Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Spatial and Socioeconomic Analysis of Purposeful Mobile Internet Use in US States

The digital divide in the United States has received renewed attention during the COVID-19 pandemic. As achievement of digital equity remains a high priority, this study examines spatial patterns and socioeconomic determinants of the purposeful use of mobile internet for personal and business needs in US states. Agglomerations of mobile internet use are identified using K-means clustering and the extent of agglomeration is measured using spatial autocorrelation analysis. Regression analysis reveals that mobile internet use is associated with employment in management, business, science, and arts occupations, affordability, age structure, and the extent of freedom in US states. Spatial randomness of regression residuals shows the effectiveness of the conceptual model to account for spatial bias. Implications of these findings are discussed. © 2023 IEEE Computer Society. All rights reserved.

Satellite-based estimates of daily NO2 exposure in urban agglomerations of China and application to spatio-temporal characteristics of hotspots

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd

Temporal evolution and spatial pattern of the COVID-19 epidemic in the United States

It is the cornerstone of precise and scientific prevention and control to understand the temporal evolution and spatial pattern of the COVID-19 epidemic. Based on the county-level COVID-19 case of the United States from January 22, 2020 to October 8, 2021, we explored and analyzed the epidemic by using time series analysis, spatial autocorrelation analysis and gravity center trajectory analysis. The results show that: (1) the epidemic in the United States experienced four stages of low incidence, growth, peak and rebound with June 15, September 30 and October 1, 2020 as the cut-off points. (2) The global Moran index experienced a process of 'increase-decrease-increase-stability', with the maximum value exceeding 0.6, indicating that the epidemic has obvious spatial aggregation;the epidemic is dominated by high-high clusters (over 150 counties) and low-low clusters (over 500 counties), presenting a pattern of 'three cores and multiple islands' and 'north-south belt'. (3) In 60% of states, the trajectory of the epidemic center of gravity is near-linear type. The epidemic hotspots in these states were relatively stable over time. In more than half of the states, the curve of the moving distance of the epidemic center of gravity is exponential. These states experienced a very rapid epidemic. This study is expected to provide a reference for evaluating the effectiveness of epidemic prevention measures and determining targeted epidemic prevention measures, as well as accumulate experience for future research on the spread of different infectious diseases in different regions. © 2022 IEEE.

Spatial characteristics of some aspects of regional ‘realised competitiveness’ in Hungary between 2010 and 2019

In today's digitalising global economy, competition between regions and municipalities is increasingly intensifying across national borders. In addition, two events have occurred in recent decades that have had a major impact on the global economy. One was the global financial and economic crisis, the other the Covid-19 pandemic with its social and economic challenges. In the period between these shocks, the world experienced a relative economic recovery, but it can be seen that the competitiveness of the regions concerned and its analysis over time is important in the postcrisis recovery. This is no different in the United States, the European Union and, within that, Hungary. It can be concluded that regional ‘realised competitiveness’ between 2010 and 2019 in Hungary has shown a spatially different and distinctive geographical pattern that needs to be analysed. Therefore, the aim of the study is to examine the geographical pattern of regional ‘realised competitiveness’ and income divergence in Hungary between 2010 and 2019 and to shed light on some aspects and linkages. The author has formulated research questions to detect spatial and social processes and applied mathematical statistical and geoinformatics methods to answer them. The study concludes that there are significant spatial differences in the performance of areas and the income of their inhabitants, which were affected differently by various economic, spatial and social factors between 2010 and 2019. There is both a narrowing of spatial disparities and a widening of geographically differentiated income gaps in society. However, the changes that took place between 2010 and 2019 are also reflected in the spatial pattern and context of the regional ‘realised competitiveness’ of districts. © 2022,Teruleti Statisztika. All rights reserved

Spatial characteristics of some aspects of regional 'realised competitiveness' in Hungary between 2010 and 2019

In today's digitalising global economy, competition between regions and municipalities is increasingly intensifying across national borders. In addition, two events have occurred in recent decades that have had a major impact on the global economy. One was the global financial and economic crisis, the other the Covid-19 pandemic with its social and economic challenges. In the period between these shocks, the world experienced a relative economic recovery, but it can be seen that the competitiveness of the regions concerned and its analysis over time is important in the postcrisis recovery. This is no different in the United States, the European Union and, within that, Hungary. It can be concluded that regional 'realised competitiveness' between 2010 and 2019 in Hungary has shown a spatially different and distinctive geographical pattern that needs to be analysed. Therefore, the aim of the study is to examine the geographical pattern of regional 'realised competitiveness' and income divergence in Hungary between 2010 and 2019 and to shed light on some aspects and linkages. The author has formulated research questions to detect spatial and social processes and applied mathematical statistical and geoinformatics methods to answer them. The study concludes that there are significant spatial differences in the performance of areas and the income of their inhabitants, which were affected differently by various economic, spatial and social factors between 2010 and 2019. There is both a narrowing of spatial disparities and a widening of geographically differentiated income gaps in society. However, the changes that took place between 2010 and 2019 are also reflected in the spatial pattern and context of the regional 'realised competitiveness' of districts. (English) [ FROM AUTHOR] Napjaink digitalizálódó globális gazdaságában országhatárokon átívelően egyre nagyobb mértékben felerősödik a régiók és a települések közötti verseny. Mindemellett az utóbbi évtizedekben két, a világgazdaságot jelentősen befolyásoló eseményt is bekövetkezett. Egyik a pénzügyi-gazdasági világválság volt, a másik pedig a Covid-19-világjárvány, annak társadalmi és gazdasági kihívásaival. Az általuk kiváltott sokkhatások közötti időszakban viszonylagos gazdasági fellendülés jellemezte a világot, azonban megállapítható, hogy a válságok utáni helyreállás során fontos szerepet kap az adott térségek versenyképessége és ennek időbeli elemzése. Nincs ez másként az Egyesült Államokban, az Európai Unióban és azon belül Magyarországon sem. A regionális megvalósult versenyképesség 2010 és 2019 között Magyarországon térben eltérő és jellegzetes földrajzi mintázatot mutatott, amit elemezni szükséges. Ebből adódóan a tanulmány célja, hogy megvizsgálja Magyarország 2010 és 2019 között bekövetkezett regionális megvalósult versenyképességének és jövedelmi eltéréseinek földrajzi mintázatát, valamint megvilágítson néhány szempontot és összefüggést. A szerző a térbeli és társadalmi folyamatok kimutatására kutatási kérdéseket fogalmazott meg, amelyek megválaszolásához matematikai-statisztikai és geoinformatikai módszereket alkalmazott. A tanulmány arra a következtetésre jut, hogy jelentős térbeli különbségek vannak a területek teljesítményében és az ott élők jövedelmében, amire az egyes gazdasági, térbeli és társadalmi tényezők eltérően hatottak 2010 és 2019 között. Egyszerre figyelhető meg a térbeli különbségek csökkenése, valamint földrajzi szempontból differenciáltan a társadalomban meghúzódó jövedelmi eltérések egyfajta növekedése is. Mindemellett a 2010 és 2019 között végbement változások kirajzolódtak a járások regionális megvalósult versenyképességének területi mintázatában és összefüggéseiben is. (Hungarian) [ FROM AUTHOR] Copyright of Területi Statisztika is the property of Hungarian Central Statistical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid-19 severity and urban factors: investigation and recommendations based on machine learning techniques

Since March 5, 2020, the West Bank has faced a real crisis due to the Coronavirus disease 2019 (COVID-19) pandemic. It has infected 581,678 people and caused 5,382 deaths so far, which has resulted in negative impacts on public health and other aspects of daily life. Based on the data provided by the Palestinian Ministry of Health, we inferred the spatial distribution patterns of the pandemic condition in different communities using Geographic Information System (GIS) analysis for pattern and clustering by studying the impact of urban factors on the number of confirmed COVID-19 cases. Ten urban factors were selected (i.e., population, population density, aging ratio, the hierarchy of services, health services, land use, commercial ser-vices, road density, green areas, and open spaces) to check their relation to pandemic severity using a linear model, where five factors showed a globally-significant relation. Then, the Geographically Weighted Regression' model (GWR) was adopted to define their unevenly dis-tributed effects in the urban areas on the northwest bank. Among the five factors, the population factor has the most significant impact on the epidemic situation with a positive correlation. However, a negative correlation has been stated between the area of commercial services per person, population density, hierarchy of services, and health services. Finally, we provide recommendations that coordinate various urban factors to mitigate the pandemic spread. This paper will help decision-makers plan and develop different areas in Palestine and worldwide by better understanding the transmission, occurrence, and diffusion of the COVID-19 pandemic in urban areas. © 2022, An-Najah National University. All rights reserved.

COVID-19 AND INTERNAL MIGRATION IN SERBIA— GEOGRAPHICAL PERSPECTIVE

Internal migration is an essential part of regional population change. Driven by various determinants, internal migration has been unequal across time and space. Migration responses to the changes in societal circumstances make it relevant to investigate the spatial and temporal dimension of internal migration in Serbia before and in the aftermath of the COVID-19 pandemic outbreak. The research aims to identify to what extent and in what way the pandemic has changed the magnitude and geographical patterns of internal migration in Serbia. The study is based on additionally processed official statistics on internal migration for the period 2018–2020, from March to December for each year, at the municipal, district (oblast, plural—oblasti), and regional levels. These are aggregate administrative data on usual residence registration by month. The derived data on the net migration rate is cartographically presented using the classification method natural Breaks (Jenks). Spatial dependence was assessed applying the spatial autocorrelation method, based on the Local Moran statistic. The results revealed that the pandemic affected not only the volume of internal migration but also its spatial patterns. The findings present new insights on the role of internal migration in reallocation of population across Serbia before and during the COVID-19 pandemic while underlying the importance of further research to deepen the understanding of internal migration trends upon the COVID-19 outbreak. © 2022, Geographical Institute "Jovan Cviji" of the Serbian Academy of Sciences and Arts. All rights reserved.

Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics: Case Studies of Four Waves in Vietnam

An outbreak of the COVID-19 pandemic caused by the SARS CoV 2 has profoundly affected the world. This study aimed to identify the spatio-temporal clustering of COVID-19 patterns using spatial statistics. Local Moran's I spatial statistic and Moran scatterplot were first used to identify high-high and low-low clusters and low-high and high-low outliers of COVID-19 cases. Getis-Ord's[G]_i<

Understanding differences in rural tourism recovery: a critical study from the mobility perspective

This study analyzes the impact of the COVID-19 pandemic on rural tourism and the subsequent recovery process from the perspective of mobility. The results show that the pandemic has exacerbated the time-space restrictions on tourist mobility, and the recovery process of different types of rural tourist destinations differs. Although tourist numbers in some rural areas have increased, the rural tourism market has not fully recovered and is struggling to grow significantly after the pandemic;thus, local governments and managers need to adopt prudent and diverse governance policies. The study criticizes the current research and holds that over-optimistic feelings have covered up the dilemma of rural tourism, violated the rights of vulnerable rural groups to have their voices heard, and may further exacerbate the uneven development of rural tourism. This study provides new insights that have important implications for future research on rural tourism.

Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns

As a result of the changes in social behavior due to lockdown measures aimed to avoiding COVID-19 infection, changes in crime patterns have been observed in several cities around the world. This study has two objectives: (1) Analyze the spatio-temporal patterns of the incidence of street robbery and vehicle theft in Mexico City, before and after the social distancing measures begun. Throughout this period, it has been shown a decrease in high-impact robberies in Mexico City. However, changes in spatial patterns have not been studied yet. (2) Propose an algorithm for the visualization of spatio-temporal relationships of crimes to identify near repeat patterns. These two objectives are considered relevant to identify areas of repeat victimization, especially before an imminent return to routine activities in the city, such as the return to school, the reopening of restaurants, movie theaters, shopping malls and other businesses;and thus be able to contribute to identify and prevent these crimes. One of the main results is that despite crime volumes decreased, some specific crime locations remained after the lockdown. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Temporal and Spatial Patterns of Sediment Microbial Communities and Driving Environment Variables in a Shallow Temperate Mountain River.

Microbial communities in sediment play an important role in the circulation of nutrients in aquatic ecosystems. In this study, the main environmental factors and sediment microbial communities were investigated bimonthly from August 2018 to June 2020 at River Taizicheng, a shallow temperate mountain river at the core area of the 2022 Winter Olympics. Microbial community structure was analyzed using 16S rRNA genes (bacteria 16S V3 + V4 and archaea 16S V4 + V5) and high-throughput sequencing technologies. Structure equation model (SEM) and canonical correspondence analysis (CCA) were used to explore the driving environmental factors of the microbial community. Our results showed that the diversity indices of the microbial community were positively influenced by sediment nutrients but negatively affected by water nutrients. Bacteroidetes and Proteobacteria were the most dominant phyla. The best-fitted SEM model indicated that environmental variables not only affected community abundance directly, but also indirectly through influencing their diversity. Flavobacterium, Arenimonas and Terrimonas were the dominant genera as a result of enriched nutrients. The microbial community had high spatial-temporal autocorrelation. CCA showed that DO, WT and various forms of phosphorus were the main variables affecting the temporal and spatial patterns of the microbial community in the river. The results will be helpful in understanding the driving factors of microbial communities in temperate monsoon areas.

Spatial Panel Data Modelling of COVID-19 Spreading at District Level in Sarawak, Malaysia

The COVID-19 outbreak was well-controlled in the state of Sarawak, Malaysia in year 2020. A surge in positive cases started in January 2021 and affected all districts including the rural areas which have relatively limited health facilities. Hence, we investigated the spatial patterns of COVID-19 spreading at district level for the first 16 epidemiological weeks of 2021 by spatial autocorrelation analysis and spatial panel regression model. The results show that there exists weak positive spatial autocorrelation of COVID-19 confirmed cases. Having said that, the spatial cluster of high values in both weekly rate of confirmed cases and its spatial lag emerged in the center part of Sarawak in the seventh epidemiological week. Six other districts were identified as high potential for spill overing the disease to its neighbouring districts. Among the six spatial panel regression models constructed, the spatial autoregressive model which includes the spatial lag of COVID-19 confirmed cases, apart from the other two independent variables (recovered and death), is a better-fitting model. This implies that the COVID-19 spreading in the neighbouring districts has a significant effect on the rate of confirmed cases in a particular district of Sarawak. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne.

This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes - diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to 'COVID-normal' living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.

Spatial and temporal patterns and factors influencing the spread of the COVID-19 pandemic in China

It is essential to unravel the spatial and temporal patterns of the spread of the epidemic in China during the backdrop of the global coronavirus disease 2019 (COVID-19) outbreak in 2020, as the underlying drivers are crucial for scientific formulation of epidemy-preventing strategies. A discriminant model for the spatio-temporal pattern of epidemic spread was developed for 317 prefecture-level cities using accumulated data on confirmed cases. The model was introduced for the real-time evolution of the outbreak starting from the rapid spread of COVID-19 on January 24, 2020, until the control on March 18, 2020. The model was used to analyze the basic characteristics of the spatio-temporal patterns of the epidemic spread by combining parameters such as peak position, full width at half maximum, kurtosis, and skewness. A multivariate logistic regression model was developed to unravel the key drivers of the spatio-temporal patterns based on traffic accessibility, urban connectivity, and population flow. The results of the study are as follows. (1) The straight-line distance of 588 km from Wuhan was used as the effective boundary to identify the four spatial patterns of epidemic spread, and 13 types of spatio-temporal patterns were obtained by combining the time-course categories of the same spatial pattern. (2) The spread of the epidemic was relatively severe in the leapfrogging model. Besides the short-distance leapfrogging model, significant differences emerged in the spatial patterns of the time course of epidemic spread. The peaks of the new confirmed cases in various spatio-temporal patterns were mostly observed on February 3, 2020. The average full widths at the half maximum of all ordinary cities were approximately 14 days, thus, resonating with the incubation period of the COVID-19 virus. (3) The degree of the population correlation with Wuhan city has mainly influenced the spreading and the short-distance leapfrogging spatial patterns. The existence of direct flight from Wuhan city exhibited a positive effect on the long-distance leapfrogging spatial pattern. The number of population outflows has significantly affected the leapfrogging spatial pattern. The integrated spatial pattern was influenced by both primary and secondary epidemic outbreak sites. Thus, cities should pay great attention to traffic control during the epidemic as analysis has shown that the spatio-temporal patterns of epidemic spread in the respective cities can curb the spread of the epidemic from key links. © 2022, Science Press. All right reserved.

The dynamics of world economy geography and the role of China in economic globalization

From the perspective of economic geography, this paper studies the changing spatial pattern of world economy and China's role in different waves of economic globalization. Firstly, this study finds that the geographical pattern of world economy changes from "core-periphery" to "chain-reconfiguration", and to current "network-imbalance". Meanwhile the driving force of economic globalization shifts from "trade globalization" to "manufacturing globalization". At present, "multiple globalization" is involving into a new engine to driving the development of economic globalization. We then discuss that how China changes its role in economic globalization by changing modes of strategic coupling. We argue that the role transition of China breaks the traditional developing path which developed countries set for developing countries and theoretical spatial order put forward by classical industry gradient transfer, bringing new restructuring power and possibility for changing pattern of globalization. Finally, we discuss the impacts of COVID-19 pandemic on the development of economic globalization and the development trend of economic globalization in the post-pandemic era. Based on the analysis, we come up with some suggestions regarding to the potential development paths of China under the background of economic globalization. © 2022, Science Press. All right reserved.

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO2) and fine particulate matters (PM2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estimates showed that in February, the control measures led to average NO2 reductions of 19 µg/m3 and average PM2.5 reductions of 12 µg/m3. At the same time, meteorology was estimated to contribute about 12 µg/m3 increase in NO2, thereby offsetting most of the reductions as well as an increase of 30 µg/m3 in PM2.5, thereby resulting in concentrations higher than the average PM2.5 concentrations during the lockdown. At the beginning of the lockdown period, the boundary layer height was the dominant factor contributing to a 17% increase in NO2 while humid condition was the dominant factor for PM2.5 concentrations leading to an increase of 65% relative to the baseline level. Estimated NO2 emissions declined by 42% at the start of the lockdown, after which the emissions gradually increased with the increase of traffic volumes. The diurnal patterns from the models showed that the peak of vehicular traffic occurred from about 12pm to 5pm daily during the strictest control periods. This study provides insights for quantifying the changes in air quality due to the lockdowns by accounting for meteorological variability and providing a reference in evaluating the effectiveness of control measures, thereby contributing to air quality mitigation policies.

COVID-19: Perturbation dynamics resulting chaos to stable with seasonality transmission.

The outbreak of coronavirus is spreading at an unprecedented rate to the human populations and taking several thousands of life all over the globe. In this paper, an extension of the well-known susceptible-exposed-infected-recovered (SEIR) family of compartmental model has been introduced with seasonality transmission of SARS-CoV-2. The stability analysis of the coronavirus depends on changing of its basic reproductive ratio. The progress rate of the virus in critical infected cases and the recovery rate have major roles to control this epidemic. Selecting the appropriate critical parameter from the Turing domain, the stability properties of existing patterns is obtained. The outcomes of theoretical studies, which are illustrated via Hopf bifurcation and Turing instabilities, yield the result of numerical simulations around the critical parameter to forecast on controlling this fatal disease. Globally existing solutions of the model has been studied by introducing Tikhonov regularization. The impact of social distancing, lockdown of the country, self-isolation, home quarantine and the wariness of global public health system have significant influence on the parameters of the model system that can alter the effect of recovery rates, mortality rates and active contaminated cases with the progression of time in the real world.

Stage 1 registered report: spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level.

In this stage 1 registered report, we propose an analysis of the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We will compute weekly Moran index to assess spatial autocorrelation over time and identify clusters of the disease using the "flexibly shaped spatial scan" approach. Finally, different distance models will be compared to select the best suited to predict inter-municipality contagion. This study will help us understand the spread of the epidemic over the Mexican territory and give insights to model and predict the epidemic behavior.

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans.

Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria-Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.