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Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University 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.)

Earthquakes are one of the most destructive natural disasters that occur as a result of the energy resulting from the movements of the earth's crust spreading and shaking the earth's surface. Since it is not possible to predict when and where earthquakes will occur, it has become possible to make various inferences and take necessary precautions by analyzing previous earthquakes. Spatial and temporal analysis of natural disasters such as earthquakes with geographic information systems provides useful information to local and global decision-makers in measures to be taken and mitigation studies. The objective of this study is to demonstrate that the general orientation of stress accumulation on fault lines can be monitored by weighted average centre points with the use of Geographic Information Systems (GIS). Spatio-temporal analyses of two earthquakes with a magnitude of 7.7 Mw and 7.6 Mw in Kahramanmaraş and aftershocks were performed. Ten-week spatial distributions of the aftershocks were examined with the weighted average center method and it was determined that the earthquake intensity and average centers shifted towards the north. When the spatial distribution of the earthquake centers is examined with the help of standard deviation ellipses weekly, it has been determined that they show orientation along the Eastern Anatolian Fault System.

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. 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.)

[Objective] The aims of this study are to extract long-term and precise monitoring of abandoned farmlands, to understand the spatiotemporal distribution patterns of abandoned farmlands in river basins, and to provide data support for the management of land resources and the strategic planning of rural revitalization in Liangshan Prefecture. [Methods] The Sunshuihe River Basin in Liangshan Prefecture was taken as the research area. The Classification and Regression Tree (CART) model was used to extract abandoned farmlands from 2009 to 2016, and the spatiotemporal pattern and evolution dynamics of abandoned farmland in the past eight years were analyzed using the standard deviation ellipse method. [Results] (1) Farmland abandonment in the basin showed two changing trends during the study period: slow decrease from 2009 to 2011 and rapid increase from 2012 to 2016. Abandoned farmlands mostly distributed in the middle and high mountain areas and gradually decreased in the valley plain. The average abandoned area for the entire river basin was 5 357.88 hm², and the maximum abandonment rate (24.50%) was observed in 2016. The average annual abandoned area followed the order: middle reaches (2 260.50 hm²) ＞upstream (1 716.01 hm²) ＞downstream (1 381.88 hm²) . The average annual abandonment rate increased with stream reaches: upstream (19.17%) ＞middle reaches (15.46%) ＞downstream (8.50%) . (2) The standard deviation ellipse distribution of abandoned farmland showed two stages of development. From 2009 to 2012, it showed the distribution of ‘southeast-northwest’. After 2013, the center of gravity moved back, and the abandoned land was more concentrated. (3) Along topographic and proximity factors, abandoned farmland mostly distributed with an altitude of ＞2 800 m, and a slope gradient of 15°～25°, and 500 m away from the river system. [Conclusion] The spatiotemporal pattern of abandoned farmlands can be influenced by various factors such as topographical conditions, production levels, and political factors. In the future, it is important to enhance the in-depth study of the impact mechanisms of human activities and natural conditions on the land abandonment. [ABSTRACT FROM AUTHOR]

Poverty is at the forefront of Africa's efforts to achieve the United Nations' 2030 Sustainable Development Goals (SDGs). China's aid to Africa takes poverty eradication as its starting point and goal. Does the spatial distribution of China's aid to Africa take into account the spatial distribution of Africa's poor population? This paper used "Aid Spatial Difference" to measure the difference between the spatial distribution of China's aid to Africa and the spatial distribution of the poor population in Africa. Based on the geocoded data of China's aid to Africa and the population data of Africa from 2000 to 2014, this paper adopted Standard Deviation Ellipse method to analyze the spatial-temporal evolution relationship between China's aid to Africa and African poor population. This paper tested that spatial-temporal evolution relationship from two perspectives, the number of aid projects and the amount of aid. This paper finds that the spatial distribution of China's aid to Africa favors Africa's poor population. It is especially prominent in the location of aid projects, the growth rate of aid to Africa and the change of the gravity center of aid amount. This is consistent with China's purpose of prioritizing poverty eradication and helping Africa to achieve the UN's 2030 SDGs. [ABSTRACT FROM AUTHOR]

Narcotics litigation is the main legal case in Muang Nakhon Pathom District, Nakhon Pathom Province. This investigation aimed to explore spatial analysis of narcotics litigation using a Geographic Information System in the responsibility area of Muang Nakhon Pathom Police Station using data on narcotics litigation collected between 2013 and 2017. The purposes of this study were: 1) to examine the pattern of narcotics litigation using Moran's I, 2) to investigate the spatial distribution of narcotics litigation using hot spot analysis based Getis-Ord Gi*, 3) to find out the directional distribution of narcotics litigation using Standard Deviational Ellipse, and 4) to explore the spatial relationship between narcotics litigation and land use. The results of the study was as follows. 1) The pattern of the narcotics litigation over 5 years was clustered according to Moran's I value, which was discovered at 0.31, 0.34, 0.27, 0.24, and 0.17, sequentially over the 5 years. 2) The spatial distribution of cases revealed hotspot areas with a GiZscore greater than 1.65 present at Phrapathom Chedi and Huai Chorakhe districts in 2013 and 2014, Phrapathom Chedi, Wang Takoo, and Sanam Chan districts in 2015, Phrapathom Chedi, Huai Chorakhe and Bo Plab district in 2016, and Sanam Chan district in 2017. 3) The directional distribution of the cases over 5 years was distributed to the west of the study area. 4) Urban and built-up land were found to be places where most narcotics litigation occurred. The research results can be used to consider policy plans related to the Narcotics problem. [ABSTRACT FROM AUTHOR]

The change in land-use/land-cover (LULC) is one of the physical factors in urban development and the rapid growth in population has necessitates the need for space, driven by high socio-economic activities. Banyumas is a regency that had experienced rapid population growth in the last two decades, establishing an activity and service center in the Barlingmascakeb region. This rapid population growth has led to massive changes in LULC. Therefore, this study aimed to observe the changes in LULC in 2000 and 2020 to determine the direction of urban development in Banyumas Regency within 20 years. Multispectral classification with a Maximum Likelihood algorithm was used to extract LULC information from Landsat images. The changes obtained by crosstab analysis on the multispectral classification results were used as a reference to observe the direction of urban development. This procedure used four quadrants according to the cardinal directions and Standard Deviational Ellipse (SDE). The result showed that LULC in the forest class experienced the highest change of 142,584.3 km², accounting for 48%. Based on the increase in built-up land over 20 years, the direction of urban development according to the cardinal directions showed that the most dominant increase was in quadrant II (Southeast), which is 56.44% or 21.95 km². It was concluded that the direction of urban development was oriented toward the southeast. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020–2021. The ASF outbreak data (2020–2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020–2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p <.01) and 2021 (z score = 4.89, p <.01). Spatial autocorrelation and Moran’s I value (0.05–0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space–time analysis detected five, five, and two statistically significant (p <.01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north–south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states. [ABSTRACT FROM AUTHOR]

Climate change, population growth, and socio-economic transformations present multifaceted challenges to the water resource systems in the four major river basins of Henan Province. Consequently, to gain a comprehensive understanding of water security within the study area, a quantitative assessment of water resource vulnerability (WRV) is imperative. In this investigation, the vulnerability scoping diagram (VSD) model serves as the analytical framework, subdividing vulnerability into three critical subsystems: exposure, sensitivity, and adaptive capacity. Utilizing a comprehensive evaluation index system, this study assesses WRV in Henan Province's four primary watersheds. Temporal variations and spatial patterns of WRV from 2000 to 2020 are systematically examined through the standard deviational ellipse (SDE) and GeoDetector methods. The findings indicate that vulnerability within these river basins is shaped by the interactions among exposure, sensitivity, and adaptive capacity. Moreover, exposure and sensitivity are found to be more influential than adaptive capacity. Although there are indications of future improvements in the system's resilience against water resource vulnerability, the overarching scenario remains precarious, particularly within the Hai and Yellow River basins. Considering the diverse characteristics of the watershed, it is recommended that adaptive management strategies be implemented. This study enhances the understanding of WRV dynamics in Henan Province, thereby aiding more effective decision making in sustainable water resource management. [ABSTRACT FROM AUTHOR]

The Yellow River Source Area (YRSA) functions as an ecological barrier within the Yellow River Basin, playing a significant role in providing indispensable ecosystem services. Analyzing the ecosystem service value (ESV) of YRSA holds great significance in establishing ecological protection awareness and promoting ecological actions. In this study, we reveal the spatial and temporal characteristics of ESV in YRSA from 2000 to 2020 based on the land use change and equivalent factor method, and explore the driving mechanisms behind ESV heterogeneity using geographical detector. The results showed that from 2000 to 2020, ESV in the YRSA increased significantly, with an average increase rate of 9.12 × 1021seJ/5a, showing a spatial distribution pattern of low in the northwest and high in the southeast, and this imbalance is gradually weakening. The average annual contribution of grassland ESV reached 45 %, followed by water bodies (23 %). Ecosystem services in the YRSA are mainly dominated by regulating services, among which hydrological regulating services are dominated, with an average annual contribution rate of more than 40 %. Supply and regulation, support and cultural services both form a strong correlation synergy. Climate factors are the main drivers of spatial heterogeneity in ESV, further illustrating the sensitivity of the YRSA to climate change. Moreover, our results accentuate the integral role of the YRSA in furnishing ESV to the broader Yellow River Basin, which provides a theoretical basis and reference for decision makers to assess the ecological security of the ecological barrier zones.

Global warming caused by massive carbon emissions poses a serious threat to the environment and human beings. Dynamic monitoring of the spatio-temporal evolution of carbon emissions is an essential way to achieve carbon reduction goals, especially in coastal areas like Guangdong Province faced with dual challenges of massive carbon emissions and rigorous reduction targets. To reveal the spatio-temporal characteristics of carbon emissions and provide a basis for further fine-grained carbon emission reduction strategies, we analyzed spatio-temporal distribution characteristics of carbon emissions in Guangdong Province at the county-level scale from 2000 to 2020. Based on the land-use data obtained from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences and energy consumption data from the Statistical Yearbook, the carbon emission of each county is estimated, and the spatio-temporal evolutionary features of them are explored by a series of methods of the exploratory spatio-temporal data analysis (ESTDA). The shift of spatial distribution characteristics and gravity center is further revealed with the standard deviational ellipse and gravity center migration analysis.The results reveal that the net carbon emissions increased from 6179.229 × 104 tons to 20765.723 × 104 tons during 2000–2020. The net carbon emissions of county-level administrative regions show a significant positive spatial correlation, and the spatial convergence shows a trend of first decreasing and then increasing. The spatio-temporal pattern of carbon emissions has an obvious path dependence and locked spatial features, and shows a gradually strengthening trend. The spatial distribution of carbon emissions formed a stable “northeast-southwest” pattern, and the gravity center of carbon emissions in the past 20 years has been distributed between 113.208°–113.357°E and 22.760°–22.878°N, tending slightly shifting to the northeast. The results of the study can help understand the spatio-temporal evolution of carbon emissions in Guangdong, and provide a reference for formulating effective carbon reduction policies.

Copyright of Journal of Resources & Ecology is the property of Journal of Resources & Ecology 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.)

Territorial space exhibits multiple functional attributes, which comprise production, living, and ecological functions usually. Optimizing the production-living-ecological space (PLES) has become the key to territorial and spatial planning; the scientific identification of the PLES lays a foundation for space optimization and has important guiding significance in territorial spatial zoning. To achieve the integration of macro-scale and micro-scale PLES, with the Urban Agglomeration in Central Yunnan as the research area in this study, the PLES functional identification systems from the administrative unit scale and the grid scale are constructed. The types of PLES are determined by integrating qualitative and quantitative evaluation results and using an improved primacy index model from a composite spatial perspective. On that basis, the division of comprehensive zoning is achieved for land use functions through kernel density analysis. As indicated by the results, the model is capable of reflecting the macro background of the PLES functions in administrative regions while characterizing the micro differences at the grid level in administrative units. There are significant differences in the production, living, and ecological functional spaces in the Urban Agglomeration. Production functions are concentrated in the central and northeastern, living functions are concentrated in the central, and ecological functions are concentrated in the western and northeastern, with significantly consistent or complementary spatial distributions of each other. The PLES of Urban Agglomeration includes production space (PS), ecological space (ES), production-living space (P-LS), production-ecological space (P-ES), living-ecological space (L-ES), and production-living-ecological space (P-L-ES), placing a focus on ES, P-ES, and P-L-ES, which marks significant differences in spatial distribution among different spatial types. The study area is divided into 24 functional zones, which are classified into 6 categories, and optimization paths are proposed. This study will provide a reference for territorial and spatial planning in spatial functional zoning, spatial pattern optimization, and land management applications. [ABSTRACT FROM AUTHOR]

Climate change caused by CO2 emissions is posing a huge challenge to human survival, and it is crucial to precisely understand the spatial and temporal patterns and driving forces of CO2 emissions in real time. However, the available CO2 emission data are usually converted from fossil fuel combustion, which cannot capture spatial differences. Nighttime light (NTL) data can reveal human activities in detail and constitute the shortage of statistical data. Although NTL can be used as an indirect representation of CO2 emissions, NTL data have limited utility. Therefore, it is necessary to develop a model that can capture spatiotemporal variations in CO2 emissions at a fine scale. In this paper, we used the nighttime light and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), and proposed a normalized urban index based on combination variables (NUI-CV) to improve estimated CO2 emissions. Based on this index, we used the Theil–Sen and Mann–Kendall trend analysis, standard deviational ellipse, and a spatial economics model to explore the spatial and temporal dynamics and influencing factors of CO2 emissions over the period of 2000–2020. The experimental results indicate the following: (1) NUI-CV is more suitable than NTL for estimating the CO2 emissions with a 6% increase in average R 2 . (2) The center of China's CO2 emissions lies in the eastern regions and is gradually moving west. (3) Changes in industrial structure can strongly influence changes in CO2 emissions, the tertiary sector playing an important role in carbon reduction. [ABSTRACT FROM AUTHOR]

As a kind of "pro-agricultural" intellectual property, geographical indications have brand value and cultural value, and play a unique role in promoting agricultural economic growth and Rural Revitalization. This paper describes the spatial distribution and spatial trends of geographical indication products in China based on ArcGIS platform, and explores the spatial clustering characteristics of geographical indication products in China by using standard deviational ellipse and exploratory spatial data analysis methods. The results show that: (1) Fruit geographical indication products account for the highest proportion, flowers and handicrafts account for a relatively small proportion, and agricultural geographical indication is the most important component. (2) The province with the largest distribution of geographical indication products is Shandong, and the provinces with the largest number of registered geographical indication products in the fruit category are Shandong and Xinjiang. In terms of spatial trends, Chinese geographical indication products show a relatively smooth arc-shaped trend in the east-west direction and an obvious inverted U-shaped trend in the south-north direction. (3) Chinese geographical indication products have obvious spatial agglomeration characteristics, and tea geographical indication products have the highest spatial agglomeration degree. [ABSTRACT FROM AUTHOR]

[Objective] The spatio-temporal changes and influencing factors of carbon emission at the county scale in Shaanxi Province from 2000 to 2020 were analyzed in order to provide a reference for ecological civilization construction and low-carbon sustainable development in Shaanxi Province. [Methods] Socioeconomic and land use data were used to construct a carbon emission estimation model. The total carbon emission in Shaanxi Province was calculated and the spatiotemporal patterns and changes from 2000 to 2020 were analyzed. Subsequently. The influencing factors of carbon emissions were determined using the geographically weighted regression method. [Results] ① Total carbon emissions increased from 3.30×107 tons in 2000 to 1.93×108 tons in 2020. The evolution of carbon emission can be divided into two stages (substantial growth and slow growth). ② The carbon emission center gradually moved to the northeast from 2000 to 2020, and the spatial distribution range showed an expansion trend. The hot spots were mainly located in the northern counties of Yulin City, and around Xi'an and Xianyang City. The cold spots were mainly located in Foping County and Shiquan County. ③ Per capita GDP, land use degree, and per capita total social retail sales were positively correlated with carbon emissions, while industrial structure and population density were negatively correlated in 60.74% and 92.52% of counties, respectively. [Conclusion] Low-carbon development should be promoted in Shaanxi Province by formulating differentiated regional carbon emission reduction plans, optimizing the land use structure, controlling the expansion of construction land, and by enhancing public awareness of low-carbon environmental protection.

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin's central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin's development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics. [ABSTRACT FROM AUTHOR]

Realizing the decoupling development between the economic expansion and carbon dioxide emissions of the transport sector is of great importance if the Yellow River basin is to achieve green and low-carbon development. In this paper, we adopt the Tapio decoupling index to examine the decoupling relationship within the transport sector in the Yellow River basin, and then introduce the standard deviational ellipse to dynamically analyze the spatial heterogeneity of carbon emissions and economic growth at the provincial level. Furthermore, based on the decoupling method, we expand the traditional logarithmic mean Divisia index decomposition (LMDI) model to decompose the decoupling index into eight sub-indices, and we identify the impact of each factor on the decoupling relationship. The results indicate that the carbon emissions of the transport sector in the Yellow River basin show the non-equilibrium characteristics of "upstream region < midstream region < downstream region". The decoupling state of the transport sector shows obvious spatial differences. The less-developed regions are more likely to present non-ideal decoupling states. The growth rate of carbon emissions in Sichuan, Qinghai, and Shandong provinces is relatively fast, and the azimuth of the transport sector's carbon emissions shows a clockwise trend. Moreover, the inhibitory effects of urbanization on decoupling in the Yellow River basin are much greater than the non-urbanization factors. In addition to the effect of urbanization, the transport structure has a major negative effect on decoupling development in the upstream and midstream regions, while energy intensity and energy structure are key to realizing a decoupled status in the downstream region. Finally, we propose some differentiated policy recommendations. [ABSTRACT FROM AUTHOR]

Abstract Background China now faces an increasingly aging society which may exert economic pressure in the long run. This study illustrates the spatial pattern and evolution of population aging and economic development in China. The coupling coordination degree of population aging and economic development at the national and provincial levels are calculated and demonstrated, and the spatial patterns and characteristics are investigated. Methods This paper presents a coupling analysis of the elderly population rate (EPR) and per capita Gross Regional Product (GRPpc) in China by using the coupling and coordination model. Further, the spatial pattern and evolution of population aging and economic development are investigated based on the standard deviational ellipse. The collected data is at the level of provincial administrative units in mainland China covering the period 2002 to 2020. Results The results reveal the spatial difference in the coupling and coordination degree between EPR and GRPpc across provinces. The eastern coastal areas are higher than the central and western regions of China. The orientation and directions of EPR are more than GRPpc, indicating that the polarization in population aging is more severe than economic development. Significant positive correlations between coupling coordination degree and sustainable competitiveness are detected. Conclusions Policymakers should fully consider regional differences and sustainable development in policy formulation of China. The western and northeastern provinces should be given priority in the regional sustainable development plan. At the same time, the coordination between population aging and economic development also requires to be examined especially.

Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatiotemporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ~1.44OC, 1.40OC, ~0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT). [ABSTRACT FROM AUTHOR]

At the end of 2019, a new form of Coronavirus (later dubbed COVID-19) emerged in China and quickly spread to other regions of the globe. Despite the virus’s unique and unknown characteristics, it is a widely distributed infectious illness. Finding the geographical distribution of the virus transmission is therefore critical for epidemiologists and governments in order to respond to the illness epidemic rapidly and effectively. Understanding the dynamics of COVID-19’s spatial distribution can help to understand the pandemic’s scope and effects, as well as decision-making, planning, and community action aimed at preventing transmission. The main focus of this study is to investigate the geographic patterns of COVID-19 dissemination in Iraq from May 1 to July 29, 2021. The analysis was primarily based on using spatial analysis tools such as standard deviational ellipse (SDE) with in GIS environment, in addition to incidence rates calculations. The results revealed that the direction of COVID-19 spread is NW-SE. Furthermore, the findings showed that the rate of COVID-19 infections is greater at the middle and south of Iraq. This may aid decision-makers in identifying priority areas for emergency efforts.

At the end of 2019, a new form of Coronavirus (later dubbed COVID-19) emerged in China and quickly spread to other regions of the globe. Despite the virus's unique and unknown characteristics, it is a widely distributed infectious illness. Finding the geographical distribution of the virus transmission is therefore critical for epidemiologists and governments in order to respond to the illness epidemic rapidly and effectively. Understanding the dynamics of COVID-19's spatial distribution can help to understand the pandemic's scope and effects, as well as decision-making, planning, and community action aimed at preventing transmission. The main focus of this study is to investigate the geographic patterns of COVID-19 dissemination in Iraq from May 1 to July 29, 2021. The analysis was primarily based on using spatial analysis tools such as standard deviational ellipse (SDE) with in GIS environment, in addition to incidence rates calculations. The results revealed that the direction of COVID-19 spread is NW-SE. Furthermore, the findings showed that the rate of COVID-19 infections is greater at the middle and south of Iraq. This may aid decision-makers in identifying priority areas for emergency efforts. [ABSTRACT FROM AUTHOR]

The development of green residences is crucial to reducing energy consumption and carbon emissions of the construction industry. However, the study on the spatial distribution characteristics of green residences and its influencing factors has not attracted enough attention in the academic circles. Base on the panel database on the number of each star green residences and their driving factors at the municipal level from 2008 to 2016, this paper employed the Weighted Standard Deviational Ellipse model to reveal the spatial and temporal evolution features of green residences in China, creatively introduced an improved Gini coefficient (G′-score) to measure the green residences development in each city, and utilized the panel Tobit model and average marginal effect to identify the driving mechanisms and key factors of green residences from economy, society, the real estate market, policy and climate. The main conclusions show that: (1) China has formed a relatively stable and clear temporal and spatial evolution path since 2011, such as the center of gravity and coverage having moved to the west, and the direction of development trend having weakened; (2) China's green residence is mainly distributed in the central and eastern regions, and the main direction layout is northeast–southwest; (3) the development of green residences is the result of the interaction of various factors, and the driving force of each factor varies greatly under the single action and the interaction; (4) the driving effects of the same factor on green residences with different star ratings are inconsistent in sign, magnitude, and significance, the same as for each factor under the same star rating. [ABSTRACT FROM AUTHOR]

The JESSICA initiative was set up to provide a more sustainable and efficient response to the needs of urban areas, as compared to non-repayable grants. Anchored in the literature on place-based policy and territorial cohesion, this paper addresses the question how the JESSICA funds were allocated among Polish cities – whether, intuitively, only to key urban centres, or to smaller cities as well. The results illustrate that the repayable assistance of JESSICA was dispersed throughout the regions, although the degree of dispersion remains mixed across them. Almost half of the JESSICA funds was transferred to small and medium-sized cities. It was also found that the bulk of the assistance went to the projects that were implemented in cities situated within metropolitan areas of the regional capital cities.

Water, energy, and food are important factors affecting people’s lives and socio-economic development, and their production and consumption processes are closely related, so it is necessary to do research on input–output efficiency as a whole. The research is conducive to promoting the effective utilization and rational allocation of relevant resources in the Yangtze River Economic Belt, thereby promoting sustainable development. This study calculates the input–output efficiency of the water–energy–food (WEF) nexus of 11 provincial administrative regions in the Yangtze River Economic Belt utilizing the DEA-BCC model. Then, new indicators called area expansion degree and the subsystem’s influence degree are proposed with the aid of the standard deviation ellipse model to analyze the characteristics and trends of spatial–temporal distribution of input–output efficiency. The standard deviation ellipse model starts from the basic spatial elements, including point, line, and surface, and is used to study the spatial distribution and trend change of efficiency according to the center of gravity and area. The shift of the center of gravity shows the direction of significant improvement in the effect of resource allocation, and the change of area shows the differences in the speed of efficiency improvement between regions and the future development trend. The results mainly indicate that the resource allocation in the middle and lower reaches of the Yangtze River Economic Belt is more reasonable than that in the upper reaches, and the efficiency distribution is obviously concentrated in the northeast direction. It is suggested that the provincial administrative regions in the upper reaches should optimize the industrial structure, the regions in the middle and lower reaches should improve the resource structure, and the flow of talents and technology of regions should be promoted.

This study established a probability model based on the landslide spatial and size probabilities to predict the possible volume and locations of landslides in watershed scale under rainfall events. First, we assessed the landslide spatial probability using a random forest landslide susceptibility model including intrinsic causative factors and extrinsic rainfall factors. Second, we calculated the landslide volume probability using the Pearson type V distribution. Lastly, these probabilities were joined to predict possible landslide volume and locations in the study area, the Taipei Water Source Domain, under rainfall events. The possible total landslide volume in the watershed changed from 1.7 million cubic meter under the event with 2-year recurrence interval to 18.2 million cubic meter under the event with 20-year recurrence interval. Approximately 62% of the total landslide volume triggered by the rainfall events was concentrated in 20% of the slope units. As the recurrence interval of the events increased, the slope units with large landslide volume tended to concentrate in the midstream of Nanshi River subwatershed. The results indicated the probability model posited can be used not only to predict total landslide volume in watershed scale, but also to determine the possible locations of the slope units with large landslide volume.

Background: China now faces an increasingly aging society which may exert economic pressure in the long run. This study illustrates the spatial pattern and evolution of population aging and economic development in China. The coupling coordination degree of population aging and economic development at the national and provincial levels are calculated and demonstrated, and the spatial patterns and characteristics are investigated. Methods: This paper presents a coupling analysis of the elderly population rate (EPR) and per capita Gross Regional Product (GRPpc) in China by using the coupling and coordination model. Further, the spatial pattern and evolution of population aging and economic development are investigated based on the standard deviational ellipse. The collected data is at the level of provincial administrative units in mainland China covering the period 2002 to 2020. Results: The results reveal the spatial difference in the coupling and coordination degree between EPR and GRPpc across provinces. The eastern coastal areas are higher than the central and western regions of China. The orientation and directions of EPR are more than GRPpc, indicating that the polarization in population aging is more severe than economic development. Significant positive correlations between coupling coordination degree and sustainable competitiveness are detected. Conclusions: Policymakers should fully consider regional differences and sustainable development in policy formulation of China. The western and northeastern provinces should be given priority in the regional sustainable development plan. At the same time, the coordination between population aging and economic development also requires to be examined especially. [ABSTRACT FROM AUTHOR]

Copyright of Acta Vet Indones. The Indonesian Veterinary Journal / Jurnal Acta Veterinaria Indonesiana is the property of IPB University 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.)

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This 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.)

Abstract Background The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service. Methods We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19. Results We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient’s centroid which was highly concentrated in West Jakarta. Conclusions Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19′ case in support to the implementation of social distancing in Jakarta.

The contradiction between economic growth, social development, and water environment deterioration represent significant challenges for river basin sustainable development. By constructing an indicator system of river basin sustainable development, the entropy method is adopted to conduct a quantitative evaluation of the cities sustainable development level for the Weihe River Basin in Shaanxi Province from 2009 to 2018, and the standard deviational ellipse is used to analyze the evolution of spatial distribution pattern of sustainable development in the study area. Furthermore, the obstacle degree model is applied to analyze the main obstacle factors restricting the improvement of river basin sustainable development. The results show that the sustainable development level of the Weihe River basin in Shaanxi Province improved slowly during the study period and significant regional differences among cities. This study provides a novel approach for future evaluation on sustainable development of the Weihe River basin and even the arid region in Northwest China, to achieve a win-win situation between economic and social development and ecological environment protection.

The development of green residences is crucial to reducing energy consumption and carbon emissions of the construction industry. However, the study on the spatial distribution characteristics of green residences and its influencing factors has not attracted enough attention in the academic circles. Base on the panel database on the number of each star green residences and their driving factors at the municipal level from 2008 to 2016, this paper employed the Weighted Standard Deviational Ellipse model to reveal the spatial and temporal evolution features of green residences in China, creatively introduced an improved Gini coefficient (G′-score) to measure the green residences development in each city, and utilized the panel Tobit model and average marginal effect to identify the driving mechanisms and key factors of green residences from economy, society, the real estate market, policy and climate. The main conclusions show that: (1) China has formed a relatively stable and clear temporal and spatial evolution path since 2011, such as the center of gravity and coverage having moved to the west, and the direction of development trend having weakened; (2) China’s green residence is mainly distributed in the central and eastern regions, and the main direction layout is northeast–southwest; (3) the development of green residences is the result of the interaction of various factors, and the driving force of each factor varies greatly under the single action and the interaction; (4) the driving effects of the same factor on green residences with different star ratings are inconsistent in sign, magnitude, and significance, the same as for each factor under the same star rating.

Copyright of Studia Regionalne i Lokalne is the property of University of Warsaw 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.)

Recognition of small moving targets in space has become one of the frontier scientific researches in recent decade. Most of them focus on detection and recognition in star image with sidereal stare mode. However, in this research field, few researches are about detection and recognition in star image with track rate mode. In this paper, a novel approach is proposed to recognize the moving target in single frame by machine learning method based on elliptical characteristic extraction of star points. The technical path about recognition of moving target in space is redesigned instead of traditional processing approaches. Elliptical characteristics of each star point can be successfully extracted from single image. Machine learning can achieve the classification goal in order to make sure that all moving targets can be extracted. The experiments show that our proposed approach can have better performance in star images with different qualities. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department 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.)

Background: The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service.Methods: We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19.Results: We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient's centroid which was highly concentrated in West Jakarta.Conclusions: Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19' case in support to the implementation of social distancing in Jakarta. [ABSTRACT FROM AUTHOR]

The economic and social development of a country rely heavily on transportation. In China, it has become the third largest energy consumption sector and generates substantial amounts of carbon emissions. In the present study, direct and indirect carbon emissions from the transportation industry throughout China's 30 provinces during 1997–2017 were calculated. Further, to reveal the spatio-temporal characteristics of the relationship between carbon emissions and economic growth, the standard deviational ellipse and Tapio decoupling method were employed. The main results are as follows. (1) The total carbon emissions from the transportation industry increased from 132.79 million tons (Mt) in 1997 to 849.64 Mt in 2017, with an average annual growth rate of 9.72%; direct carbon emissions accounted for approximately 86% of the total. (2) Carbon emissions as well as the added value of the transportation industry had the same spatial distribution characteristics, presenting a northeast–southwest pattern during 1997–2017. Although their spatial distribution patterns were mainly in the north–south direction, the development in the east–west direction became increasingly obvious. (3) The decoupling index in the transportation industry was greater than 0.8 for most years, with an expansive negative decoupling state or an expansive coupling state. The differences in carbon emissions and economic growth between various provinces showed a spatio-temporal disparity of the decoupling states in the transportation industry. The obtained results are of considerable interest for China's policymakers to set more reasonable carbon emission reduction goals and implement targeted policies according to the carbon emission situation at a local scale. [ABSTRACT FROM AUTHOR]

Studies about climate change and the variation of the spatial pattern of war are extremely scarce in academia at present. Therefore, the temperature series and battle coordinates in imperial China from AD 5 to 1911 are integrated in this research, and their long-term quantitative relationship is examined by employing mathematical statistics such as one-way ANOVA, as well as the spatial analytical tool, standard deviational ellipse (SDE) in ArcGIS. Meanwhile, the temperature sequence is divided into three multicentennial warm–cold cycles, which are combined with different types of war (all war, agri-nomadic conflict, and rebellion) to reveal the spatial disparity of war under the influence of secular and periodic temperature change. Results show that (1) battle longitude and battle latitude are significantly different between warm and cold phases. (2) SDEs stretch toward the north/west/northwest in warm intervals but retreat south-/east-/southeastward in cold stages. (3) SDEs generally shift southeastward over time, and the variation of latitude is more evident than that of longitude, which corresponds to the overall cooling trend throughout the past 2000 years. Based on these research findings, we conclude that temperature fundamentally regulates the spatial difference of war in imperial China via controlling agricultural and pastoral productivity. This innovative study provides a robust climatological explanation of the historical conundrum why wars in ancient China distribute with specific directions, and it also lays a foundation for spatiotemporal investigations of climate change and human responses at long-term scales in the future. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and seriously affects the development of animal husbandry. It is necessary to defend the spread of FMD. To explore the distribution characteristics and transmission of FMD between 2010 and 2017 in China, Global Moran's I test and Getis-Ord Gi index were used to analyze the spatial cluster. A space-time permutation scan statistic was applied to analyze the spatio-temporal pattern. GIS-based method was employed to create a map representing the distribution pattern, directional trend, and hotspots for each outbreak. The number of cases was defined as the number of animals with FMD for the above analysis. We also constructed a phylogenetic tree to compare the homology and variation of FMD virus (FMDV) to provide a clue for the potential development of an effective vaccine. The results indicated that the FMD outbreaks in China had obvious time patterns and clusters in space and space-time, with the outbreaks concentrated in the first half of each year. The outbreaks of FMD decreased each year from 2010 with an obvious downward trend of hotspots. Spatial analysis revealed that the distribution of FMD outbreaks in 2010, 2015, and 2017 exhibited a clustered pattern. Space-time scanning revealed that the spatio-temporal clusters were centered in Guangdong, Tibet and the junction of Wuhan, Jiangxi, Anhui. Comparison of the spatial analysis and space-time analysis of FMD outbreaks revealed that Guangdong was the same cluster of the two in 2010. In addition, the directional trend analysis indicated that the FMD transmission was oriented northwest-southeast. The findings demonstrated that FMDV in China can be divided into three pedigrees and the homology of these strains is very high while comparing the first FMDV strain with the others. The data provide a basis for the effective monitoring and prevention of FMD, and for the development of an FMD vaccine in China.

Studying the temporal and spatial evolution trends in earthquakes in an area is beneficial for determining the earthquake risk of the area so that local governments can make the correct decisions for disaster prevention and reduction. In this paper, we propose a new method for analyzing the temporal and spatial evolution trends in earthquakes based on earthquakes of magnitude 3.0 or above from 1980 to 2019 in California and Nevada. The experiment’s results show that (1) the frequency of earthquake events of magnitude 4.5 or above present a relatively regular change trend of decreasing–rising in this area; (2) by using the weighted average center method to analyze the spatial concentration of earthquake events of magnitude 3.0 or above in this region, we find that the weighted average center of the earthquake events in this area shows a conch-type movement law, where it moves closer to the center from all sides; (3) the direction of the spatial distribution of earthquake events in this area shows a NW–SE pattern when the standard deviational ellipse (SDE) method is used, which is basically consistent with the direction of the San Andreas Fault Zone across the north and south of California; and (4) the spatial distribution pattern of the earthquake events in this region is found to be clustered using the global spatial autocorrelation analysis method. This study provides a new perspective for the exploration of the temporal and spatial evolution trends in earthquakes and understanding the earthquake risk in an area.

Regional ecological health, the core of comprehensive ecosystem assessments, is an important foundation for regional exploration, environmental conservation, and sustainable development. The mountainous areas in southwest China are backward in economy, but industrialization and urbanization have been rapid in recent years. This study assessed the ecosystem health of the Sichuan and Yunnan provinces in China using a pressure-state-response (PSR) model. Spatiotemporal patterns of regional ecosystem health were analyzed from 2000 to 2016, including overall characteristics as well as local characteristics. Ecosystem health in most regions was improved over time (Y = 0.0058X − 11.0132, R2 = 0.95, P < 0.001), and areas with poorer ecosystem health decreased from half to one-third of the total area. Analysis of the primacy ratio and the variation coefficient confirmed that the gap in health scores between regions has gradually expanded since 2007, but there are more high quality regions overall (Z of Moran's index < 1.96, P > 0.05). Overall, the regional ecosystems to the east of the Hu line—an imaginary line dividing east and west China into roughly equivalent parts—were healthier than those to the west. The pressure and state scores of ecosystems were determined by physiographic condition, and the response scores by government policies and social concern. The spatiotemporal patterns of ecosystem health were dominated to a greater extent by natural than anthropogenic factors, which explains why the shift in the patterns aligned with the direction of the Hu line. Dividing regions into key management areas based on natural geographical conditions and socioeconomic development could contribute to the formulation of a reasonable ecological and environmental protection policy, guaranteeing ecosystem services in the long run. [ABSTRACT FROM AUTHOR]

Scientific identification of the spatial evolution process of rural settlements is an important basis for optimizing the space of rural production, living and ecology in rural revitalization. In this paper, Jimei District of Xiamen city, which locates in the Southern Fujian Province, was selected as a typical study area. Based on the classification and interpretation data of remote sensing images at four time points from 1986 to 2017, the spatial evolution process and mechanism of rural settlement land in Jimei District in recent 30 years were quantitatively identified by using standard deviation ellipse, nuclear density and spatial autocorrelation analysis methods. The results showed that the density of rural settlement could be divided into background area, low density area, medium density area and high density area in Jimei District. There was spatial heterogeneity in the density distribution of rural settlements in Jimei District. The medium and high density areas were concentrated in central areas with good agricultural conditions. The high density areas were far away from the centers of urban and industrial mine. There were more obvious spatial continuity features between the high and low density area. The total area of rural settlements showed an increasing trend year by year. The evolution process of settlements was dominated by correlated expansion. Although the number of patches had decreased, the total area and average area of the settlements continued to increase. The combined impact of correlated expansion and contraction leaded to an increasing difference in the size between large and small settlements. With rapid development of urban-rural integration and rural economy, the hot spots of settlement expansion appeared in traditional agricultural industry concentration areas. With the settlement land around Jimei and Xinglin City transformed into state-owned land, the low-low cluster areas appeared. Combined with analysis of remote sensing image, the contracted settlements were closely related to urbanization and industrialization, and the settlements with good traffic location were more susceptible to urbanization and industrialization. The spatial distribution of contracted patches in rural settlements was random, and the spatial autocorrelation existed in the expanded patches. From 1986 to 2017, the expansion of rural settlements did not generate a signification cold zone, and the hotspot clustering pattern was dominated by high clusters in Jimei District. The basic spatial pattern of settlements remained stable due to the combination of topographic factors and evolution characteristics. It was found that there were spatial differences in the hot spots of settlement evolution in various periods by visualizing the rate and direction of the center of gravity of the settlements in each period. The core density analysis and partial autocorrelation analysis revealed a close relationship between settlement evolution and geographical environment, industrial evolution and urban and rural relationship change, and reflected the driving characteristics and internal mechanism of urban and rural interaction process of rural settlement spatial evolution in different development stages under the background of industrialization and urbanization in Jimei District. The results of this study could provide theoretical and methodological supports for optimizing development layout of rural settlements and coordinating rural revitalization planning and land remediation planning. [ABSTRACT FROM AUTHOR]

The multidimensional poverty index (MPI) is generally credited for better capturing the various components of poverty. Where such indexes have a spatial component, opportunity arises for analyzing changes in the spatial concentration of multidimensional poverty over given periods across space. Using current available MPI data for Gauteng province, South Africa, we apply spatial statistical analysis techniques to measure the degree of spatial concentration, spread and orientation of poverty across the various wards. Results reveal distinct variations in concentration, spatial spread and orientation of poverty across the province. These results open up possibilities of spatially targeted state interventions for reducing poverty. [ABSTRACT FROM AUTHOR]

The paper presents an original application of the Gini's centre of population (MC) and its Standard Deviational Ellipse (SDE) in the field of spatial distribution of foreigners. The proposed measures have been applied to the top five foreign communities (and Italians) counted in 2011 Italian demographic census in the municipality of Rome and Naples. The results show that, compared to Italians, in both municipalities the communities coming from Asia show spatial distributions characterized by quite clear residential patterns: a tendency to have high levels of spatial concentration and the centre of gravity (or mean centre) located at comparatively high distances from the one of the Italians. On the contrary, the communities coming from Central and Eastern Europe present a spatial residential pattern characterized by a quite high level of spatial dispersion and a centre of gravity very close to the one of the Italians. In an intermediate situation between these two spatial patterns there are the Peruvians. Some explanatory hypotheses are advanced. The Gini's centre of population and its Standard Deviational Ellipse seem capable to synthesize the spatial distribution of foreign population and to detect their residential patterns. [ABSTRACT FROM AUTHOR]

Abstract Coordinating the dilemma of economic development and reducing carbon emissions is of great significance to reaching China's energy-saving and emission-reduction targets. This paper investigates the decoupling relationship between economic growth and carbon emissions from the construction industry of China's 30 provinces, and uses the standard deviational ellipse method to explore the spatial evolution of carbon emissions and the economy. The results indicate that the economic development levels of most provinces were positively correlated with carbon emissions. The spatial differences in the decoupling state of provincial construction industry are significant, and the decoupling states of the same type exhibited a certain spatial aggregation phenomenon. Furthermore, the spatial distribution of the output value and carbon emissions exhibited a northeast-southwest pattern. The weighted mean centers of both were located in the east and moved towards the northwestern region. These results may provide a basis for assessing regional construction carbon emissions and formulating strategies for the coordinated development of low carbon emissions in the construction industry. Highlights • The standard deviational ellipse method is employed. • It studies the spatial pattern of economic growth and carbon emissions. • It demonstrates the dynamic evolving trend of the decoupling relationship. • It can further promote carbon emission reduction at the provincial level. [ABSTRACT FROM AUTHOR]