Exploratory Spatial Data Analysis Research Articles

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.

Spatial Pattern and Spillover Effects of the Urban Land Green Use Efficiency for the Lanzhou-Xining Urban Agglomeration of the Yellow River Basin

Green development is necessary for building high-quality modern economic systems. Due to limited land resources, ensuring the ecological benefits will not be damaged while developing the economy is necessary, as is improving urban land green use efficiency (ULGUE). Lanzhou-Xining Urban Agglomeration is a crucial urban agglomeration in the upper reaches of the Yellow River Basin. The improvement of ULGUE can promote the integrated management of the region’s land and achieve harmonious development with the social economy. This study adopts a super-efficiency SBM model to quantify the ULGUE of the Lanzhou-Xining urban agglomeration between 2015 and 2020, analysing its spatial and temporal patterns by combining exploratory spatial data analysis. The spatial spillover effect was discussed using the spatial Durbin model. The results showed that the average value of ULGUE in the study period was on the rise and showed positive spatial autocorrelation. The ULGUE of provincial capital cities was higher than non, along with the efficiency of city proper, compared to other counties. Many factors were found to positively influence the ULGUE of the whole study region. Among these were the optimisation of industrial structure, economic development, urbanisation level, and government intervention. Population concentration and environmental regulation had a negative spatial spillover effect.

Slope characteristics of urban construction land and its correlation with ground slope in China

Abstract Since the 21st century, China’s urban construction land has been growing rapidly, piquing academic interest. However, mountainous counties account for the majority in China, previous studies have concentrated on the horizontal expansion characteristics of construction land, leaving a gap in the vertical expansion. This study used datasets for urban construction land and digital elevation model to analyze the spatiotemporal evolution characteristics of construction land slope in 2670 China’s counties, by exploratory spatial data analysis. Furtherly, we explored the slope relationship between ground and construction land using regression analysis. The findings indicate what follows: (1) The average slope of construction land had a spatial pattern of “high in the south and low in the north,” with significant spatial agglomeration characteristics. And it had increased with the urban expansion, shifting the slope-weighted mean center toward the southeast while enhancing spatial agglomeration. (2) There was a significant and steadily rising linear association between the urban construction land slope and ground slope, due to spatial heterogeneity; the most affected counties were primarily located near the Sichuan basin. According to the results, we provided suggestions for the rational use and sustainable development of land in cities, especially for mountainous regions in a period of rapid urbanization.

A Longitudinal Spatial-Temporal Analysis of Ancient Village Tourism Development in Zhejiang, China

The sustainable development of tourism is essential for revitalizing historically and culturally significant ancient villages in China. Despite the longstanding recognition of the relationship between the spatial distribution of village destinations and their sustainable development, there is a dearth of longitudinal studies in village tourism. Using the geographic information system (GIS) spatial analysis method and the exploratory spatial data analysis model, this study explored the spatial-temporal features of ancient village tourism over three important time nodes of rural tourism development (in Zhejiang, China), as well as the contributing factors at both the provincial and prefectural city levels. The findings of this study suggested a spatial inequality in the distribution of ancient villages, in terms of tourism development over time. In particular, tourism development was clustered in the southern region, with a noticeable spillover effect. Meanwhile, transportation, source markets, and physical topography are essential factors contributing to this spatial distribution. The study contributes to ancient village tourism development literature and offers meaningful, practical implications for regional governments and business investors.

Coordinated Development of Farmland Transfer and Labor Migration in China: Spatio-Temporal Evolution and Driving Factors

The coordinated development of farmland transfer (FT) and labor migration (LM) is of great efficiency significance to facilitate the development of rural economy and implement the rural revitalization strategy. The study used socioeconomic data from 30 provinces/autonomous regions/municipalities (hereafter referred to as provinces) in China to measure the coupling coordination degree (CCD) of FT and LM. It adopted the coupling coordination degree model (CCDM), exploratory spatial data analysis method (ESDA), and gray relational analysis model (GARM) to investigate the spatial differences in the CCD and its influencing factors. The results indicate the following: (1) Regional differences are evident despite the fact that the comprehensive evaluation level of FT and LM in the various provinces is relatively low and displaying a rising trend. (2) The CCD of FT and LM exhibits a fluctuating upward trend and is at the primary coupling coordination stage, with a significant difference in coupling coordination levels between regions, and a spatial distribution pattern of central region > eastern region > northeast region > western region. (3) The CCD shows a strong global spatial positive correlation with clear fluctuations, demonstrating the agglomeration dispersion development tendency over time; the local spatial agglomeration state emerges and stabilizes. According to the distribution pattern, the Western region exhibits weak agglomeration type, whereas the eastern and central regions exhibit strong agglomeration type. (4) There are significant variations between provinces in terms of the intensity of the CCD of FT and LM, as well as the level of concurrent employment business, the level of non-agricultural industry development, the level of urbanization, the level of agricultural equipment, and the land approval.

Spatial Distribution of Airbnb Supply in Los Angeles

Following the growing popularity of Airbnb, much research has been conducted, while limited attention has been devoted to the spatial patterns of Airbnb, which can provide insights into the geographic logistics of Airbnb supply. To fill this gap, this study investigates the spatial patterns of Airbnb supply in the city of Los Angeles during the years 2014 to 2019. This study integrates exploratory spatial data analysis (ESDA) and explores the determinants of spatial patterns of Airbnb supply in 2019 with spatial regression models. The results of the spatial autocorrelation analysis show that the spatial distribution of Airbnb supply in Los Angeles is positively clustered and shows a geographical logistical trend over time. The distribution of Airbnb supply has a clear center–periphery pattern. The high–high (HH) clusters are concentrated in the central areas of Los Angeles, while low–low (LL) clusters predominate in the peripheral areas. In addition, with spatial regression analysis, several determinants affecting the spatial distribution of Airbnb supply are identified such as resident population, housing units, income, rent, point of interest (POI), and the distance to Hollywood. Further implications of the findings are discussed below.

The Role of Neighborhoods in the Age of Crime Hot Spots: A Multi-Level Theory of the Spatial Distribution of Robbery in Chicago

This study observes the spatial distribution of robbery using a multi-level framework to further understand the unique contribution of both micro-places and neighborhoods to the spatial variability of crime patterns. Mixed-effects regression models were estimated to determine if the influence of spatial interdependence measured with a spatial lag variable at street segments had a random effect between Chicago’s community areas. The variance parameters guided an exploratory spatial data analysis which revealed clusters of robbery patterns which were larger than crime hot spots and often nested within neighborhoods. We found there is an important distinction between places in Chicago where the spatial transmissibility of robberies is most acutely experienced across entire neighborhoods or within the immediate proximity of certain micro-places.

The Coupling Coordination Degree and Spatial Correlation Analysis of the Digital Economy and Sports Industry in China

Driven by a new round of scientific and technological revolution and industrial transformation, the interactive and integrated development of the digital economy and the sports industry has become the objective demand of current economic development. In order to explore the coupling coordination relationship between the digital economy and the sports industry, this paper uses the coupling coordination degree model and exploratory spatial data analysis method to analyze the coupling coordination degree and spatial correlation of the integrated development of China’s digital economy and its sports industry. It does this by taking the statistical data of 30 provinces, autonomous regions and municipalities in China from 2015 to 2019 as observation samples. The results show that the coupling coordination degree of China’s digital economy and sports industry is low, showing an unbalanced development trend of “low in the west, high in the east, low in the north and high in the south”. There is a significant positive clustering in spatial integration of the two, and the degree of clustering increases year by year. It is necessary to establish a regional coordinated development mechanism, define the stage of regional integration, optimize the spatial pattern of coupled development, and promote the balanced development of digital economy and sports industry.

Social status and air quality in Barcelona: A socio-ecological approach

• There is a relationship between higher building density and higher air pollution. • There is an inverse relationship between the isolated street trees and less pollution. • The areas with higher income level are related with the worse the air quality • The GWR models show a more complete view that OLS models for the air quality studies. During the last two decades, many studies have described the effects of air pollution in urban areas. This paper analyses the spatial patterns of the main air pollutants in the city of Barcelona, i.e. nitrogen dioxide (NO 2 ), fine particulates of less than 2.5μm (PM 2.5 ) and coarse particulates of 10μm (PM10), and explores the factors associated with air quality in relation to the social and environmental characteristics of each area of the city using 1068 census tracts. The city exhibits a situation of high annual averages of pollutants regarding the World Health Organization (WHO) guidelines and the European Union regulations, with most of the census tracts with amounts of NO 2 >20 µg/m 3 , PM 2.5 >10 µg/m 3 and PM 10 >20 µg/m 3 . To analyse the spatial pattern, we use exploratory spatial data analysis (ESDA) and estimation of spatial econometric models. We test global and local spatial autocorrelation, and we identify spatial clusters of pollutants with the Local Moran cluster map. We use different geographically weighted regression (GWR) approaches to provide local insight and to consider the spatial heterogeneity of the relationships analysed, including standard GWR, multiscale and mixed techniques. Our results for Barcelona reveal a clear relationship, but with opposite signs depending on the census tract, between the pollutants commonly analysed and population density (from -2.17 to 3.69, median 1.3), income (from -7.20 to 20.55, median 6.45) and population aged 0-4 (from -1.96 to 12.38, median 0.74).

Spatial Relationship between the Residential Land Price and the Land Intensive use in Hebei Province

Using the exploratory spatial data analysis method to analyze the level of residential land price, and calculate the use level of urban land intensive use based on the PSR model, exploring the differences between the urban residential land price and land intensive spatial in the city of Hebei Province in 2019 and make relevant suggestions. The result shows (1) The land price level of urban housing is characterized by spatial differentiation of circle structure with Langfang city as the center and decreasing outward. (2) The overall level of urban land intensive use shows the spatial differentiation characteristics of high in the southeast and low in the northwest. (3) The spatial distribution of urban residential land price level is different from that of land intensive use level, and there is a situation that the land price level is high and the urban land intensive use level is low. (4) Because of the shortcomings in the development of land price and intensive land use, some suggestions are put forward from the overall perspective of the whole province and the specific situation of each city.

Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis

Urban environmental quality consisting of ecological, physical, and socio-economic components, often deteriorates due to rapid urbanization. Therefore, using Remote sensing and GIS environment, a composite measure is applied to quantify the spatial heterogeneity of urban environmental quality for the Class-1 Indian city (Siliguri). In this study, the Urban Environmental Quality Index was constructed using 15 indicators and three interconnected dimensions (eco-environment, landscape and built-up, and socio-economy). The three domains and Urban Environmental Quality Index were computed utilizing Principal Component Analysis with average aggregation techniques. Exploratory Spatial Data Analysis includes Moran’s I and Local indicator of spatial auto-correlation, were used to leverage the information of spatial clusters, spatial heterogeneity, and outliers based on the Urban Environmental Quality Index. The results show that Siliguri’s northern, north-western, and southern parts experience good environmental quality. The effectiveness of the employed model was checked using R2 (0.832), providing a good fit for the model. Moreover, the spatial pattern of urban environmental quality and the constructed domains (except socio-economy) revealed that the Low-Low values were predominantly clustered in the city centre, while High-High patterns are concentrated towards the periphery. Also, the value of Moran’s I indicated the existence of spatial autocorrelation and non-randomness pattern in Siliguri City. The results obtained from the analysis indicate spatial heterogeneity and spatial differentiation across the study area. The study’s outcome is relevant for urban planning, frequent monitoring of urban environmental quality, urban governance, and the well-being of urban inhabitants for a sustainable urban space.

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

The quality of urban spaces is a pivotal part of high-quality spatial development. It is directly connected to the comprehensive, coordinated and sustainable development of a region. In recent years, Northeast China has characterized urban space contraction and development. To study the quality of urban space in Northeast China, this paper fitted the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data with 11 indicators related to high-quality urban development for the period 1992–2018. The feasibility of nighttime light data reflecting urban spatial quality was verified by a linear equation, and the temporal characteristics of urban spatial quality in Northeast China were obtained. The Exploratory Spatial Data Analysis Geographically and Temporally Weighted Regression (ESDA-GTWR) explores the spatial relevance and possible influencing factors of this kind of development. The results suggest that the overall trend of spatial quality in the three northeastern provinces is “initial slow growth and significantly weakened after”. The fast developing cities include Panjin, Liaoyang, Shenyang, and Dalian in the Liaoning Province. On the other hand, cities such as Heihe and Yichun in the Heilongjiang Province have relatively slow development speeds. Furthermore, the spatial quality development in the three northeastern provinces exhibits a trend of continuous concentration. The cities with high spatial qualities are concentrated near the Liaoning Province, with low spatial qualities in the north and high spatial qualities in the southern parts of the three provinces. As there is a notable gap between the northern and the southern regions, the central region represents an area in partial transition. The spatial quality of each city in the three northeastern provinces is the result of a number of intertwined factors, with significant differences in the degree of their influence. The significant degree of influence factors on spatial quality from higher to lower is urbanization, quality of life, rural revitalization, government promotion, and infrastructure.

The Space–Time Evolution of the Coupling and Coordinated Development of Public Cultural Services and Cultural Industries: A Case Study of 31 Regions in China

The coordinated development of public cultural services (PCS) and cultural industries (CI) is conducive to sustainable regional development. Few studies focus on the coordinated development of PCS and CI. This study builds an evaluation index system for the coordinated development of PCS and CI and uses the entropy weight method, synchronous development model, coupling coordination model, and exploratory spatial data analysis method on 31 regions in China. It evaluates the synchronous development levels, coupling coordination levels, and spatial evolution characteristics of the coordinated development of PCS and CI from 2011 to 2020. The results are as follows: (1) The coordinated development level of PCS and CI has risen steadily. The leading type is dominant in space, with the leading type > synchronous type > lagging type. (2) The overall coupling degree is high. The high-level coupling in space is dominant. (3) The degree of coupling coordination has improved. Regions with good spatial and high-quality coordination are concentrated in the eastern, central, and coastal areas. (4) The coordinated development of PCS and CI shows positive spatial correlation agglomeration. The degree of agglomeration is increasing, mainly high high, forming an important growth region in the Yangtze River Delta economic region and its surroundings. Finally, we propose policy recommendations for the coordinated development of PCS and CI, and provide new ideas for the collaborative development of PCS and CI in China.

Spatial Distribution of Canine Sporotrichosis in Rio de Janeiro, Brazil (1998-2018) and Its Correlation with Socioeconomic Conditions.

Canine sporotrichosis is a poorly described global disease and a spatial approach has not yet been used to assess the disease in dogs. Therefore, this study aimed to describe the occurrence of canine sporotrichosis in the state of Rio de Janeiro, Brazil, from 1998 to 2018 and its correlation with socioeconomic characteristics using exploratory spatial data analysis. A total of 295 cases of canine sporotrichosis were identified and 249 were georeferenced. There was a higher concentration of cases in the municipality of Rio de Janeiro, as well as along the border of the city and the adjacent municipalities in the great metropolitan area. The cases occurred in areas where most of the dwellings are houses. Moreover, no focus of disease density was found in the southern part of Rio de Janeiro city over the period studied, possibly due to better socioeconomic conditions. Areas with a high concentration of canine sporotrichosis cases coincided with regions that possessed a low proportion of households without paving, suggesting that the disease is not necessarily linked to extreme poverty. The mapping of areas with a greater density of cases is fundamental to formulate targeted and strategic plans in order to implement effective public health prevention and control measures.

HUMAN DEVELOPMENT AND GOVERNANCE IN AFRICA

This study examines the nexus between governance and human development in Africa. It uses data for the period 2010–2019 and takes into account the existence of spatial dependence and controls for the endogeneity problem through a Generalized Spatial Two Stage Least Squares (2SLS) technique. The exploratory spatial data analysis reveals the existence of spatial dependence on human development and governance quality. Our empirical findings support that in Africa, “good fences make good neighbors,” or proximity matters in the distribution of human development. Implications are discussed. This study complements the extant literature by using more updated data and employing an alternative and more robust estimation approach.

Geo-environmental and socioeconomic determinants of poverty in China: an empirical analysis based on stratified poverty theory.

According to the stratified poverty theory, poverty includes individual (people) and regional (place) poverty. Understanding the interaction mechanism between individual poverty and regional poverty is crucial to achieving the UN goal of poverty eradication by 2030. However, at present, the relevant empirical research is still limited by the availability of data. To fill this important gap, based on the multi-source data of poverty census, geo-environmental and socio-economic data of China's 1587 counties in 2013, we used exploratory spatial data analysis (ESDA) and spatial econometric models (spatial-lag and spatial-error model) to identify determinants of individual poverty and regional poverty in this county. Results show that the spatial distribution of the rural poor in China had strong spatial dependence (Global Moran's I = 0.574). There was a high degree of spatial overlap between individual poverty and regional poverty. The poverty-causing factors were complex and varied across regions and individuals. Disease of family members was the leading factor driving rural areas in Northeast, Central, and Southwest China. Northeast China was mainly affected by the illness and lack of labor skills of family members. The complex terrain conditions were the determinants driving rural poverty in most areas of China. Improved transportation can greatly reduce rural poverty. Geographical isolation or lack of geographical capital caused by complex terrain conditions, backward transportation, and regional closure promoted regional poverty. In turn, regional poverty-causing factors further restricted the improvement of rural residents' self-development ability and aggravated individual poverty. Our findings indicate that individual poverty and regional poverty havedifferent poverty-causing mechanisms and poverty reduction priorities. Effective poverty reduction strategiesrequire the coordinated promotion of individual and regional povertyreduction. The reduction of individual poverty should focus on enhancing the livelihood capital of the poorthrough differentiated policy intervention, while regional poverty alleviationshould focus on creatingafavorabledevelopment environment by increasing infrastructure investment and public service supply.

Regional disparities and evolution trend of city-level carbon emission intensity in China

Carbon emission intensity (CEI) plays a crucial role in promoting urban green economy transition, which has attracted strong attention from scholars and society. However, the regional disparities and evolution trend of city-level CEI have not been yet fully investigated. This paper addresses this gap by exploring the regional disparities and evolution trend of China's city-level CEI from 2000 to 2017, based on nighttime light data and the methods of Dagum Gini coefficient, exploratory spatial data analysis (ESDA), kernel density estimation (KDE), and spatial Markov chains. The research results revealed that China's city-level CEI showed an obvious decline trend from 3.677 kgCO2/USD in 2000 to 1.696 kgCO2/USD in 2017. There are evident regional disparities of China's city-level CEI, and inter-regional disparities are the main source. China's city-level CEI shows a positive spatial autocorrelation, and there are obvious differences in the distribution rules of cities with different spatial agglomeration forms. Moreover, China's city-level CEI presents obvious polarization and siphon effect. This paper therefore formulates the differentiated urban green economy transition strategies based on the regional disparities and evolution trend of China's city-level CEI.

Incidence of dog bite injuries and its associated factors in Punjab province of Pakistan

Dog bites are a major cause for transmission of rabies virus to humans. Pakistan ranks fifth among most rabies affected countries in the world. There are a few regional (ecological) studies that investigated factors that explain geographic disparities in incidence of dog bite injuries. The main objective of this research was to document findings of spatial exploratory data analysis of incidence of reported cases of dog bite in Punjab province of Pakistan (2016–2019). In addition, we have quantified the association between incidence of dog bites and a set of selected socio-economic and demographic variables. District-wise data about reported cases of dog bites from 2016 to 2019 were used to map annual crude incidence per 100,000 of population. There was an obvious spatial variation in incidence of dog bites but there was no evidence of spatial autocorrelation. The risk of dog bite attacks was relatively higher in districts with low human population density (per sq. km), poor literacy rate, more rural population (% of total population), and lower median nighttime lights.

The contribution of tourism mobility to tourism economic growth in China.

Mobility is the key factor in promoting tourism economic growth (TEG), and the transportation infrastructure has essential functions for maintaining an orderly flow of tourists. Based on the theory of fluid mechanics, we put forward the indicator of tourism mobility (TM). This study is the first to measure the level of TM in China and analyze the spatiotemporal evolution characteristics of TM. Applying the Exploratory Spatial Data Analysis method, we analyze the global and local spatial correlation characteristics of TM. Moreover, we further estimate the contribution of TM to TEG by econometric models and the LMDI method. The results show that (1) the TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. The TM in each region only showed a significant positive spatial correlation in 2016-2018. The space-time pattern is constantly changing over time. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces. (2) The regression results of the traditional panel data model and spatial panel data model both show that TM has a significant positive effect on TEG. Moreover, TM has a negative spatial spillover effect on neighboring regions. (3) The result from the decomposition of LMDI shows that the overall contribution of TM to TEG is 15.76%. This shows that improving TM is a crucial way to promote the economic growth of tourism.

Spatial distribution characteristics and accessibility analysis of beautiful leisure villages in China.

This study was designed to evaluate the spatial distribution characteristics of 1432 beautiful leisure villages in China using econometric geography and spatial geographic information system analysis methods, such as nearest distance index, K index, and nuclear density. We also used the grid cost weighted distance algorithm to determine the spatial accessibility of beautiful leisure villages and the overall accessibility of county units. In addition, our evaluations determined the spatial differences in county accessibility using exploratory spatial data analysis (ESDA). Our results showed that the spatial distribution of the beautiful leisure villages in China could be best described using the cohesion type classification and that there were large differences in their distribution between provinces and economic regions. The average accessibility time of beautiful leisure villages was 197.24 min with only 57.19% of these commutes being less than 2 h, and only 17.88% being less than 30 min. The area with the longest accessibility time was located on the Qinghai Tibet Plateau, at up to 1510.03 min. The spatial distribution of accessibility showed obvious traffic directivity producing a positive Moran I value for most counties. There was also a significant positive correlation between the accessibility of beautiful leisure villages and their adjacent areas, and clear patterns of hot spots–sub-hot spots–sub-cold spots—cold spots from east to west. The overall service scope of beautiful leisure villages was characterized by west > east > middle, with topography, population, economy, and location acting as the major factors in the spatial distribution of these beautiful leisure villages in China.