Spatial Data Analysis Techniques Research Articles

SPATIAL ANALYSIS BETWEEN YOUTH AND ADULT EDUCATION AND LABOR MARKET REMUNERATION

This study analyzes the spatial distribution of migration from traditional high school to Youth and Adult Education (YAE) and its relationship with formal work remuneration. For this, the Exploratory Spatial Data Analysis (ESDA) technique was used through global and local analyses. The results of the global analysis indicate a negative spatial autocorrelation between migration from regular secondary education to YAE and formal work remuneration. The local analysis indicated that the Northeast had the highest proportion of municipalities in the cluster with high migration of individuals in the YAE and higher remuneration in the formal market, while the South region had the highest concentration of municipalities with a low proportion of municipalities in the cluster with low migration of individuals in the YAE and high remuneration in the formal job market. Therefore, it is concluded that the low quality of YAE teaching leads individuals to enter the formal job market with a lower pay.

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

A tutorial: Analyzing eye and head movements in virtual reality.

This tutorial provides instruction on how to use the eye tracking technology built into virtual reality (VR) headsets, emphasizing the analysis of head and eye movement data when an observer is situated in the center of an omnidirectional environment. We begin with a brief description of how VR eye movement research differs from previous forms of eye movement research, as well as identifying some outstanding gaps in the current literature. We then introduce the basic methodology used to collect VR eye movement data both in general and with regard to the specific data that we collected to illustrate different analytical approaches. We continue with an introduction of the foundational ideas regarding data analysis in VR, including frames of reference, how to map eye and head position, and event detection. In the next part, we introduce core head and eye data analyses focusing on determining where the head and eyes are directed. We then expand on what has been presented, introducing several novel spatial, spatio-temporal, and temporal head-eye data analysis techniques. We conclude with a reflection on what has been presented, and how the techniques introduced in this tutorial provide the scaffolding for extensions to more complex and dynamic VR environments.

The future of plant diversity within a Mediterranean endemism centre: Modelling the synergistic effects of climate and land-use change in Peloponnese, Greece

Climate- and land-use change stand as primary threats to terrestrial biodiversity. Yet, their synergistic impacts on species distributions remain poorly understood. To address this knowledge gap, we conducted the first-ever comprehensive species distribution analysis on an entire regional endemism centre within an eastern Mediterranean country, incorporating dynamic land-use/land-cover change data together with climate change scenarios. Specifically, we apply species distribution modelling and spatial data analysis techniques to compare the individual and synergistic effects of these environmental drivers on the endemic vascular flora of Peloponnese, focusing on potential range contractions, altitudinal shifts, and habitat fragmentation levels. Moreover, we identify fine-scale present and potential future endemism hotspots within our study area, incorporating taxonomic and phylogenetic information. Overall, we aim to enhance our current understanding of endemism patterns and contribute to the development of future-proof conservation strategies for safeguarding Greece's endangered endemic flora. The integration of land-use change projections with climate change yielded less severe impacts compared to the effects anticipated when considering climatic variables alone. Most taxa are expected to undergo significant range declines and nearly half might experience increased habitat fragmentation, due to the synergistic effects of climate- and land-use change. We identified endemism hotspots, which are concentrated in or along the main Peloponnesian mountain massifs. However, our predictions indicate that areas presently recognized as endemism hotspots will undergo a concerning area decline, across all future scenarios considered in this study. Our findings highlight the importance of including dynamic land-use variables alongside climatic predictors when projecting species distributions under global change. Moreover, we showed that endemism hotspots are not static and considering their potential geographic shifts is paramount to delineate effective forward-looking conservation strategies.

The evaluation and optimization of the agricultural sustainable development based on a data-driven approach: A case from Northern Anhui

The sustainable advancement of agriculture stands as the fundamental cornerstone of sustainable human progress. This study introduces a data-centric methodological framework founded upon the holistic delineation of measurement, feature assessment, and pathway enhancement for agricultural sustainability. Initially, the research articulates a comprehensive evaluative schema incorporating sub-dimensions encompassing agricultural production, agricultural economics, the agricultural resource environment, and rural society, grounded in sustainable development theory. Subsequently, it devises a methodological apparatus for assessing and enhancing sustainable development capabilities, employing entropy evaluation methods and exploratory spatial data analysis techniques. Employing North Anhui as a case study, the viability of this approach is substantiated. The empirical inquiry conducted within this article operationalizes comprehensive evaluation and explores pathways for optimizing agricultural sustainability, focusing on the period spanning 2011 to 2020 in Northern Anhui. The findings affirm the feasibility and efficacy of the data-driven approach. Recommendations derived from the empirical exploration of agricultural sustainability pathways at the local level offer valuable insights for governmental authorities and policymakers. This research endeavor could be extrapolated to other geographical locales worldwide, fostering innovative strides in the sustainable development of regional agriculture.

National or Regional Recruitment: “Market Area” of University Cities

The depopulation of eastern Portugal and concentration on the coast has been a longstanding process. Since 1973 new HEIs has been disseminated around the country, and nowadays, every Portuguese region holds a set of HEIs. However, those closer to the coast tend to have a higher demand than those more distant. It is not clear if each one has a kind of restricted market area, such as a spatial monopoly in its neighbouring area or a national recruitment for some of them. This paper is the first exercise of determination of these areas for the Portuguese public network of HEIs. We use a database from the Ministry of Education (DGEEC-Ministry of Education) with information of the pair, place of residence (municipality level) and respective HEI, for enrolled students. Through techniques of Exploratory Spatial Data Analysis (ESDA), we will look for clusters of municipalities that could be market areas for each “university city” considered. We also use fractional regression models that confirm the positive correlation between the chosen university city and the distance to the family home and belonging to the market area. Despite some overlaps, the market areas of the university cities look to be well defined.

Study on the historical landscape evolution of the core reserve of Guangfu Ancient City based on computer simulation

Abstract In the conservation of the historical landscape within the Guangfu Ancient City Core Protection Area, traditional descriptions via drawings and textual narratives often exhibit subjective biases. To address this, a theoretical model for the historical architectural landscape has been developed to objectively measure and characterize the spatial patterns and morphologies of the landscape. This study introduces Exploratory Spatial Data Analysis (ESDA) techniques, utilizing spatial weights and spatial correlation measures as primary analytical tools. Additionally, meta-cellular automata are employed to simulate the spatial and temporal evolution of the historical landscape. Geographical Information Systems (GIS) and Remote Sensing (RS) technologies have been leveraged to establish a spatial database and facilitate the visualization of the historical landscape’s evolutionary processes. This integrated model and technological approach enable a detailed analysis of the architectural landscape characteristics and the spatial structure’s evolution within the study area. The findings reveal distinct spatial distribution characteristics, with an undulation index ranging from 5 to 20 and a predominant index over 7, accounting for 15.67% of the land. Based on these insights, the study proposes strategic interventions to safeguard the historical integrity of Guangfu Ancient City’s core conservation area.

Mapping and modelling the main determinants of Migration Flows at the NUTS2 Level in European Union using Spatial Data Analysis Techniques

Abstract Spatial data analysis approaches have grown in popularity in recent years as there has been an increased interest in studying migration at the regional level. We employed in our analysis the Nomenclature of Territorial Units for Statistics (NUTS) level, which offers a more thorough view of migratory patterns inside a nation as well as among EU countries.Using spatial data analytic tools, this research aims to investigate the key determinants of international migration at the NUTS2 level. Using data from the Eurostat database, we undertake an extensive analysis of migration movements in a sample of European Union countries. We apply spatial econometric models to examine the relationship between crude rate migration and other relevant variables as independent variables. Although the available data at the NUTS2 level of regional ranking are limited, we believe that we included the most comprehensive set of relevant variables in our analysis.Our study contributes to the existing literature on migration determinants by highlighting the importance of spatial analysis techniques in understanding migration factors at the NUTS2 level.

The spatial patterns of Airbnb offers, hotels and attractions: are professional hosts taking over cities?

ABSTRACT Airbnb has created a significant incentive to invest in real-estate with the purpose of short-term accommodation provision in touristic cities. The robust growth of Airbnb supply has been accompanied with an increasing professionalization of the platform, contributing to such challenges as overtourism and gentrification. The main aim of this analysis is to examine the spatial characteristics of Airbnb supply in Barcelona, Berlin and London, focussing on the differences between offers that belong to non-professional and professional hosts. Based on Exploratory Spatial Data Analysis techniques the study demonstrates that listings that belong to professional hosts are more concentrated in city centres, and are more influenced by the location of tourist attractions and hotels than non-professional offers. On the other hand, non-professional listings facilitate a spatially more diverse accommodation supply. The results suggest that professional offers may contribute more to the negative effects of Airbnb on local residents.

Supply–Demand Imbalance in School Land: An Eigenvector Spatial Filtering Approach

The spatial flows of school-age children and educational resources have been driven by such factors as regional differences in population migration and the uneven development of the education quality and living standards of residents in urban and rural areas. This phenomenon further leads to a supply–demand imbalance between the area of school land and the number of school-age children in the geographical location of China. The georeferenced data characterizing supply–demand imbalance presents an obvious spatial autocorrelation. Therefore, a spatial data analysis technique named the Eigenvector Spatial Filtering (ESF) approach was employed to identify the driving factors of the supply–demand imbalance of school land. The eigenvectors generated by the geographical coordinates of all primary schools were selected and added into the ESF model to filter the spatial autocorrelation of the datasets to identify the driving factors of the supply–demand imbalance. To verify the performance of the technique, it was applied to a county in the southwest of Shandong Province, China. The results from this study showed that all the georeferenced indicators representing population migration and education quality were statistically significant, but no indicator of the living standards of residents showed statistical significance. The eigenvector spatial filtering approach can effectively filter out the positive spatial autocorrelation of the datasets. The findings of this research suggest that a sustainable school-land-allocation scheme should consider population migration and the possible preference for high-quality education.

Flexible-Elliptical Spatial Scan Method

The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques.

SUICIDE AND COVID-19: A TEMPORAL-SPATIAL ANALYSIS IN THE STATE OF PARANÁ AND THE 15th HEALTH REGIONAL FROM 2010 TO 2020

IntroductionSuicide occurs throughout all the life course, but in 2019 it was the fourth leading cause of death among 15-29 years old worldwide.In Brazil, between 2010 and 2019, 112,230 deaths from suicide were recorded, with a 43% increase in the annual number of deaths from the beginning to the end of that period in all age groups. (SAÚDE, 2021).ObjectivesTo accomplish a space-time analysis of the rate of suicide deaths in the state of Paraná and in its 15th Health Regional, from 2010 to 2020, in the population aged 20 to 69 years, contrasting the pre-pandemic periods (2010 to 2019) and during the COVID-19 pandemic (2020).MethodsThis is an ecological, observational, cross-sectional, retrospective study, using spatial and temporal analysis tools, based on secondary data obtained from the Department of Informatics of the Unified Health System (DATASUS), in the period 2010 and 2020, referring to the 5568 Brazilian cities, with emphasis on the 399 of the state of Paraná and the 30 cities of its 15th Health Regional (HR).Exploratory Spatial Data Analysis (AEDE) techniques were used through GeoDA (version 1.20.0.8) and QGis (version 3.22.4) software to determine the existence of Spatial Autocorrelation (Moran’s Global Index or Moran I) and to calculation and visualization of the Local Spatial Association Indicator (LISA).Global (Moran I) and local (LISA) spatial autocorrelation coefficients were considered significant when p < 0.05 at a 95% confidence level.The Space Time Cube is a simultaneous spatial and temporal analysis methodology of ArcGIS Pro Software (ESRI, 2011) that evaluates the behavior of a value in space over a defined time interval.ResultsThe arithmetic mean of the age at death by suicide, from 2010 to 2020, was 41.1 years, with a standard deviation of 13.3 years and a median of 40 years. Most deaths in the pre-pandemic period occurred among men aged 20 to 29, followed by women aged 30 to 39 years and men aged 40 to 49 years.Regarding the most common causes and modes of suicide in the State, there was no significant difference between the periods from 2010 to 2019, pre-pandemic reference, and 2020 (pandemic) in a global perspective. The three main causes of death were: 1) Hanging, strangulation or suffocation (ICD-10 X70, T71) were the majority and accounted for more than half of all causes; 2) Self-harm by gunshot (ICD- 10 X74 and X72); 3) Self-poisoning by pesticides and drugs, respectively (ICD-10 X68 and X61).ConclusionsThis study showed an increase in suicide deaths in the state of Paraná, when analyzing the periods before and during the COVID-19 pandemic. The most affected population was men, aged 20 to 29 years.Disclosure of InterestNone Declared

Spatial-Temporal Changes in Water Supply and Demand in the Citarum Watershed, West Java, Indonesia Using a Geospatial Approach

Balancing water supply demand is vital for sustaining livelihoods. Spatial mapping and calculating water yield dynamics due to land use changes over decades are needed to manage land resources and formulate ecological protection policies. This study mapped the supply, demand, and matching status of water product service using the Integrated Valuation of Ecosystem Service and Tradeoff (InVEST) biophysical models in the Citarum Watershed (CW) in 2000, 2010, and 2020. Moreover, this study used Exploratory Spatial Data Analysis (ESDA) and Geographic Information System (GIS) techniques to study the agglomeration characteristics and evolutionary trajectories of supply–demand over two decades. The results showed that between 2000–2010 and 2010–2020, the water supply decreased by 19.01 × 108 m3 (18.28%) and 12.97 × 108 m3 (15.27%), respectively. However, the water demand in the same period increased by 6.17 × 108 m3 (23%) and 15.74 × 108 m3 (47%), respectively. Over the decades, the contribution of land use land cover (LULC) changes to variations in water supply has yielded values ranging from 2.87% to 6.37%. The analysis of the water supply–demand imbalance indicated that the entire CW experienced water shortage, and the type of spatial matching for supply and demand is dominated by a high supply and high demand class (16.09% of the total area). Based on the level of water deficit calculation, the upstream and downstream areas were identified as zones that require ecological conservation, while the middle CW area requires ecological restoration or ecological improvement.

Homicides in Alagoas - Brazil: A spatial analysis

This paper aimed to identify the main factors that influence homicides in the state of Alagoas, Brazil. Therefore, we employed the Exploratory Spatial Data Analysis (ESDA) technique and estimated spatial models in order to fix spatial autocorrelation. Although several articles have explored this theme for other states in Brazil, we chose Alagoas, a small state in northeastern Brazil, since it lead the homicide ranking per 100 thousand inhabitants in 2010, at the time of the national census. The main results show that the higher the income concentration, the greater the number of poor and the lower the educational level, the higher the homicide rate.

Exploratory Analysis of Regional Disparities in Human Development in Cameroon

The objective of this paper is to analyze the link between geographic location and the level of human development in Cameroon. To achieve that objective, we use Exploratory Spatial Data Analysis (ESDA) techniques on regional human development indices (HDI) in Cameroon from 2001 to 2014. The Moran test confirms the presence of a positive and significant global spatial autocorrelation of the regional human development indices. Thus, the values of the regional human development index in Cameroon are not randomly distributed. There is thus spatial clustering of regions with similar HDI levels. The spatial dependence of the regional HDI in Cameroon suggests cooperation and synergy of action by the regions within the framework of their competences resulting from decentralization, in order to take advantage of the spatial diffusion effects of human development. The Cameroonian government could pursue geo-targeting policies in the implementation of development programs, focusing on regions where spatial externalities would have a greater impact.

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis.

Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies.

Exploring multi-scale variability in hotspot mapping: A case study on housing prices and crime occurrences in Heerlen

This paper examines the use of varying scaling in hotspot mapping as an exploratory spatial data analysis technique. The ambiguous geographic relationships between different crime types and housing prices are used to highlight the necessity of the multi-scale approach in effective pattern recognition. Hotspot maps were created using crime and housing price data from the city of Heerlen, The Netherlands. First, insights generated from observing the hotspot maps suggest a consistent spatial pattern of all crime types except home burglaries. By analyzing these relationships further with correlation analysis, it was found that all crime types except home burglaries have a persistent negative relationship with housing prices. The relationship between home burglaries and housing prices was found to differ in direction and significance depending on location and scale of the hotspot maps. A method for fine-tuning the scale is developed and presented. The fine-tuned results enrich hotspot mapping and highlight the need to consider scale variability properties in spatial data analysis.

Geospatial Clustering of Mobile Phone Use and Tuberculosis Health Outcomes Among African Health Systems.

BackgroundWhile multiple studies have documented the impacts of mobile phone use on TB health outcomes for varied settings, it is not immediately clear what the spatial patterns of TB treatment completion rates among African countries are. This paper used Exploratory Spatial Data Analysis (ESDA) techniques to explore the clustering spatial patterns of TB treatment completion rates in 53 African countries and also their relationships with mobile phone use. Using an ESDA approach to identify countries with low TB treatment completion rates and reduced mobile phone use is the first step toward addressing issues related to poor TB outcomes.MethodsTB notifications and treatment data from 2000 through 2015 that were obtained from the World Bank database were used to illustrate a descriptive epidemiology of TB treatment completion rates among African health systems. Spatial clustering patterns of TB treatment completion rates were assessed using differential local Moran's I techniques, and local spatial analytics was performed using local Moran's I tests. Relationships between TB treatment completion rates and mobile phone use were evaluated using ESDA approach.ResultSpatial autocorrelation patterns generated were consistent with Low-Low and High-Low cluster patterns, and they were significant at different p-values. Algeria and Senegal had significant clusters across the study periods, while Democratic Republic of Congo, Niger, South Africa, and Cameroon had significant clusters in at least two time-periods. ESDA identified statistically significant associations between TB treatment completion rates and mobile phone use. Countries with higher rates of mobile phone use showed higher TB treatment completion rates overall, indicating enhanced program uptake (p < 0.05).ConclusionStudy findings provide systematic evidence to inform policy regarding investments in the use of mHealth to optimize TB health outcomes. African governments should identify turnaround strategies to strengthen mHealth technologies and improve outcomes.

Selection of suitable parking lot sites in megacities: A case study for four districts of Istanbul

Parking is one of the biggest challenges faced by megacities experiencing rapid growth. With more than 15 million population and approximately 3 million passenger cars, Istanbul is a megacity having a considerable parking problem. Sustainable planning of the parking lots in the city requires smarter spatial decision making approaches to use limited land resources efficiently. In order to determine the optimum locations for parking supply in the case of four districts of Istanbul, a GIS-based fuzzy analytical hierarchy process (AHP) approach is employed. In this context, the relevant criteria are identified and collected under three main categories: (1) land use, (2) finance and (3) transportation. Raster grid layers are created in GIS for those criteria through spatial data processing and analysis techniques. Those layers are then combined after being weighted with the fuzzy AHP approach. Thus, the scores of all grid cells in the study area are obtained and then transferred to the land parcels to rank them by their suitability. Besides, the parcels are also evaluated with respect to shape and area. Finally, those assigned to the highest ranking category as well as having proper shape and area are recognized as the feasible parcels for parking lot generation and prioritized according to those factors. The findings showed that the GIS-based fuzzy AHP approach applied in this study enabled multi-criteria investigation of each location (i.e. grid cell) in the study area based on highly detailed spatial data and could therefore be used to determine the feasible parcels for parking lot generation. Among the main criteria, transportation had the greatest weight while finance had the least weight. According to the case study, the need for parking and the number of feasible parcels are greater in Uskudar than the other three districts.

Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai

Taking Shanghai as an example, this paper uses remote sensing (RS) and geographical information systems (GIS) technology to conduct multisource data fusion and a spatial pattern analysis of urban carrying capacity at the micro scale. The main conclusions are as follows: (1) based on the “production, living and ecology” land functions framework and land use data, Shanghai is divided into seven types of urban spaces to reveal their heterogeneity and compatibility in terms of land use functions. (2) We propose an urban carrying capacity coupling model (UCCCM) based on multisource data. The model incorporates threshold and saturation effects, which improve its power to explain urban carrying capacity. (3) Using the exploratory spatial data analysis (ESDA) technique, this paper studies the spatial pattern of carrying capacity in different urban spaces of Shanghai. (4) We analyse the causes of the cold spots in each urban space and propose strategies to improve the urban carrying capacity according to local conditions.