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A prerequisite for the rational development and utilization of regional water resources is the measurement of water stress. In this study, from the perspective of water footprints, we took the proportion of the agricultural water footprint within the total water resource usage of Xinjiang (hereafter referred to as XJ) as an example to measure its water stress index and explore the state of water stress in the region and its corresponding driving factors. The ESDA method was applied to characterize the spatial patterns of and correlations with water stress. The effects of different factors on the spatial differentiation between the water footprint and water stress were quantified using the LMDI and geoprobes, respectively. The results showed that (1) both the agricultural water footprint and the water stress index in XJ showed an upward trend, the spatial distribution of water stress was uneven, and the regional pressure difference between the east and the west was greater than that between the north and the south; (2) the water stress index has an obvious negative spatial correlation, fluctuations in its discrete nature have been enhanced, and the number of spatially correlated prefectures is decreasing; (3) water consumption of CNY 10,000 GDP, GDP per capita, and total CO2 emissions have the most significant impact on the evolution of the agricultural water footprint in XJ. Meanwhile, spatial variations in water stress are mainly determined by the area of cultivation, the area of natural oasis, and the proportion of water used in agriculture. Analysis of the characteristics of and factors influencing water stress in XJ from the perspective of its agricultural water footprint provides a new perspective for further analyzing the actual state of the water footprint and water stress in XJ and supplies a reference basis for the decision-makers of the XJ government. [ABSTRACT FROM AUTHOR]

Pakistan is spatially a diverse state in terms of location of its economic activities and regional disparities in various dimensions of development have been a vital concern in its history. The article aims to analyze the distribution of household welfare index across 97 districts of Pakistan through exploratory spatial data analysis for periods 2004-05 and 2014-15. For this purpose, an augmented Household Welfare index is constructed for measuring Household Welfare across districts. The index consists of consist of five indicators. Final index is obtained by aggregating these indicators through Principal Component Analysis (PCA). Findings of the study indicate positive global autocorrelation and thus indicating that a district with a high (low) is linked spatially with bordering districts which also have high (low) Household Welfare level. The results also display the HH quadrant in scatterplots of Household Welfare Index includes mostly districts of Punjab and KP, while LL illustrates a cluster of most districts from Interior Sindh and Baluchistan for both 2004-05 and 2014-15 periods. Overall, the findings demonstrate the twofold structure of Pakistan’s economic geography, as explained by most of the previous studies. Since geography of Household Welfare matters, it is recommended to reduce across districts inequalities by developing the social and economic institutions and infrastructure in the Baluchistan and interior Sindh. [ABSTRACT FROM AUTHOR]

Spatial analysis approach is capable of identifying spatial clustering of Infant Mortality Rate (IMR) across districts/cities in Indonesia. This information is essential for policymakers when developing appropriate health interventions at districts/cities level. At delivery, maternal age is one of the strongest predictors of infant mortality, which is closely related to child marriage. Therefore, this study aimed to determine the relationship between child marriage and IMR in Indonesia, which was controlled by the influence of maternal, household, and health factors. The results showed that there were spatial dependency on IMR in Indonesia. Analysis Regression Spatial Durbin Model showed that child marriage had a positive and significant relationship with IMR. Furthermore, it was discovered that IMR was influenced by several explanatory variables in districts and neighboring cities. To reduce mortality rate, there was a need to increase health services by developing neighboring areas and building access to areas with good health facilities. Educational initiatives are also important, emphasizing the importance of delaying the age of marriage and enforcing the minimum age limits. This approach empowered every child to marriage carefully and had good reproductive health knowledge that could decrease IMR in Indonesia.

Scholars have different observations and undertandings over the relationship between the economic development and environment protection, since the ''Environmental Kuznets Curve'' hypothesis has been raised. The integrated utilization of resources and the collaborative industrial development in North China have improved economic development, and caused nonnegligible environmental pollution simuatenously. Currently, North China is at the critical point to achieve industrial green development. This research analyzes the spatial and temporal distribution patterns and influencing factors of industrial wastewater discharge in North China by using exploratory spatial data analysis (ESDA) and spatial econometric model. The results show that the industrial wastewater discharge shows an inverted ''U'' shape in time, reaching a peak in 2010 and then fluctuating down; spatially, it shows a significant spatial aggregation, while the spatial divergence also appears. Industrial structure is an important factor in promoting wastewater discharge, while economic development has an obvious inhibitory effect on pollution, but the influence of technological progress is not obvious.

In the context of bank failure, the internet and development trend of financial businesses have notably influenced the banking industry. However, existing literature shows that few studies have quantitatively examined these influences on bank outlets, and the outcomes of studies on the impact of internet on the offline operation of banks have not been authoritatively verified; therefore, further exploration is required. In this context, this study analyzed the withdrawal of offline bank outlets and spatial distribution of bank withdrawals from 2019-2021 using kernel density analysis, geographical concentration index, and DBSCAN clustering algorithm and identified the comprehensive factors influencing bank withdrawals by combining these analyses with a spatial econometric model. This study determined the influences of various factors on the operation of offline financial banks under the background of information development. According to the results: 1) From 2019 to 2020, the withdrawn financial bank outlets revealed aggregated distribution overall, and the number of financial bank outlets that withdrew gradually declined from east to west; given the different conditions in terms of information network impact, different types of bank outlets reported various trends in these changes. 2) The financial institutions that withdrew had some common characteristics from single- and intra-city perspectives. Regarding single cities, In the three regions classified by the level of economic development, large urban agglomerations are the centralized regions where banks withdraw branches, forming the center–edge structure with core cities as the center. From the intracity perspective, more financial banks withdrew in regions with intensive capital, dense population, and developed technology. 3) In the context of mobile information, financial bank withdrawal was influenced by multiple factors. Overall, information environment exerts a strong influence. However, whether innovative ability is related to financial bank withdrawal remains unclear. In addition, the competition, labor, rent, and other operation costs and bank operation benefits from the external market substantially influenced the withdrawal of banks. In contrast to previous studies on the spatial expansion of bank outlets, this study discussed the development characteristics of financial bank withdrawal from the perspectives of time and multidimensional space and additionally considered financial geography, providing subsequent researchers with a reference and guidance. However, limited by data, this study only analyzed and discussed the data of from a 3-year period; the longer-term details of the study object were not summarized. In the future, more abundant data and broader methods will be used to conduct more detailed studies from richer perspectives.

High-quality development is an important part of tourism. This paper study the characteristic of spatial-temporal and the influencing factors of high-quality development level of tourism industry in China from 2006 to 2019 by means of entropy method, ESDA, Dagum Gini coefficient and panel regression model. The results show that: (1) During the study period, the average high-quality development level of China's tourism is 0. 327, and the average annual growth rate is 0. 796%. The overall level was relatively low, and the spatial distribution pattern was roughly "Eastern > Central > Western > Northeast". (2) The overall spatial correlation of the high-quality development of tourism is significant, and the local characteristics was "hot in the Southeast and cold in the Northwest". The overall Gini coefficient of high-quality develop‐ ment of tourism is fluctuate, and the inter-regional and intra-regional differences show a trend of convergence and contraction, and the inter-regional differences makes the greatest contribution to the overall differences. (3) At the national level, the informatization level and the scale of tourism talents are the main driving forces for the high-quality development of tourism. From the regional level, the high-quality development of tourism in Eastern China is the result of the joint promotion of the level of informatization, the level of opening up, the scale of tourism talents and tourism reception facilities. The Central region was driven by the improvement of informatization level, opening up level, transportation accessibility and tourism talent scale; The significant driving factors in Western China were the level of informatization and the scale of tourism talents. The positive leading factor in Northeast China was informatization level. Therefore, all regions should adopt policies based on local conditions, highlight key points, coordinate and integrate to promote high-quality development of tourism. [ABSTRACT FROM AUTHOR]

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model. [ABSTRACT FROM AUTHOR]

In the context of bank failure, the internet and development trend of financial businesses have notably influenced the banking industry. However, existing literature shows that few studies have quantitatively examined these influences on bank outlets, and the outcomes of studies on the impact of internet on the offline operation of banks have not been authoritatively verified; therefore, further exploration is required. In this context, this study analyzed the withdrawal of offline bank outlets and spatial distribution of bank withdrawals from 2019-2021 using kernel density analysis, geographical concentration index, and DBSCAN clustering algorithm and identified the comprehensive factors influencing bank withdrawals by combining these analyses with a spatial econometric model. This study determined the influences of various factors on the operation of offline financial banks under the background of information development. According to the results: 1) From 2019 to 2020, the withdrawn financial bank outlets revealed aggregated distribution overall, and the number of financial bank outlets that withdrew gradually declined from east to west; given the different conditions in terms of information network impact, different types of bank outlets reported various trends in these changes. 2) The financial institutions that withdrew had some common characteristics from single- and intra-city perspectives. Regarding single cities, In the three regions classified by the level of economic development, large urban agglomerations are the centralized regions where banks withdraw branches, forming the center-edge structure with core cities as the center. From the intracity perspective, more financial banks withdrew in regions with intensive capital, dense population, and developed technology. 3) In the context of mobile information, financial bank withdrawal was influenced by multiple factors. Overall, information environment exerts a strong influence. However, whether innovative ability is related to financial bank withdrawal remains unclear. In addition, the competition, labor, rent, and other operation costs and bank operation benefits from the external market substantially influenced the withdrawal of banks. In contrast to previous studies on the spatial expansion of bank outlets, this study discussed the development characteristics of financial bank withdrawal from the perspectives of time and multidimensional space and additionally considered financial geography, providing subsequent researchers with a reference and guidance. However, limited by data, this study only analyzed and discussed the data of from a 3-year period; the longer-term details of the study object were not summarized. In the future, more abundant data and broader methods will be used to conduct more detailed studies from richer perspectives. [ABSTRACT FROM AUTHOR]

Traditional method of estimating pollution loads may neglect the internal spatial heterogeneity of socio-economic driving factors, which can result in overestimate and underestimate of pollution loads. In this study, the corrected approach to estimating total phosphorus (TP) pollution load was proposed to explore its future variation to develop effective phosphorus pollution control strategies for water environment management. As the first-class tributary of the Yangtze River, the TP out of limits in the Tuojiang River is serious. Thus, based on the presently related basic datasets related to TP pollution load estimation, we firstly adopted the GM (1,1) model to predict their varied trends from 2021 to 2025. We then used the pollution emission coefficient method to calculate the TP pollution load. Moreover, considering the temporal and spatial heterogeneity of the pollutant generation coefficient, we further introduced population and GDP factors to further modify the pollutant generation coefficient to correct TP pollution load. Finally, we employed the exploratory spatial data analysis (ESDA) method to explore spatial distribution characteristics and spatial autocorrelation of TP pollution load from diverse pollution sources in 2025. The results showed that the total TP pollution load from diverse pollution sources will increase from 12,194.92 t in 2021 to 12,461.26 t in 2025, an increase of 2.18%. More concretely, the TP pollution load from rural domestic sewage, rural domestic waste and livestock, and poultry pollution sources will separately decrease by 94.24 t, 77.9 t, and 86.52 t. However, the TP pollution load from agricultural runoff and agricultural solid wastes pollution sources will increase by 74.52 t and 451.49 t, respectively. The contribution of TP pollution load from diverse pollution sources to total TP pollution load will be as follows: livestock and poultry (63.49%) > agricultural solid wastes (16.72%) > agricultural runoff (12.26%) > rural domestic sewage (4.12%) > rural domestic waste (3.41%). The difference in the spatial distribution of TP pollution load from diverse pollution sources in 2025 will be prominent. TP pollution from rural domestic sewage and rural domestic waste pollution sources is more serious in the Xindu and Longquanyi districts, and that from agricultural runoff and agricultural solid wastes pollution sources is more prominent in the midstream and downstream. TP pollution load from livestock and poultry pollution source is higher in the Renshou, Anyue, Rongxian, Luxian counties, and Jiangyang district. Additionally, TP pollution load from rural domestic sewage, rural domestic waste, agricultural runoff, and agricultural solid wastes pollution sources in 2025 will show a clear spatial correlation, but the spatial correlation of TP pollution load from livestock and poultry pollution source will be weak. The study is effective to eliminate the influence of temporal and spatial variation of pollutants generates coefficients on TP pollution load estimation. The method can reflect the actual condition of pollution loads in watersheds more objectively, which can be applied to estimate other pollution loads of similar watersheds with intensive socio-economic activities. The findings in this study can provide a critical reference for the stakeholders to balance water environment conservation and socio-economic development. [ABSTRACT FROM AUTHOR]

Carbon emissions from energy consumption caused by industrial production have become an important factor in global warming, and China now has the highest global carbon emissions, with enormous pressure to reduce emissions. The Mid-Yangtze River urban agglomeration (MYRUA), which is an important region for China, also faces the contradiction of balancing environmental protection and economic production. Currently, there is little discussion on low-carbon pollution research in the MYRUA in China. Therefore, this study takes the MYRUA as the research object, uses a slack-based model (SBM) and exploratory spatial data analysis (ESDA) to explore the spatiotemporal evolution of the industrial carbon emission efficiency (ICEE) of the MYRUA in China from 2006 to 2019, and empirically analyses the factors influencing the ICEE based on a spatial econometric model. The results show that (1) in the period 2006–2019, the ICEE in the MYRUA region generally exhibited a "jagged" trend of decreasing, then increasing until finally decreasing again. Spatially, ICEE generally exhibited a dynamic trend of being low throughout, with high-efficiency areas tending to move to the center and low-efficiency areas being scattered. (2) The global Moran's I coefficient for ICEE ranged between 0.002 and 0.213, with the highest Moran's I value and significant spatial correlation occurring in 2006, after which the value gradually decreased. On the local autocorrelation, the ICEE in 2006 exhibited an agglomeration effect and weakened after 2010. (3) In 2019, the level of industrialization (LI) and the level of external openness (LEO) in the MYRUA changed from nonsignificant to significant positive correlations, and the LEO had the largest impact. The energy consumption structure (ECS) was always significantly negatively correlated. The levels of social development (LSD) and environmental regulation (ER) changed from showing a significant positive correlation to showing a nonsignificant correlation. In conclusion, ICEE is affected by interactions of multiple factors. In the future, the ECS of the MYRUA should be continuously optimized and replaced by clean energy. In addition, foreign investment compensates for the lack of production and technology research funds, which can increase investment and promote the digitalization of manufacturing enterprises. • Spatial-temporal pattern of industrial carbon emission efficiency was investigated. • Spatial regression was employed to identify the factors influencing the efficiency of industrial carbon emissions. • The level of external openness has the largest driving factor. • Differential optimization pattern was established due to the differences within urban agglomerations. [ABSTRACT FROM AUTHOR]

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Revealing the spatial patterns of differentiation and the driving mechanism of agricultural multifunctional patterns is an important aspect of coordinating the functional optimisation and coordinated development of different agricultural regions. On the basis of understanding the connotation of agricultural multiple functions, this paper constructed an evaluation index system of agricultural multiple functions. Taking Jiangsu Province as a typical case, the spatial patterns of agricultural multifunctions in Jiangsu since 1978 were analysed by using the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method and ESDA (exploratory spatial data analysis) model, and the influencing mechanism of agricultural multifunction spatial differentiation was revealed by a geographic detector model. The results showed that (1) the cities with higher agricultural grain production functions were mainly concentrated in Yancheng and Huai'an; cities with higher agricultural economic development functions were mainly distributed in the coastal areas of Jiangsu; cities with higher agricultural social security functions were mainly concentrated in the Suzhou–Wuxi–Changzhou metropolitan area; and cities with higher agricultural ecotourism functions evolved from Nanjing–Zhenjiang to Suzhou–Wuxi–Changzhou. (2) The H–H (high–high) cluster pattern of the agricultural grain production function shifted from southern Jiangsu to northern Jiangsu. The H–H clusters of the agricultural economic development function and social security function were mainly distributed in Suzhou–Wuxi–Changzhou, while the L–L (low–low) cluster was mainly distributed in northern Jiangsu. The H–H cluster of agricultural ecotourism functions was mainly distributed in the areas with rich mountain and hill resources or dense water networks in Jiangsu. (3) The agricultural multifunction pattern differentiation was affected by the natural environment and economic and social comprehensive factors; the level of economic development and population employment structure were the leading factors of agricultural multifunction spatial differentiation; industry structure and people's living conditions were the important driving forces of agricultural multifunction spatial differentiation; and the natural environment and population density were the basic factors underlying agricultural multifunction spatial differentiation. [ABSTRACT FROM AUTHOR]

Dairy cattle farming is an important activity for generating income and maintaining man in the countryside and the state of Santa Catarina has Brazilian relevance in the sector. Knowing the dispersion of the activity, as well as the discrepancies in its location and productivity work as a tool for public policies, enabling public and private investments and rural extension actions. Thus, this article aim to analyze the spatial configuration of the quantity produced and the gross value of cow's milk production in the municipalities of Santa Catarina State and presents the results by mesoregions. To achieve the objective, an Exploratory Spatial Data Analysis - ESDA was carried out using data from the 2017 Agricultural Census. The analysis made it possible to identify the existence of spatial clusters in the state for both variables. The main High-High clusters, that is, municipalities with high gross production value and quantity produced, surrounded by municipalities also with high value are located in municipalities belonging to the western mesoregion of the state. Low-Low clusters were more dispersed across other regions. The article points out factors that contribute to this spatial configuration. [ABSTRACT FROM AUTHOR]

Elucidating the correlation among tourism, CO2 emissions, and economic growth from a spatiotemporal standpoint is of utmost significance for the tourism industry responding to China's "double-carbon" goal. This study expansively uses the bottom-up approach, Theil index, Exploratory Spatial Data Analysis (ESDA), and Logarithmic Mean Divisia Index (LMDI) method to calculate tourism CO2 emissions (TE) at different spatial scales in China during 2000–2019, and based on the TE, we further analyze the spatial heterogeneity of the TE intensity (TEI) and examine the spatiotemporal effects of driving factors on TE increases. The results revealed that (i) China's TE increased from 3714.06 × 104 t to 19,396.00 × 104 t, and the TEI declined from 47 to 9 g/yuan during 2000–2019. (ii) The high-TEI provinces varied from agglomerative distribution in the north by western region to scattered distribution in the eastern region. (iii) China's TEI exhibited increasing spatial differences, primarily within regions during 2000–2009, which also distributed with both the global and local agglomeration in space before 2014, and since then, only the local agglomeration enhanced and characterized by diffusing low–low (L–L) agglomeration from the east to the central and west regions. (iv) The tourism industrial scale and the industrial economy exerted cumulative effects on TE increases, and the energy intensity and energy structure exerted reduction effects. The spatial structure played different roles on TE among the regions. Policy implications are also discussed depending on the study results. [ABSTRACT FROM AUTHOR]

El presente trabajo examina la dependencia espacial, global y local, de la Tasa Municipal de Desempleo en Andalucía. Aplicando el software Geoda realizamos en primer lugar un ejercicio exploratorio encaminado a identificar los valores atípicos superiores del indicador. Posteriormente se implementan estadísticos propios del Análisis Espacial Exploratorio para determinar la dependencia espacial del desempleo en Andalucía. En la última sección de resultados se identifican los puntos calientes (Hot Spots) de mayor incidencia en la construcción del indicador de autocorrelación espacial. Se concluye tras el examen de resultados en la necesidad de añadir el territorio entre las variables de análisis para el estudio sociológico del paro. The current paper examines the spatial, global and local autocorrelation of the Municipal Unemployment Rate in Andalusia. Applying the Geoda software, firstly, we carried out an exploratory exercise aimed at identifying the superior outliers of the indicator. Later, statistics of the Exploratory Spatial Data Analysis are implemented to determine the spatial dependence of unemployment in Andalusia. In the last section of results, the hot spots with the highest incidence in the construction of the spatial autocorrelation indicator are identified. After examining the results it’s concluded on the need to add the spatial context among the analysis variables for the sociological research of unemployment.

In this paper, taking the per capita tourism revenue of the administrative units of Chinese mainland from 2007 to 2018 as an indicator, we analyze the unbalanced regional tourism growth in Chinese mainland and its trends by using kernel density estimation (KDE), the Gini index, and exploratory spatial data analysis (ESDA). The results demonstrate that (1) the distribution of regional tourism data in Chinese mainland and its four economic regions tends to converge, and the relative difference in tourism development among cities is slowly decreasing. (2) The differences in regional tourism development within the four economic regions in China are different, and each part shows its own degree of decline, with the gap in regional tourism development in Western China being the largest, and that in regional tourism growth in Northeast China the smallest. The difference in regional tourism development in both East and Northeast China decreased significantly and that in Central China decreased the least. (3) Finally, the difference in regional tourism development is dominated by a positive spatial correlation. The range of high-high types has expanded, and low-high and high-low types have decreased.

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of the influences of various driving factors on the digital economy restricts the further development of regional coordination. This paper constructs an index system for measuring the development level of the digital economy from the three dimensions of infrastructure construction, digital application and digital industry development. Using the entropy method to measure the development level of the digital economy in each region in 2018 and based on the theory of economic growth and new economic geography, a theoretical model of the influences of input factors, technological progress and institutional changes on China’s digital economy is established. Combined with Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) model analysis, the spatial distribution pattern of China’s digital economy and its influencing factors are discussed. The results show that there is a large gap in the development level of the digital economy in the eight comprehensive economic regions, and the development level of the digital economy presents a significant spatial correlation in space. The driving patterns of input factors, technological progress and institutional changes to the spatial distribution of the digital economy show obvious spatial differentiation. This study provides important referential value for promoting the coordinated development of the regional digital economy.

Analisa variabilitas spasial digunakan untuk mempelajari variablitas hujan yang diterima pada luasan tertentu. Analisis tersebut juga dapat digunakan untuk menentukan kalender tanam, mitigasi banjir, kekeringan, dan potensi bencana hidro-meteorologi terkait. Penelitian ini bertujuan menganalisis variabilitas spasial hujan bulanan dan tahunan di wilayah Kabupaten Jombang, Kediri, dan Nganjuk. Data diperoleh dari 137 stasiun hujan dengan panjang rekaman data dari tahun 1990 sampai dengan 2016. Tahap penelitian mencakup: (1) pengolahan data, (2) Exploratory Spatial Data Analysis (ESDA) menggunakan tool histogram, Voronoi-map dan Normal QQ-plot, (3) interpolasi, dan 4) analisis regresi-korelasi. Hasil analisis menunjukkan bahwa data hujan yang digunakan tidak terdistribusi normal. Nilai standar deviasi 126,9 mm untuk hujan bulanan dan 629,1 mm untuk hujan tahunan. Peta tematik menunjukkan bahwa sebagian besar wilayah penelitian menerima hujan bulanan maksimum 621 – 760 mm/bulan, dan mengalami hujan tahunan maksimum 2.178 – 3.202 mm/tahun. Analisis korelasi regresi menunjukkan adanya hubungan yang kuat antara ketinggian tempat dengan besarnya hujan yang diterima.

Crimes are actions and behaviors deemed harmful to society by the legislator. Beside the psychological, sociological, economic factors that reveal the crime also spatial factors are important. According to the basic law of geography, everything is related to everything else. But close things are more related than distant things. Thus, this study examines the spatial interactions between per capita income and unemployment rates and economic crimes via the Explanatory Spatial Data Analysis (ESDA). The results indicate significant spatial relationships in respect to the crime of smuggling for Gaziantep, Hakkâri and Iğdır provinces. [ABSTRACT FROM AUTHOR]

The urbanization process of the Hanjiang Basin is speeding up and facing many challenges. It is necessary to take active actions to realize the balance between urbanization development and water environment protection. This paper aims to establish a comprehensive evaluation model of urbanization and water environment (UAWE) and explore the development of UAWE in the Hanjiang Basin in China from 2010 to 2018. In terms of methodology, these evaluation models are combined with the Coupling coordination degree model (CCDM), Exploratory Spatial Data Analysis (ESDA), and Geographically and Temporally Weighted Regression (GTWR) to measure and analyze the coupling degree (CD) and spatiotemporal heterogeneity of UAWE. Our findings include the following: (1) From 2010 to 2018, in all regions of the Hanjiang Basin, urbanization showed a gradual upward trend. Among Them, the comprehensive urbanization index (CUI) of Xiangyang increased by 25.37%. (2) The comprehensive water environment index (CWEI) presents a fluctuating growth, with the rate of increase in Jingmen's reaching 17.44%. (3) There is heterogeneity in time and space between cities in the Hanjiang Basin, and the positive interaction effect of UAWE is mainly focused on the middle and lower reaches of the Hanjiang River. (4) Coupling coordination degree (CCD) in the basin shows an increasing trend, and the areas with high CCD are mainly concentrated in Xiangyang and Xiantao. (5) ESDA shows that the CCD among cities in the basin has spatial correlation and heterogeneity, and the regions with similar CCD tend to be distributed centrally in space.

Revealing the spatial patterns of differentiation and the driving mechanism of agricultural multifunctional patterns is an important aspect of coordinating the functional optimisation and coordinated development of different agricultural regions. On the basis of understanding the connotation of agricultural multiple functions, this paper constructed an evaluation index system of agricultural multiple functions. Taking Jiangsu Province as a typical case, the spatial patterns of agricultural multifunctions in Jiangsu since 1978 were analysed by using the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method and ESDA (exploratory spatial data analysis) model, and the influencing mechanism of agricultural multifunction spatial differentiation was revealed by a geographic detector model. The results showed that (1) the cities with higher agricultural grain production functions were mainly concentrated in Yancheng and Huai’an; cities with higher agricultural economic development functions were mainly distributed in the coastal areas of Jiangsu; cities with higher agricultural social security functions were mainly concentrated in the Suzhou–Wuxi–Changzhou metropolitan area; and cities with higher agricultural ecotourism functions evolved from Nanjing–Zhenjiang to Suzhou–Wuxi–Changzhou. (2) The H–H (high–high) cluster pattern of the agricultural grain production function shifted from southern Jiangsu to northern Jiangsu. The H–H clusters of the agricultural economic development function and social security function were mainly distributed in Suzhou–Wuxi–Changzhou, while the L–L (low–low) cluster was mainly distributed in northern Jiangsu. The H–H cluster of agricultural ecotourism functions was mainly distributed in the areas with rich mountain and hill resources or dense water networks in Jiangsu. (3) The agricultural multifunction pattern differentiation was affected by the natural environment and economic and social comprehensive factors; the level of economic development and population employment structure were the leading factors of agricultural multifunction spatial differentiation; industry structure and people’s living conditions were the important driving forces of agricultural multifunction spatial differentiation; and the natural environment and population density were the basic factors underlying agricultural multifunction spatial differentiation.

The potential level of rural tourism development in Guizhou Province is studied and its spatial differentiation characteristics is analyzed,to provid theoretical and reference for rural tourism development. Rural tourism development potential of 88 counties and its spatial differentiation characteristics are analyzed by ArcGIS 10. 2, OpenGeoDa tools, entropy method, ESDA and other research methods. The result shows that(1) there are obvious differences in the development potential of rural tourism in Guizhou. The counties with better rural tourism development potential are mainly distributed near the municipal administrative center. Good rural tourism resources and economic development are important factors for the differences of rural tourism development potential. (2) The development potential of rural tourism shows the spatial structure characteristics of "center periphery". The high level of rural tourism is concentrated in the central area of provincial and municipal areas, and the low-level counties are scattered and far away from the provincial and municipal centers. However, the radiation driving effect of high-level regions on the surrounding potential is not significant. Regional linkage development and regional cooperation need to be strengthened. (3) According to the main constraints of county development, we should promote different counties actively. Suggestions are by government public functions to expand tourism economics and to improve the construction of transportation and service facilities, and rural tourism transformation in Guizhou,to upgrade rural tourism effectively. [ABSTRACT FROM AUTHOR]

Abstract As of July 27, 2020, COVID‐19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID‐19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID‐19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID‐19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID‐19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID‐19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID‐19 epidemic.

As of July 27, 2020, COVID‐19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID‐19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID‐19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID‐19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID‐19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID‐19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID‐19 epidemic. Key Points: The spatiotemporal patterns of COVID‐19 cases in Hubei Province were analyzedAnomalies of environmental and meteorological factors contributed little to COVID‐19 developmentSocial distancing and isolation played a key role in controlling the epidemic [ABSTRACT FROM AUTHOR]

Paper evidence usually undergoes two consecutive examinations in forensic laboratories: ESDA (electrostatic detection apparatus) to detect indented impressions and a subsequent search for fingermarks by optical, physical, and chemical techniques. The ability of ESDA in visualizing fingermarks is well known. The aim of the present study is to understand better how long fingermarks can be developed with ESDA and if the different constituents of fingermarks show a different susceptibility to being developed. Several types of marks were collected from six donors on A4 standard office copy paper and tested with ESDA up to 69 days from deposition. A clear difference between natural, eccrine, and sebaceous marks emerged, being the latter ones better developed. ESDA developed standard sebaceous marks up to 16 days from deposition, whereas natural marks were visible within a few days. Eccrine marks were mainly visualized in reverse mode, despite the quality of these marks was not as good as the sebaceous marks. The appearance of the mark (standard/reverse) seemed to be correlated with time since deposition. Data suggest that the mechanism by which ESDA visualizes the fingermarks could be related to the residue (water‐soluble and/or non‐water‐soluble) available on the surface of the substrate and not yet absorbed by the paper. ESDA could act as a useful screening tool in fingermark visualization on porous surfaces, especially because it is a non‐destructive technique. Therefore, as a general procedure, it is highly recommended to acquire potential fingermarks developed by means of ESDA before proceeding with fingermark visualization. [ABSTRACT FROM AUTHOR]

The current paper examines the spatial, global and local autocorrelation of the Municipal Unemployment Rate in Andalusia. Applying the Geoda software, firstly, we carried out an exploratory exercise aimed at identifying the superior outliers of the indicator. Later, statistics of the Exploratory Spatial Data Analysis are implemented to determine the spatial dependence of unemployment in Andalusia. In the last section of results, the hot spots with the highest incidence in the construction of the spatial autocorrelation indicator are identified. After examining the results it's concluded on the need to add the spatial context among the analysis variables for the sociological research of unemployment. [ABSTRACT FROM AUTHOR]

To find out the spatial dependence of carbon emissions and its evolution characteristics is the key to achieving regional differential emission reduction strategy. In this study, 30 provinces with different population sizes and in different stages of development in China, were selected to explore the spatial heterogeneity of carbon emissions by exploratory spatial data analysis (ESDA), combined with geographically and temporally weighted regression (GTWR). The findings revealed that (1) energyrelated carbon emissions at the province-level in China increased from 1997 to 2016, with an increment of 8,893 million tons; (2) there is a significant positive spatial correlation between provincial carbon emissions, which showed the characteristics of rising first and then falling; this indicated that provincial carbon emissions have obvious spatial dependent characteristics; (3) the tertiary industry ratio had a restraining effect on carbon emissions, whereas the other three variables, namely GDP, urbanization rate, and energy intensity had a positive effect on carbon emissions of provinces in China; and (4) province-scale spatial differences in and distribution patterns of carbon emissions within the same countrywide, which will help decision making in terms of carbon trading and ecological compensation mechanisms. Therefore, we suggested that in the formulation of reduction policies for carbon emissions, policymakers need to adapt to local conditions which accord to the characteristics of the province. [ABSTRACT FROM AUTHOR]

• We found evidence of spatial autocorrelation in seat belt non-use in Tennessee. • Seat belt non-use is not produced solely by the internal structural factors. • We identified two major spatial regimes in Tennessee. • Relationship between demographic variables and seat belt non-use varies across regimes. Roadside observations indicate that seat belt use rates are often spatially correlated with nearby areas. However, very few studies have examined the effects of spatial autocorrelation on seat belt use. This study used exploratory spatial data analysis (ESDA) to explore spatial autocorrelation in Tennessee, which has a lower seat belt use than the United States national average. We geocoded home-addresses of vehicle occupants involved in traffic crashes between 2014 and 16 (n = 1,251,901) and projected them to the census tract corresponding to their home address. This projection reveals information about the spatial distribution of seat belt non-use and socioeconomics of the areas surrounding the crash victim's home. The presence of highly spatially correlated observations (i.e., a significant positive Moran's I) suggests that seat belt non-use is not produced solely by the internal structural factors represented in the non-spatial models. ESDA reveals a distinctive regional imprint for spatial autocorrelation, in which Southern-metropolitan areas' (Southern-MPOs) in Tennessee census tracts have higher than average seat belt non-use compared to Non-Southern-MPOs (16% vs. 9%). The spatial error model was suitable for Non-Southern-MPOs, whereas the spatial lag model was more suitable for Southern MPOs. Comparison of the estimated models indicates that in the Non-Southern MPOs, percentage of the White population, percentage of the population with Bachelor's degree, median household income, vehicle ownership, and population density are significant predictors of seat belt non-use. On the other hand, median household income, vehicle ownership, and percentage of population aged between 16 and 42 years old predict seat belt non-use in Southern MPOs. The study results could be used to identify seat belt non-use clusters at the state level and identify seat belt non-use hot zones. Furthermore, this analysis indicates that the relationship between demographic variables and seat belt non-use varies across regimes. Failing to consider the spatial regimes in the analysis would lead to falsely prioritizing groups prone to seat belt non-use. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite the precipitous economic development in China since it adopted the open-door policy in 1978, the issue of regional economic disparity remains to be a notable macroeconomic and social challenge in the 21st century. The Chinese government has allocated large amounts of resources to promote economic development and to reduce economic disparity over the past few decades. This study attempts to better understand the effectiveness and efficiency of the Chinese government's poverty reduction policy in the Sichuan Tibetan region, a remote area with a severe economic disparity issue in China. Using the exploratory spatial data analysis (ESDA) method, the present study first categorized the 32 counties in the studied Sichuan Tibetan region into four clustering groups. Further, the data envelopment analysis (DEA) method was applied to examine relative efficiency performance at the county level over the 2000-2007 period. A unique feature of the present study is that it incorporates both the ESDA and DEA techniques to assess not only economic outcomes (i.e., GDP per capita) but also the degree of relative efficiency for each subset of the spatial area. Our findings suggest that the clustering outcomes from the ESDA method are consistent with the relative efficiency evaluations from the DEA approach overall. However, at an individual county level, we found that some counties in the low-level economic performance groups outperform the counties in high-level economic performance groups in terms of efficiency performance. These findings reveal that the economic performance variable, such as GDP per capita, does not comprehensively reflect an individual county's level of relative efficiency performance and each clustering group has a non-balanced efficiency performance pattern. Empirical findings and the methods employed in this study could be of help to government agencies and regional economics scholars when they evaluate the efficiency of a policy or a program across various geographic areas through effective benchmarking comparisons. We also recommend both traditional economic measures and scale efficiency outcomes should be considered when a local government's overall economic performance is evaluated. Besides, government officials should pay more attention to the decision-making units and attempt to find factors underlying the non-balanced efficiency performance pattern. [ABSTRACT FROM AUTHOR]

This paper uses the entropy-weighted TOPSIS method to comprehensively evaluate the municipal economic development of prefecture- level cities in the Shanxi- Shaanxi- Inner Mongolia-Gansu-Ningxia (SSIGN) region. Then, it explores the spatio-temporal differentiation of economic development of the region using Markov transition probability analysis method and ESDA spatial analysis method. The results show that: (1) From 2005 to 2015, the gap in municipal economic development between cities in the SSIGN region was narrowed in the fluctuations, indicating that the municipal economic of the study region had not developed towards polarization, and there was a certain foundation for achieving coordinated development. (2) Each economic development grade city presented a diametrically opposite evolution law in different periods. Over time, all besides the lowest-grade cities had a higher risk of grade decline. This result indicates that for cities with higher municipal economic level, effective measures shoud be taken to improve their economic development sustainability. (3) From 2005 to 2010, to a certain degree, there was a significant spatial negative correlation between the economic levels of the cities in the SSIGN region. A positive interaction needs to be formed in this region. By 2015, this relationship was no longer significant. This result indicates that the municipal economic development of the region has improved in the direction of positive integration, but we still need to be alert to the problems of excessive homogenization and lack of effective interaction that have occurred in the historical development of this region. (4) The regional economic development of the SSIGN region has gradually emerged in the spatial pattern of the Golden Triangle consisting of Xi'an, Hohhot and Taiyuan. However, there has also been a clear cold spot in the study region. (5) The municipal economic development system in the cities of this region is generally in a state of low coupling coordination, and unbalanced economic development and uncoordinated are prominent. This result actually points out the key issue affecting the overall coordinated development of the region's economy. That is, the economic development subsystems within the city should also be a balanced system and coordinated development should be achieved among these subsystems. [ABSTRACT FROM AUTHOR]

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

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.

Under the limited reserve resources of cultivated land, it is necessary to change the traditional extensive utilization methods of cultivated land resources, and the road of intensive development is the most effective way to solve the human-land contradiction. The scientific analysis of spatial and temporal differences and influencing factors of cultivated land intensive use in the region is of great practical significance for promoting intensive potential exploitation and efficient utilization of cultivated land resources. Taking the Yangtze River Economic Belt as a case study, evaluation index system of cultivated land intensive use was constructed from the dimensions of input intensity, utilization intensity, output efficiency and sustainability. Projective pursuit model was used to quantitatively measure the level of cultivated land intensive use in the Yangtze River Economic Belt from 1978 to 2016. Based on the multiscale perspective of watershed, province and city, through the coefficient of variation, spatial autocorrelation index, the paper explored multiscale spatial-temporal differences of cultivated land intensive use in the Yangtze River Economic Belt. The influence mechanism of intensive utilization of cultivated land was revealed by using geographic detector model. The results showed that: since 1978, the level of cultivated land intensive use in the Yangtze River Economic Belt has improved at the watershed scale, but in the evolution process, the difference characteristics of “east high west low” have appeared; the intensive use of cultivated land in the Yangtze River Economic Belt at provincial and city scales presents spatial agglomeration characteristics, and the smaller the scale, the more significant the spatial agglomeration. During the period of the study, the types of spatial correlation between provinces and regions were mainly positive correlation, and there was a phenomenon of “club convergence” in cultivated land intensive use in the Yangtze River Economic Belt at city scale. The hot spots are mainly concentrated in Shanghai and Southern Jiangsu, and gradually evolved into Shaoxing-Hangzhou and Lancang; the cold point area was mainly concentrated in the Western Sichuan Plateau area. Natural factors, population growth, economic and social development, and institutional policies can effectively explain the spatial-temporal differences in the intensive use of cultivated land in the Yangtze River Economic Belt; demographic factors and economic and social factors have a significant impact on cultivated land intensive use, while policy factors have a strong driving effect. Finally, suggestions were put forward on how to promote cultivated land intensive use from the aspects of comprehensive land remediation, increasing the input of elements, optimizing and adjusting the structure of cultivated land, and promoting innovation in agricultural science and technology. In addition, it should be pointed out that there are still some deficiencies in this study: only the spatial and temporal patterns of cultivated land intensive use in six time sections since 1978 are analyzed on the time scale, it is also necessary to further reveal the temporal and spatial differentiation of intensive cultivated land use in the Yangtze River Economic Belt at a multitime scale. In terms of the impact mechanism, the factors of farmer households have not been discussed in this study, and the factors of farmer households have an important impact on regional cultivated land use efficiency. How to reveal the micro-mechanism of spatial and temporal differentiation of cultivated land intensive use in the Yangtze River Economic Belt from the perspective of farmers will be the next deepening area and direction of this study. [ABSTRACT FROM AUTHOR]

Because of their several advantages like simplicity, flexibility and adaptability, nature‐inspired (NI) optimisation algorithms have attracted significant attention for solving complex optimisation problems. Source of inspiration for NI are multiple. This study aims to propose a new NI optimisation algorithm inspired by the electrostatic discharge (ESD) event. Tested on a large set of benchmarks and compared with several well‐known optimisation algorithms the ESD algorithm (ESDA) is found to be a very competitive algorithm. Moreover, the ESDA has been applied for the determination of wort‐case scenarios for electromagnetic compatibility (EMC) filter. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate impact of political terrorism on GDP for Eurasia region countries by using spatial statistics in the period of 1996-2013. For this purpose, quantile maps, Moran’s Scotterplots, LISA (Local Indicators of Spatial Association) statistics are performed. We firstly found that there are the negative impacts of political terrorism on GDP. Secondly, countries as Iceland, Ireland, UK, Portugal, Spain, Norway, Sweden, Italy, Austria, Czech Rep., Denmark, Slovenia, Belgium, France, Germany, Luxemburg, Switzerland, Finland, and Netherland are determined as the strongest positive attraction centers in all Europe countries. Thirdly, Asian countries as India, Uzbekistan, Afghanistan, Kyrgyzstan, Nepal, Tajikistan, Bangladesh and Myanmar are obtained as the strongest negative attraction centers for Asia. The rest of countries in Asia have been negatively affected by these negative attraction centers. Finally, for the big part of Asia (exclude countries as Japan, South Korea) regions, political terrorism is one of the biggest factors of poorness and weakness of per capita GDP

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this article is to analyze spatially the revenues of municipalities, as well as intergovernmental relations, in order to evaluate the great asymmetries that define Brazilian federalism. For this purpose, the means provided by Quantitative Geography were used, especially in its interfaces with other sciences, such as Economics (2000-2015), through the Moran I Global Index and the Getis-Ord Index. [ABSTRACT FROM AUTHOR]

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major US cities: Chicago, Illinois and Washington DC. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favourably influenced valence, albeit only in DC. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation. [ABSTRACT FROM AUTHOR]

Urban fire occurs within the built environment, usually involving casualties and economic losses, and affects individuals and socioeconomic activities in the surrounding neighborhoods. A good understanding of the spatiotemporal dynamics of fire incidents can offer insights into potential determinants of various fire events, therefore enabling better fire risk estimation which can assist with future allocation of prevention resources and strategic planning of mitigation programs. Using a twelve-year (2002-2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatiotemporal dynamics of urban fires using a range of exploratory spatial data analysis (ESDA) approaches. Of particular interest here are the fire incidents involving residential properties and local facilities due to their relatively higher occurrence frequencies. The results indicate that the overall amount of urban fires has greatly increased in the last decade and the spatiotemporal distribution of fire events varies among different incident types. The identified spatiotemporal patterns of urban fires in Nanjing can be linked to the urban development strategies and how they have been reflected in reality in recent years. [ABSTRACT FROM AUTHOR]