Spatial Analysis Approach Research Articles

Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: A structured spatial analytical approach.

Infant mortality rate is a measure of population health and neonatal mortality account for great proportion of these deaths. Underdevelopment might be associated to higher neonatal mortality risk due to assistant related factors. Spatial and temporal distribution of mortality help identifying and developing strategies for interventions. To investigate the cluster areas of asphyxia-associated neonatal mortality and to explore its association with per capita gross domestic product (GDP) in São Paulo State (SP), Brazil. Ecological study including live births residents in SP from 2004-2013. Neonatal deaths (0-27 days) with perinatal asphyxia were defined as intrauterine hypoxia, birth asphyxia or meconium aspiration syndrome written in any line of the Death Certificate. Geoprocessing analytical approach included detection of first order effects through quintiles and spatial moving average maps, followed by second order effects by global and local spatial autocorrelation (Moran and LISA, respectively) before and after smoothing with local Bayesian estimates. Finally, Spearman correlation was applied between asphyxia-associated neonatal mortality and mean per capita GDP rates for the municipalities with significant LISA. There were 6,713 asphyxia-associated neonatal deaths among 5,949,267 live births (rate: 1.13/1000) in SP. Spatial moving average maps showed a non-random distribution among municipalities, with presence of clusters (I = 0.048; p = 0.023). LISA map identified clusters of asphyxia-associated neonatal mortality in the south, southeast and northwest. After applying local Bayes estimates, clusters were more pronounced (I = 0.589; p = 0.001). There was a partial overlap of the areas of higher asphyxia-associated neonatal mortality and lower mean per capita GDP. Spatial analysis identified cluster areas of high asphyxia-associated neonatal mortality and low per capita GDP rates, with a significant negative correlation. This optimized, structured, and hierarchical approach to identify high-risk areas of cause-specific neonatal mortality may be helpful for guiding public health efforts to decrease neonatal mortality.

SPATIAL PATTERN OF DISTRIBUTION OF DENGUE FEVER IN GORONTALO CITY IN 2019-2020

Gorontalo City is an endemic area of ​​dengue hemorrhagic fever, in 2019 the Incidence Rate was 55.06 per 100,000 population, in 2020 there was a decrease in the Incidence Rate but not significantly at 49.36 per 100.00 population The complexity of risk factors associated with the spread of Fever BLOODING DENGUE often causes related agencies to be less appropriate in planning prevention and control programs in their coverage areas. Therefore, an analysis of the distribution pattern of dengue hemorrhagic fever was carried out so that it could be utilized by the relevant agencies in controlling dengue hemorrhagic fever cases. The purpose of this study was to determine the pattern of distribution of dengue hemorrhagic fever cases in Gorontalo City. This research is a type of descriptive research with a spatial analysis approach. The analytical method used is Average Nearest Neighbor (ANN). The number of samples in this study were 138 which were analyzed based on kelurahan units. The results of the analysis showed that in 2019 there was a spatial pattern with NNR 0.608 < 1 which means the distribution pattern is clustered, while in 2020 there is a spatial pattern with NNR 0.624 <1 which means the distribution pattern is clustered. This study concluded that the distribution of dengue fever cases in Gorontalo City in 2019 and 2020 showed a clustered pattern. Areas with a clustered distribution pattern indicate areas that are prone to dengue hemorrhagic fever.

A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.

Identification and spatial mapping of tracers of PM10 emission sources using a high spatial resolution distributed network in an urban setting

We employed an experimental approach for high spatial resolution sampling and analysis of PM10, allowing identification and spatial mapping of tracers of PM10 emission sources. Very-low-volume samplers were used at 17 sites in Amersfoort and at one regulatory reference site in Utrecht, The Netherlands, in a 5-month monitoring period (from September 2018 to February 2019), to assess the monthly spatial distribution of PM10 mass and PM10 chemical compounds. By performing principal component analysis on the obtained spatially-resolved data, selective and reliable source tracers were identified for soil dust (Ca+, Cl−, insoluble Al, Ce, Li, U and V), brake dust (insoluble Fe, Mn, Mo, Nb, Sb, Sn, W and Zr, water-soluble Fe, Mn, Mo and Sb), industrial and/or agricultural emissions (NH4+, water-soluble As, Co, Fe, Mn, Mo, Pb, Sb, Se, Sn and Ti), secondary organic and inorganic aerosols (water-soluble organic carbon, NO3− and SO42−), biomass domestic heating (water-soluble organic carbon, levoglucosan, water-soluble Cs, Li, Rb and Tl) and New Year's Eve fireworks (K+, Mg2+, Na+, water-soluble Al, Ba, Bi, Cr, Cu and Sr). The autumn and winter spatial mapping of the identified source tracers allowed us to effectively assess and localize the impact of the different PM10 sources and to evaluate the diffusion of the PM10 particles. This approach proved to be very effective to trace low-intensity PM10 sources and to map their seasonal spatial distribution. The obtained spatially-resolved chemical data can be used in further studies to evaluate spatial relationships between the concentration of PM10 air pollutants and adverse outcomes for human health. This approach promises to be a powerful tool for obtaining seasonal and spatially-resolved information about PM composition and sources in several study areas, having high impact on the air quality management.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.

Microbial pathogens have evolved numerous mechanisms to hijack host’s systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one’s analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Mumbai's business landscape: A spatial analytical approach to urbanisation

India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.

Face Masking Violations, Policing, and COVID-19 Death Rates: A Spatial Analysis in New York City ZIP Codes

The use of face masks during a pandemic and compliance with state and local mandates has been a divisive issue in the United States. We document variation in face masking violation rates involving police enforcement in New York City and examine the association between police-enforced face masking violations and COVID-19-related death rates. We assemble a Zone Improvement Plan (ZIP) code–level data set from the New York City Open Data, Department of Health, and the American Community Survey (2014–2018). We use maps to demonstrate the spatial patterning of police-enforced face masking violation rates and COVID-19-related death rates. Using a Bayesian spatial analysis approach to model police-enforced face masking violations, we find considerable variation in police-enforced face masking violation rates and COVID-19-related death rates across New York City and similarities in their spatial distribution, with higher rates for both measures found in Brooklyn and the Bronx. The positive association between police-enforced face masking violation rates and COVID-19-related death rates holds after including other covariates. The percentage of non-Hispanic Blacks, Hispanics, and households with limited English proficiency are positively associated with police-enforced face masking violations. This study extends the COVID-19 literature by reporting more aggressive enforcement of face masking rules in minority and limited-English-proficiency communities.

Spatial analysis approaches for the evaluation and protection of groundwater resources in large watersheds of the Canadian Shield

Spatial analysis approaches for the evaluation and protection of groundwater resources in large watersheds of the Canadian Shield

A scenario-based approach for urban water management in the context of the COVID-19 pandemic and a case study for the Tabriz metropolitan area, Iran

The world's poorest countries were hit hardest by COVID-19 due to their limited capacities to combat the pandemic. The urban water supply and water consumption are affected by the pandemic because it intensified the existing deficits in the urban water supply and sanitation services. In this study, we develop an integrated spatial analysis approach to investigate the impacts of COVID-19 on multi-dimensional Urban Water Consumption Patterns (UWCPs) with the aim of forecasting the water demand. We selected the Tabriz metropolitan area as a case study area and applied an integrated approach of GIS spatial analysis and regression-based autocorrelation assessment to develop the UWCPs for 2018, 2019 and 2020. We then employed GIS-based multi-criteria decision analysis and a CA-Markov model to analyze the water demand under the impacts of COVID-19 and to forecast the UWCPs for 2021, 2022 and 2023. In addition, we tested the spatial uncertainty of the prediction maps using the Dempster Shafer Theory. The results show that the domestic water consumption increased by 17.57% during the year 2020 as a result of the COVID-19 pandemic. The maximum increase in water consumption was observed in spring 2020 (April–June) when strict quarantine regulations were in place. Based on our results, the annual water deficit in Tabriz has increased from ~18% to about 30% in 2020. In addition, our projections show that this may further increase to about 40–45% in 2021. Relevant stakeholders can use the findings to develop evidence-informed strategies for sustainable water resource management in the post-COVID era. This research also makes other significant contributions. From the environmental perspective, since COVID-19 has affected resource management in many parts of the world, the proposed method can be applied to similar contexts to mitigate the adverse impacts and developed better informed recovery plans.

City transportation network vulnerability to disasters: the case of Hurricane Hermine in Florida

ABSTRACT Hurricanes affect millions of people in the U.S. every year and cause billion-dollar economic losses. Florida is one of the states in the U.S. which is vulnerable to hurricanes and major infrastructure damages were reported due to these extreme events such as Hurricane Hermine (2016). This study aims to assess the impacts of Hermine on the transportation network of Tallahassee, the capital of Florida, using a spatial-analytical approach and real-world hurricane data on roadway closures, power outages, power network configuration, and other storm-related variables. First, a geographic information systems (GIS)-based analysis was utilised to obtain the spatial patterns of hurricane-induced roadway closures along with power outages, power lines, downed trees, and the city’s flood hazard zones. Second, a statistical model was developed to understand the association between the aforementioned variables and roadway closures. The spatial model results revealed that the roadway closures and power outages almost have the same patterns, and the city centre experienced more disruptions than other areas. The statistical model findings indicated that the power outages and power lines have strong relationships with roadway closures. The obtained knowledge of this study can provide valuable information for city officials to prepare better emergency policies for future potential hurricanes.

Spatial Multiresolution Analysis Approach to Identify Crash Hotspots and Estimate Crash Risk

AbstractIn this paper, we evaluate the performance of a spatial multiresolution analysis (SMA) method, that behaves like a variable bandwidth kernel density estimation (KDE) method, for hazardous r...

A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results

A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers.

Evaluation of the intention to shift to public transit in Saudi Arabia

Public transit (PT) is the backbone of a sustainable transport system. This study aims to investigate commuters’ initial intentions to shift to PT in a car-oriented city without a PT, quantifying the direct economic and environmental impacts. The study considers data from a recent survey conducted in Medina, Saudi Arabia. Analyses are based on a stated-preference (SP) survey in conjunction with spatial analysis approaches. SP results show that 53% of participants are willing to shift to PT, with no spatial autocorrelation existing between the participants’ willingness to use PT and their residential zones. Logistic regression indicates that participants who used PT abroad are more likely to shift whenever PT becomes available in their city. Equally, gender and vehicular occupancy rate determine participants’ intention to shift to PT. The results show that for each 1% of would-be PT commuters, a reduction of 0.79% in CO2 emissions is obtained.

Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta

AbstractSpatiotemporal analysis and monitoring of vegetation help us investigate ecological health and guide better forest conservation and land management practices for sustainable development. This paper proposes the use of spatial analysis approaches (i.e., ordinary least squares [OLS] and the Hurst exponent) combined with time‐series analysis using enhanced vegetation index (EVI) data, derived from LANDSAT via the Google Earth Engine, to estimate the trends and sustainability of vegetation dynamics in the Tra Vinh Province in the Mekong River Delta. We also assessed the EVI changes connected to land change issues to examine the influence of land use conversion on vegetation dynamics. Results show that a large portion of the study area was covered by abundant vegetation (over 50% of the total area), and the increased EVI area was about 5.5‐times greater than the area of EVI reduction. Additionally, vegetation sustainability was being seriously compromised (e.g., a decrease in the total area of 8,275 ha) due to several land conversion drivers such as shrimp farming, urbanisation, and industrialisation. Furthermore, results obtained from this research provide insight into the spatiotemporal dynamics of vegetation coverage and reveal the consistency of future vegetation trends. Moreover, the study also quantitatively assessed the positive impacts of Buddhist doctrines on reducing the negative trend of vegetation change in the study area. These findings can lay the ground to formulate sustainable land and environmental plans that meet the 11th, 13th and 15th Sustainable Development Goals (SDGs) (i.e., the sustainable cities and communities, the climate actions, and the life on land). Besides, the analytical procedure adopted in this study can also be applicable to any other coastal areas that require the accurate assessment of vegetation status over time.

Spatial data analysis approach for network-wide consolidation of bus stop locations

Existing studies indicate that ideal spacing between bus stops improves the quality of service, travel time, and other transit service parameters. In this study, a network-wide heuristic methodology is proposed to optimize the number of stops in an existing bus network by eliminating redundant stops along each bus route in the network through an iterative process. It is postulated that ideal stop spacing can substantially lower the operating costs as it can reduce fleet size requirements without affecting the performance of the transit system. The proposed methodology attempts to eliminate undesired stops based on two vital inputs: (1) stop spacing, and (2) passenger boarding, which represent accessibility and travel time/delay. Potentially undesirable stops are eliminated recursively while their corresponding passenger boarding is redistributed across the adjacent stops. The process is repeated until ideal stop locations are identified. Stop location consolidation is evaluated using two performance indicators, i.e., in-vehicle travel time and access distance, which are considered for evaluating the performance of the modified bus network and comparing the total ridership with the existing network to justify the improvements. In other words, the ideal stop spacing is determined based on projected demand (ridership) loss and assessment of the performance indicators. The application of the proposed methodology is demonstrated using the bus network of the City of Regina in Saskatchewan, Canada. The findings of this research are expected to serve as a decision support tool for transit planners by offering additional flexibility to adopt appropriate bus stop spacing for an existing network.

Spatial analysis of shared e-scooter trips

Shared e-scooters have become a common mode of transportation in many cities around the world. E-sooters provide convenient and quick rides for short distances and can act as a connection for first/last mile trips. To date, limited studies have explored the spatial variation of e-scooter trips and there is knowledge to be gained by investigating variables associated with e-scooter trip generation. This study implemented a spatial analysis approach, Geographical Weighted Regression (GWR), to explore how factors relating to demographics, density, diversity, design, urbanism scores, distance to transit and other transportation-related variables influence e-scooter trips in Louisville, KY. More than 400,000 e-scooter trips across 159 Traffic Analysis Zones (TAZs) were included in the study. Results show TAZ-level factors including land use, age distribution, gender distribution, Walk Score and Park Score impacted the density of e-scooters trips in the TAZ. The GWR model showed improvements over a global Ordinary Least Squares (OLS) model. Local goodness of fit ranged from 0.732–0.895 across the study area. This study can help governments and e-scooter sharing companies develop policies that maximize e-scooter use, equity, and accessibility while improving the mobility of cities.

Spatial variability analysis of the solar energy resources for future urban energy applications using Meteosat satellite-derived datasets

The proper management and operation of any energy resources require accurate analysis and assessment of the available resources. In this context, the variability analysis of solar energy resources is gaining attention due to its prospects for future thermal/heating and photovoltaic applications. But, availability of any energy resources depends on the topographic orientation of the region and temporal availability of the resources. In this context, the current work offers an approach for spatial variability analysis of the available solar energy resources at the selected southern region of India with the help of geostatistical models and time series studies to identify the availability of energy potential for the future urban energy applications. The work requires Meteosat satellite-derived datasets acquired from the National Renewable Energy Laboratory (NREL) data catalogue. After performing the geostatistical operations on the datasets, it classifies the selected region into five classes comprising “most”, “highly”, “moderately”, “marginally”, and “least” suitable region for solar energy resources. The results obtained from this research result specifies that the region has an annual average variability of DNI as 3.757 to 5.797 kWh/m2/day and GHI as 4.4 to 6.083 kWh/m2/day respectively and total availability at selected 56 urban locations having footprint area more 10 km2 over the selected southern region has average annual GHI as 690.051 GWh/m2 (total GHI of 38642.870 GW) and annual average DNI as 617.580 GWh/m2 (total DNI of 32523.653 GW). The research outcomes infer that the region has sufficient solar energy resource, and these can be harvested for urban solar photovoltaics or solar heating/thermal installations. The work will provide an ideal solution for sustainable energy resources towards the urban applications in the region along with infrastructural development with natural resource management.

Significance Assessment in the Application of Spatial Analytics

The breadth of spatial analytics relied on in geography is remarkable. These methods are used to gain insight, typically in the context of planning, management, decision making, and policymaking. The challenge is invariably establishing meaning in obtained findings, providing insights and knowledge. This is done, however, in very different ways depending on the spatial analytic approach, with statistical notions of significance a prevailing tool in assessment. This article reviews significance assessment approaches in the application of spatial analytics. Noteworthy in this review is that many approaches can be considered through the lens of sampling. In some cases, the underlying sample is a few or as small as one. There are methods, however, that are based on sampling that is comprehensive, involving an implicit or explicit enumeration of all possible outcomes. This suggests that significance assessment using certain methods accounts for the entire range of possibilities, whereas other methods draw inference from scant sampling. In addition to the sampling perspective, extrapolation as well as indirect accuracy and anecdotal measures of assessment are not uncommon. The comparative review of spatial analytic methods suggests that significance is assessed in many different ways, making meaning interpretation challenging.

An exploratory approach for urban data visualization and spatial analysis with a game engine

The extensive use of Information and Communication Technologies and the consequent unprecedented generation of data have radically transformed the way we understand cities today. The vision of smart cities considers technology as an enabling force for the emergence of new forms of intelligence and collaboration, enhancing, thus, the problem-solving capacity of the city. Despite the wide range of applications aiming to improve urban systems and city governance, urban planning processes are rarely informed by online platforms and data generated by them lack comprehensive data visualization approaches. This research introduces an exploratory approach to exploit urban data through interactive visualization and game design, as a way to facilitate the access and understanding of such data. A novel methodology is proposed, leveraging on spatial data as an input source which drives the generation of three-dimensional environments and interactive applications supported by game engines. More specifically, this research builds upon existing tools and methods for geoprocessing and spatial analysis and embeds them in a 3D environment that employs game design elements. Three indicative visualization scenarios are designed, developed, and implemented to showcase the dynamics and flexibility of the proposed methodology, based on the registry of an urban reporting application in Thessaloniki.

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its “pacemaker”, continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of “mismatched” data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.