of 226.19 m for a central grid and a threshold distance of 514.85 m for surrounding grids, respectively. This study explores the determinants of micro-level retail sales and provides decision makers with practical and realistic approaches for generating better site selection and marketing strategies, thus realizing the sustainable development goals of the retail industry.
 
Mezzanine ceiling. Authors' own photograph, 2020.
Detail of the scanner station positions within the mezzanine room.
Manual recording of two scans with indication of the fitting error.
Ceiling orthoimage with the tilted side planes laid flat.
Article
Suitable graphic documentation is essential to ascertain and conserve architectural heritage. For the first time, accurate digital images are provided of a 16th-century wooden ceiling, composed of geometric interlacing patterns, in the Pinelo Palace in Seville. Today, this ceiling suffers from significant deformation. Although there are many publications on the digital documentation of architectural heritage, no graphic studies on this type of deformed ceilings have been presented. This study starts by providing data on the palace history concerning the design of geometric interlacing patterns in carpentry according to the 1633 book by López de Arenas, and on the ceiling consolidation in the 20th century. Images were then obtained using two complementary procedures: from a 3D laser scanner, which offers metric data on deformations; and from photogrammetry, which facilitates the visualisation of details. In this way, this type of heritage is documented in an innovative graphic approach, which is essential for its conservation and/or restoration with scientific foundations and also to disseminate a reliable digital image of the most beautiful ceiling of this Renaissance palace in southern Europe.
 
Article
The history of modern maps in Japan begins with the Japan maps (called INŌ’s maps) prepared by Tadataka Inō after he thoroughly surveyed the whole of Japan around 200 years ago. The purpose of this study was to investigate the precision degree of INŌ’s Tokyo map by overlaying it with present maps and analyzing the map style (map projection, map scale, etc.). Specifically, we quantitatively examined the spatial distortion of INŌ’s maps through comparisons with the present map using GIS (geographic information system), a spatial analysis tool. Furthermore, by examining various factors that caused the positional gap and distortion of features, we explored the actual situation of surveying in that age from a geographical viewpoint. As a result of the analysis, a particular spatial regularity was confirmed in the positional gaps with the present map. We found that INŌ’s Tokyo map had considerably high precision. The causes of positional gaps from the present map were related not only to natural conditions, such as areas and land but also to social and cultural phenomena.
 
Number of gaps and their areas in the non-surveyed (estimated) coastlines in Japan.
Distance of non-surveyed (estimated) coastlines drawn by sea-based measurement in Ja- pan.
Article
The history of modern maps in Japan began with Inoh’s map that was made by surveying the whole of Japan on foot 200 years ago. Inoh’s team investigated coastlines, major roads, and geographical features such as rivers, lakes, temples, forts, village names, etc. The survey was successively conducted ten times from 1800 to 1816. Inoh’s map is known as the first scientific map in Japan using a systematic method. However, the actual survey was conducted only for 75% of the coastlines in Japan and the remaining 25% was drawn by Inoh’s estimation (observation). This study investigated how the non-surveyed (estimated) coastlines were distributed in the map and why the actual survey was not conducted in these non-surveyed coastlines. Using GIS, we overlaid the geometrically corrected Inoh’s map (Digital Inoh’s Map Professional Edition) with the current map published by the Geospatial Information Authority (GSI) of Japan for examining the spatial difference. We found that the non-surveyed coastlines were in places where the practice of actual surveying was topographically difficult because of the limited surveying technology of those days. The analytical result shows that 38.6% of the non-surveyed coastlines were cliffs, 25.7% were rocky beaches, and 6.2% were wetlands and tidal lands (including rice fields and tidal flats).
 
Article
The work of Philibert Girault de Prangey, who was a draughtsman, pioneering photographer and an Islamic architecture scholar, has been the subject of recent exhibitions in his hometown (Langres, 2019), at the Metropolitan Museum (New York, 2019) and at the Musée d’Orsay (Paris, 2020). After visiting Andalusia between 1832 and 1833, Prangey completed the publication “Monuments arabes et moresques de Cordoue, Seville et Grenada in 1839”, based on his own drawings and measurements. For the first time, this research analyses his interior perspectives of the Mosque-Cathedral of Cordoba (Spain). The novel methodology is based on its comparison with a digital model derived from the point cloud captured by a 3D laser scanner. After locating the different viewpoints, the geometric precision and the elaboration process are analysed, taking into account historic images by various authors, other details published by Prangey and the architectural transformations of the building. In this way, the veracity and documentary interest of some beautiful perspectives of a monument inscribed on the World Heritage List by UNESCO is valued.
 
Article
The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality.
 
Article
As the threat of COVID-19 increases, many countries have carried out various non-pharmaceutical interventions. Although many studies have evaluated the impact of these interventions, there is a lack of mapping between model parameters and actual geographic areas. In this study, a non-pharmaceutical intervention model of COVID-19 based on a discrete grid is proposed from the perspective of geography. This model can provide more direct and effective information for the formulation of prevention and control policies. First, a multi-level grid was introduced to divide the geographical space, and the properties of the grid boundary were used to describe the quarantine status and intensity in these different spaces; this was also combined with the model of hospital isolation and self-protection. Then, a process for the spatiotemporal evolution of the early COVID-19 spread is proposed that integrated the characteristics of residents’ daily activities. Finally, the effect of the interventions was quantitatively analyzed by the dynamic transmission model of COVID-19. The results showed that quarantining is the most effective intervention, especially for infectious diseases with a high infectivity. The introduction of a quarantine could effectively reduce the number of infected humans, advance the peak of the maximum infected number of people, and shorten the duration of the pandemic. However, quarantines only function properly when employed at sufficient intensity; hospital isolation and self-protection measures can effectively slow the spread of COVID-19, thus providing more time for the relevant departments to prepare, but an outbreak will occur again when the hospital reaches full capacity. Moreover, medical resources should be concentrated in places where there is the most urgent need under a strict quarantine measure.
 
Article
In order to understand how related research are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with the extracted information, the research evolution was analyzed from the temporal, spatial, and topic perspectives. From a temporal view, in the three months after the emergence of COVID-19, the number of published papers showed an obvious growth trend, and it showed a relatively stable cyclical trend in the later period, which is basically consistent with the development of COVID-19. Spatially, most of the authors who participated in related research are concentrated in the United States, China, Italy, the United Kingdom, Spain, India, and France. At the same time, with the continuous spread of COVID-19 in the world, the distribution of the number of authors has gradually expanded, showing to be correlated with the severity of COVID-19 at a spatial scale. From the perspective of topic, the early stage of COVID-19 emergence, the related research mainly focused on the origin and gene identification of the virus. After the emergence of the pandemic, studies related to the diagnosis and analysis of psychological health, personal security, and violent conflict are added. Meanwhile, some categories are most closely related to the control and prevention of the epidemic, such as pathology analysis, diagnosis, and treatment; epidemic situation and coping strategies; and prediction and assessment of epidemic situation. In most time periods, the majority of studies focused on these three categories.
 
Global spatial autocorrelation of the global COVID-19 pandemic over time.
Cold and hot spot patterns of confirmed COVID-19 cases.
The definitions of eight hot (cold) spot patterns.
Geographic centroid migration distance for the COVID-19 pandemic on six continents.
Proportions of cold and hot spot patterns globally for confirmed COVID-19 cases.
Article
Unlike previous regionalized studies on a worldwide crisis, this study aims to analyze spatial distribution patterns and evolution characteristics of the COVID-19 pandemic, using space-time aggregation and spatial statistics from a global perspective. Hence, various spatial statistical methods, such as the heat map, global Moran’s I, geographic mean center, and emerging hot spot analysis were utilized comprehensively to mine and analyze spatiotemporal evolution patterns. The main findings were as follows: Overall, the spatial autocorrelation of confirmed cases gradually increased from the initial outbreak until September 2020 and then decreased slightly. The geographic centroid migration ranges of the pandemic in Asia, Europe, and Africa are wider than those in South America, Oceania, and North America. The spatiotemporal evolution pattern of the global pandemic mainly consisted of oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. This study provides auxiliary decision-making information for pandemic prevention and control.
 
The designed integrated architecture for complex event detection (DB: DataBase).
Implementation diagram of the developed architecture (IoT: internet of things, STA: SensorThing API, MQTT: message queuing telemetry transport, AWS: amazon web services).
Proposed data model based on SensorThings API.
Sample spatial/temporal relationships and queries.
YOLO and Mask-RCNN comparison results.
Article
Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models.
 
Article
The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.
 
Article
During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.
 
Sweden and its COVID-19 case profile through the seasons (2020).
COVID-19 weekly reported cases within the four seasons. (A) shows the general trends for all the seasons; (B) shows the trend in Spring; (C) shows the trend in Summer; (D) shows the trend in Fall; and (E) shows the trend in Winter.
Article
While COVID-19 is a global pandemic, different countries have experienced different morbidity and mortality patterns. We employ retrospective and prospective space–time permutation analysis on COVID-19 positive records across different municipalities in Sweden from March 2020 to February 2021, using data provided by the Swedish Public Health Agency. To the best of our knowledge, this is the first study analyzing nationwide COVID-19 space–time clustering in Sweden, on a season-to-season basis. Our results show that different municipalities within Sweden experienced varying extents of season-dependent COVID-19 clustering in both the spatial and temporal dimensions. The reasons for the observed differences could be related to the differences in the earlier exposures to the virus, the strictness of the social restrictions, testing capabilities and preparedness. By profiling COVID-19 space–time clusters before the introduction of vaccines, this study contributes to public health efforts aimed at containing the virus by providing plausible evidence in evaluating which epidemiologic interventions in the different regions could have worked and what could have not worked.
 
Article
Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.
 
Adjusted R 2 time-serial sliding. Note: Adj.R 2 m represents the adjusted R square after MGWR regression; Adj.R 2 t represents the adjusted R square after GTWR regression; Adj.R 2 o represents the adjusted R square after Global regression.
Time-serial correlation between immigration and COVID-19 confirmed cases: (a) the correlation result; (b) the corresponding significance. Notes: D2 refers to the second day of January, 2020 and the labels can be explained similarly; Z1-Z17 refer to the 17 prefecture-level cities in Table 2; The different colors in Figure 6a, b reveal the degree of correlation at different spatio-temporal points and the significance of each corresponding point.
Summaries of OLS, GTWR and MGWR models.
Article
Clarifying the regional transmission mechanism of COVID-19 has practical significance for effective protection. Taking 103 county-level regions of Hubei Province as an example, and taking the fastest-spreading stage of COVID-19, which lasted from 29 January 2020, to 29 February 2020, as the research period, we systematically analyzed the population migration, spatio-temporal variation pattern of COVID-19, with emphasis on the spatio-temporal differences and scale effects of related factors by using the daily sliding, time-ordered data analysis method, combined with extended geographically weighted regression (GWR). The results state that: Population migration plays a two-way role in COVID-19 variation. The emigrants’ and immigrants’ population of Wuhan city accounted for 3.70% and 73.05% of the total migrants’ population respectively; the restriction measures were not only effective in controlling the emigrants, but also effective in preventing immigrants. COVID-19 has significant spatial autocorrelation, and spatio-temporal differentiation has an effect on COVID-19. Different factors have different degrees of effect on COVID-19, and similar factors show different scale effects. Generally, the pattern of spatial differentiation is a transitional pattern of parallel bands from east to west, and also an epitaxial radiation pattern centered in the Wuhan 1 + 8 urban circle. This paper is helpful to understand the spatio-temporal evolution of COVID-19 in Hubei Province, so as to provide a reference for similar epidemic prevention.
 
Article
Exploring the spatial patterns of COVID-19 transmission and its key determinants could provide a deeper understanding of the evolution of the COVID-19 pandemic. The goal of this study is to investigate the spatial patterns of COVID-19 transmission in different periods in Singapore, as well as their relationship with demographic and built-environment factors. Based on reported cases from 23 January to 30 September 2020, we divided the research time into six phases and used spatial autocorrelation analysis, the ordinary least squares (OLS) model, the multiscale geographically weighted regression (MGWR) model, and dominance analysis to explore the spatial patterns and influencing factors in each phase. The results showed that the spatial patterns of COVID-19 cases differed across time, and imported cases presented a random pattern, whereas local cases presented a clustered pattern. Among the selected variables, the supermarket density, elderly population density, hotel density, business land proportion, and park density may be particular fitting indicators explaining the different phases of pandemic development in Singapore. Furthermore, the associations between determinants and COVID-19 transmission changed dynamically over time. This study provides policymakers with valuable information for developing targeted interventions for certain areas and periods.
 
Article
The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.
 
Article
Indoor navigation has become more important these days due to the current situation worldwide in the aftermath of the outbreak of the COVID-19 pandemic, posing an unparalleled threat amounting to a humanitarian crisis on a global scale. Indoor navigation employs a variety of technologies, including Wi-Fi, Bluetooth, and RFID. Support for these technologies requires accurate information and appropriate processing and modeling to help and direct users of the optimal route to desired destinations and to monitor crowd density in order to maintain social distancing. This research will present a semantic indoor ontology model for indoor navigation and the reduction of human density in indoor space to ensure social distancing and prevent transmission. The proposed system is based on semantic representations of the components of navigation paths which, in turn, enable reasoning functionality. Despite the system’s complexity, the evaluation revealed that it functions well.
 
Article
At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which greatly increased the infection risk of COVID-19 across the country. Financially supported by the Wuhan government, and based on cellphone signaling data from Unicom (a mobile phone carrier) and Baidu location-based data, this paper analyzed the effects that city dwellers, non-commuters, commuters, and people seeking medical services had on the transmission risk of COVID-19 in the early days of the pandemic in Wuhan. The paper also evaluated the effects of the city lockdown policy on the spread of the pandemic outside and inside Wuhan. The results show that although the daily business activities in the South China Seafood Wholesale Market and nearby commuters’ travel behaviors concentrated in the Hankou area, a certain proportion of these people were distributed in the Wuchang and Hanyang areas. The areas with relatively high infection risks of COVID-19 were scattered across Wuhan during the early outbreak of the pandemic. The lockdown in Wuhan closed the passageways of external transport at the very beginning, largely decreasing migrant population and effectively preventing the spread of the pandemic to the outside. However, the Wuhan lockdown had little effect on preventing the spread of the pandemic within Wuhan at that time. During this period, a large amount of patients who went to hospitals for medical services were exposed to a high risk of cross-infection without precaution awareness. The pandemic kept dispersing in three towns until the improvement of the capacity of medical treatment, the management of closed communities, the national support to Wuhan, and the implementation of a series of emergency responses at the same time. The findings in this paper reveal the spatiotemporal features of the dispersal of infection risk of COVID-19 and the effects of the prevention and control measures during the early days of the pandemic. The findings were adopted by the Wuhan government to make corresponding policies and could also provide supports to the control of the pandemic in the other regions and countries.
 
Article
The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.
 
Article
Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.
 
Simulation of COVID-19-infected cases in Poland; (a) model of infected cases-seven weeks of 2021; aggregated unconstrained gravity model; (b) Residuals of infected cases-seven weeks of 2021; aggregated unconstrained gravity model; (c) Simulation of infected cases based on seven weeks of 2021; aggregated unconstrained gravity model calibrated using SIR-F models; (d) Simulation of infected cases-seventh week of 2021; aggregated unconstrained gravity model calibrated using SIR-F models.
Spatial interaction model fit statistics (* spatial verification; ** spatial prediction).
Spatial interaction model fit statistics (for different time intervals in the early stages of the outbreak).
Simulation of the SIR-F model in the sixth week of 2021.
Article
This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model’s fit with the spatiotemporal distribution of the validation data.
 
Article
The global outbreak of the COVID-19 epidemic has caused a considerable impact on humans, which expresses the urgency and importance of studying its impacts. Previous studies either frequently use aggregated research methods of statistic data or stay during COVID-19. The afterward impacts of COVID-19 on human behaviors need to be explored further. This article carries out a non-aggregated study methodology in human geography based on big data from social media comments and takes Nanjing, China, as the research case to explore the afterward impact of the COVID-19 epidemic on the spatial behavior of urban tourists. Precisely, we propose the methodology covers two main aspects regarding travel contact trajectory and spatial trajectory. In contact trajectory, we explore three indicators—Connection Strength, Degree Centrality, and Betweenness Centrality—of the collected attractions. Then, in spatial trajectory, we input the results from contact trajectory into ArcGIS by using the Orientation–Destination Model and Standard Deviation Ellipse to explore the influences on the spatial pattern. By setting up comparative groups for the three periods of before, during, and after the COVID-19 in Nanjing, this study found that, in the post-epidemic era, (1) the spatial behavior of urban tourists showed a state of overall contraction; (2) the objects of contraction changed from urban architectural attractions to urban natural attractions; (3) the form of contraction presents concentric circles with the central city (Old City of Nanjing) as the core; (4) the direction of contraction heads to the large-scale natural landscape in the central city, which highlights the importance of green open spaces in the post-epidemic era.
 
Article
Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.
 
Article
The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems.
 
Article
The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.
 
Service population of the community healthcare centers of four travel modes.
The potential capacity indices of various medical facilities.
Article
In December 2019, the coronavirus disease 2019 (COVID-19) pandemic attacked Wuhan, China. The city government soon strictly locked down the city, implemented a hierarchical diagnosis and treatment system, and took a series of unprecedented pharmaceutical and non-pharmaceutical measures. The residents’ access to the medical resources and the consequently potential demand–supply tension may determine effective diagnosis and treatment, for which travel distance and time are key indicators. Using the Application Programming Interface (API) of Baidu Map, we estimated the travel distance and time from communities to the medical facilities capable of treating COVID-19 patients, and we identified the service areas of those facilities as well. The results showed significant differences in service areas and potential loading across medical facilities. The accessibility of medical facilities in the peripheral areas was inferior to those in the central areas; there was spatial inequality of medical resources within and across districts; the amount of community healthcare centers was insufficient; some communities were underserved regarding walking distance; some medical facilities could be potentially overloaded. This study provides reference, in the context of Wuhan, for understanding the spatial aspect of medical resources and residents’ relevant mobility under the emergency regulation, and re-examining the coordination of emergency to improve future planning and utilization of medical facilities at various levels. The approach can facilitate policymakers to assess potential loading of medical facilities, identify low-accessibility areas, and deploy new medical facilities. It also implies that the accessibility analysis can be rapid and relevant even only with open-source data.
 
Article
Public emergencies often have an impact on the production and operation of enterprises. Timely and effective quantitative measurement of enterprises’ offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this study, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with night time lights remote sensing data and Baidu Migration data. The results are verified by official statistics and facts, which demonstrates that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since 10 February, work has been resuming in localities. By the end of March, the work resumption index of most cities exceeded 70% and even Shanghai, Nanjing and Suzhou had achieved complete resumption of work. Wuhan only started to resume work in the last week of March due to the more severe outbreak. Although the level of work resumption is gradually increasing in every area, the specific situation of resumption of work varies in different regions. The process of work resumption in coastal areas is faster, while the process is relatively slow in inland cities.
 
Article
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.
 
Parameters and their usage.
Industrial parks studied in this paper.
Analysis of COVID-19 impacts on the operations of CIPSAs, according to the four ratio parameters.
Analysis of COVID-19 impacts on 10 km buffer zones around parks, according to the four ratio parameters.
Comparison of the impacts of COVID-19 on rural and urban parks and buffer zones.
Article
COVID-19 has had a huge impact on many industries around the world. Internationally-funded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China’s industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019; (2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks.
 
Article
On the 30 January 2020, the WHO declared a public health emergency of international concern due to the coronavirus disease 2019 (COVID-19). Social restrictions with different efficiencies were put in place to avoid transmission. Students living in student accommodation constitute an interesting group to test restrictions because they share living places, workplaces and daily routines, which are key factors in the transmission. In this paper, we present a new geospatial agent-based simulation model to explore the transmission of COVID-19 between students living in Newcastle University accommodation and the efficiency of simulated restrictions (e.g., facemask, lockdown, self-isolation). Results showed that facemasks could reduce infection peak by 30% if worn by all students; an early lockdown could keep 65% of the students safe in the best case; self-isolation could keep 86% of the students safe; while the combination of these measures could prevent disease in 95% of students in the best case-scenario. Spatial analyses showed that the most dangerous places were those where many students interact for a long time, such as faculties and accommodation. The developed ABM could help university managers to respond to current and future epidemics and plan effective responses to keep safe as many students as possible.
 
Article
The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.
 
Article
The stagnation of multinational and cross-regional goods circulation has created significant disruptions to manufacturing supply chains due to the outbreak of the COVID-19 pandemic. To explore the impact of COVID-19 on the circulation of manufacturing industry products at different geographical scales, we drew upon a case study of development zones in the city of Weifang in China to analyze the characteristics of firms’ logistics networks in these development zones, and how these characteristics have changed since the outbreak of the COVID-19 pandemic. The data used in this study were collected from fieldwork conducted between 26 August 2020 and 15 October 2020, and included the supply originations of firms’ manufacturing sources and the sales destinations of their goods. We chose the two-mode network analysis method as our study methodology, which separates the logistics networks into supply networks and sales networks. The results show the following: First, the overall structure of firms’ logistics networks in Weifang’s development zones is characterized by localization. In the context of the COVID-19 pandemic, the local network links have further strengthened, whereas the global links have seriously declined. Moreover, the average path length of both the supply and sales logistics networks has slightly decreased, indicating the increased connectivity of the logistics networks. Second, in terms of the network node centrality, the core nodes of the supply logistics networks are the development zones and the city in which the firms are located, whereas the core nodes of the sales logistics networks are the core companies in the development zones. However, since the outbreak of the COVID-19 pandemic, the centrality of supply originations and sales destinations at the local scale has increased, whereas the centrality of supply originations and sales destinations at the global scale has decreased significantly. Third, the influencing factors of such changes include controlling personnel and goods circulation based on national boundaries and administrative boundaries, forcing the logistics networks in the development zones to shrink to the local scale. Moreover, there are differences in the scope of spatial contraction between supply logistics networks and the sales logistics networks.
 
Article
Spatial distribution heterogeneity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been observed in several countries. While previous studies have covered vast geographic areas, detailed analyses on smaller territories are not available to date. The aim of our study was to understand the spatial spread of SARS-CoV-2 in a province of Northern Italy through the analysis of positive nasopharyngeal (NP) swabs. The study was conducted on subjects who lived in the province of Alessandria with at least one positive NP swab between 2 March and 22 December 2020. To investigate if clustering occurred, the proportion of SARS-CoV-2 positive subjects over the total number of residents in each small administrative subregion was calculated and then mapped. A total of 17,260 subjects with at least one positive NP swab were included; the median age was 54 years (Interquartile range 38–72) and 54.9% (n = 9478) of our study population were female. Among the 192 towns scanned, 26 showed a prevalence between 5% and 7.5%, one between 7.5% and 10% and two with more than 10% positive swabs. The territories with a higher prevalence of positive subjects were located in areas with at least one nursing home and potential clusters were observed within these structures. The maps produced may be considered a useful and important monitoring system to identify areas with a significant and relevant diffusion of SARS-CoV-2.
 
Article
The COVID-19 pandemic is a major challenge for society as a whole, and analyzing the impact of the spread of the epidemic and government control measures on the travel patterns of urban residents can provide powerful help for city managers to designate top-level epidemic prevention policies and specific epidemic prevention measures. This study investigates whether it is more appropriate to use groups of POIs with similar pedestrian flow patterns as the unit of study rather than functional categories of POIs. In this study, we analyzed the hour-by-hour pedestrian flow data of key locations in Beijing before, during, and after the strict epidemic prevention and control period, and we found that the pedestrian flow patterns differed greatly in different periods by using a composite clustering index; we interpreted the clustering results from two perspectives: groups of pedestrian flow patterns and functional categories. The results show that depending on the specific stage of epidemic prevention and control, the number of unique pedestrian flow patterns decreased from four before the epidemic to two during the strict control stage and then increased to six during the initial resumption of work. The restrictions on movement are correlated with most of the visitations, and the release of restrictions led to an increase in the variety of unique pedestrian flow patterns compared to that in the pre-restriction period, even though the overall number of visitations decreased, indicating that social restrictions led to differences in the flow patterns of POIs and increased social distance.
 
Article
The coronavirus disease 2019 (COVID-19) pandemic has had tremendous and extensive impacts on the people’s daily activities. In Chicago, the numbers of crime fell considerably. This work aims to investigate the impacts that COVID-19 has had on the spatial and temporal patterns of crime in Chicago through spatial and temporal crime analyses approaches. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal trends of different crimes, detect the outliers of crime events, and examine the periodic variations of crime distributions. The results showed a certain phase pattern in the trend components of assault, battery, fraud, and theft. The largest outlier occurred on 31 May 2020 in the remainder components of burglary, criminal damage, and robbery. The spatial point pattern test (SPPT) was used to detect the similarity between the spatial distribution patterns of crime in 2020 and those in 2019, 2018, 2017, and 2016, and to analyze the local changes in crime on a micro scale. It was found that the distributions of crime significantly changed in 2020 and local changes in theft, battery, burglary, and fraud displayed an aggregative cluster downtown. The results all claim that spatial and temporal patterns of crime changed significantly affected by COVID-19 in Chicago, and they offer constructive suggestions for local police departments or authorities to allocate their available resources in response to crime.
 
Non-linear relationship between air pollution discrepancies and confirmed cases of COVID-19. (a) CO discrepancy and confirmed cases. (b) NO 2 discrepancy and confirmed cases. (c) O 3 discrepancy and confirmed cases. (d) SO 2 discrepancy and confirmed cases.
EDF of air pollutions estimated from GAM. (a) EDF of CO. (b) EDF of NO2. (c) EDF of O3. (d) EDF of SO2.
Margin effects (with confidence intervals) of air pollutions on confirmed cases in different countries. (a) CO column concentration; (b) NO2 column concentration; (c) O3 column concentration; (d) SO2 column concentration.
Article
The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales.
 
Top-cited authors
Linda M. See
  • International Institute for Applied Systems Analysis
Alexander Zipf
  • Universität Heidelberg
Jantien Stoter
  • Delft University of Technology
Filip Biljecki
  • National University of Singapore
Sisi Zlatanova
  • UNSW Sydney