Spatial Statistical Analysis Method Research Articles

Spatial patterns, factors, and ethnic differences: A study on ethnic minority villages in Yunnan, China

This paper takes ethnic minority villages in Yunnan province of China, which have significant characteristics of multi-ethnic integration, as the research object. Through various spatial statistical analysis methods, we analyzed the overall distribution characteristics of ethnic villages in Yunnan, the distribution difference among various ethnic groups, and the influencing factors of the distribution pattern. It was found that: (1) The ethnic minority villages in Yunnan generally exhibit a kernel density characteristic of “three concentrated areas and multiple scattered points” and distribution characteristic of “hot spots in the west and cold spots in the east”. Each ethnic group has a relatively concentrated region, presenting significant ethnic autocorrelation. (2) The differences in the relationship between villages and landscape among various ethnic groups have given rise to a unique three-dimensional distribution pattern, which is relate to ethnic origin and livelihood culture. Meanwhile, Han traditional villages tend to occupy areas with relatively better geographical conditions. (3) Natural factors, such as topographic relief, the distance between ethnic villages and rivers, and elevation, have a significant impact on the distribution of ethnic villages in Yunnan. Meanwhile, test results of social and human factors, including the distance from central cities, per capita GDP, and the gross annual output values of primary industry, indicate that the development of social economy and the promotion of urbanization pose challenges to the preservation of ethnic villages.

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.

The relationship between geological disasters with land use change, meteorological and hydrological factors: A case study of Neijiang City in Sichuan Province

Sichuan hilly area is located in southwest China, which have many hilly area, is a densely populated, economically active, frequent geological disasters area. In this paper, Neijiang City in the region is taken as the test sample. This article, which analyzes Land-Use and Land-Cover Changes (LUCC), meteorological and hydrological data from 2015 to 2020, takes Neijiang City in this region as the research object. We explored the relationship between geological disasters such as collapse and landslides that often occur in the natural ecological environment with LUCC and meteorological and hydrological factors, using GIS spatial analysis and statistical analysis methods. The research results indicate that geological disasters mainly occur in farmland areas. The density of geological disaster points is highest within urban areas, making them the most prone to geological disasters. Geological disasters are more densely distributed in the area within 600 to 800 m from the river. There is a strong correlation between the average rainfall amount and the probability of geological disasters occurring. This study provides a scientific basis for the geological disaster risk assessment, prevention and control in this region. It has certain reference significance for the ecological environment governance, land resources planning and management in other similar regions.

Analyzing synergistic effects of tourist attractions on sustainable development of neighborhoods with emphasis on urban smart growth principles

ABSTRACT Despite the spatial effects of tourist attractions in promoting smart growth principles in tourist cities, it is necessary to pay attention to the spatial synergy between tourist attractions in order to control their destructive spatial effects and realize sustainable development (SD) in tourist cities. In this research, based on smart growth measures, the necessity of spatial synergy between tourist attractions in sustainable neighborhood development is explained using innovative spatial statistical analysis methods at the scale of 469 neighborhood units of Mashhad city. The findings of the research confirm significant synergistic effect of tourist attractions on urban smart growth indicators and composite indicator of smart growth of the surrounding neighborhood units. On the other hand, lack of spatial synergy between Mashhad’s main tourist attraction in the city center and other tourist attractions leads to unbalanced residential and activity development and unbalanced distribution of tourism services. This issue prevents integrated realization of smart growth and SD of all neighborhood units in Mashhad.

Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).

Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries

Various inaccurate traditional models have resulted in major ambiguities and gaps in the interpretation of Anatolian plate deformation directions. To address this issue, a GIS-based spatial statistical analysis method was used for the first time to detect the directional distribution of deformation along the Anatolian Plate in Turkey. Two strategies were used in this study: firstly, identifying the abnormally active seismic areas by detecting significant hotspot and cold spot clusters and confirming this detection using optimized hotspot analysis for earthquake events that occurred from 1900 to the end of 2019. Secondly, detecting the directional distribution of deformation using a Standard Deviational Ellipse (SDE) by calculating the standard deviation of the x and y coordinates from the mean center for each set of earthquake events in the Anaconda Python Platform and ArcGIS 10.8 software. Our improved geostatistical analysis results confirmed the existence of abnormal seismic hazard zones within the study area and three deformation directions: the east-west trend, the southeast-northwest trend, and the south-north trend.

The Association between Street Built Environment and Street Vitality Based on Quantitative Analysis in Historic Areas: A Case Study of Wuhan, China

Over the past decade, enhancing the quality of cities and building vibrant urban streets has become a hot topic in urban planning in China. Although there are many studies on how the built environment affects street vitality, the unique built environment of the street space in historic areas, as the core node of the city, has not been fully explored. This study constructs an association model between the street built environment (SBE) and street vitality in historic areas and evaluates the influence of SBE on street vitality by spatial analysis and statistical analysis methods using POI data, road network data, and Baidu heat map data, taking Wuhan, China, as an example. The results showed that (1) appropriate built environment development intensity, street width-to-height ratio, and facade ratio of historic buildings on the street frontage all can promote street vitality; (2) the spatial distribution of historic buildings converted to commercial functions in historic areas has a high consistency with the spatial distribution of street vitality, and the consistency is significantly higher than that of general urban streets; (3) historic buildings converted to residential functions and those in vacancy or under renovation in historic areas have a significant inhibitory effect on street vitality; and (4) the spatial distribution of transportation facilities and the spatial distribution of street vitality are mutually exclusive in historic areas. This study proposes a method for studying the SBE and street vitality in historic areas and initially explores the relationship between the influences of the SBE on street vitality in historic areas. Since the functional replacement of historic buildings can affect the street vibrancy in historic areas, our findings suggest moderating commercial renovation rather than simply repairing or maintaining the status for enhancing the street vitality. Moreover, the intervention of transportation facilities will reduce the street vitality in historic areas, which provides a basis for the strategy of renewing historic areas into pedestrian street spaces.

Spatial–Temporal Evolution Characteristics of Landscape Ecological Risk in the Agro-Pastoral Region in Western China: A Case Study of Ningxia Hui Autonomous Region

Landscape ecological risk assessment can reflect the impact of landscape pattern on ecological processes and functions, and has become an important method for predicting and measuring the quality and dynamic evolution of the ecological environment. Studying the trend of landscape ecological risk evolution is important for optimizing the regional landscape pattern and maintaining the sustainable development of the ecological environment in ecologically fragile areas. Ningxia Hui Autonomous Region is a typical agro-pastoral region in northern China, which is facing prominent ecological and environmental problems such as soil erosion and land desertification. With the environmental problems becoming more and more serious, the ecological risk also poses increasing potential danger, which becomes one of the bottlenecks restricting regional economic development. This paper selects Ningxia Hui Autonomous Region as the study area. Based on land use data in 2000, 2005, 2010, and 2015, the landscape ecological risk assessment model is built from the perspective of landscape ecology, and combines the spatial statistical analysis method and the gravity shift model to explore the spatial–temporal evolution of the spatial distribution characteristics of landscape ecological risk. The results showed that: (1) During the study period, the area of farmland and grasslands has decreased, and the area of forest, water, and settlement has increased; (2) The transformation of land-use types is mainly distributed in the central northern plain and the south valley, where human activities are intensive. The transfer relationship among different land-use types is mainly between grasslands and farmland; (3) The hotspots and aggregation area of landscape ecological risk are mainly distributed along the city belt and irrigated agricultural areas along the Yellow River in the northern Ningxia Plain; (4) From 2000 to 2005, the landscape ecological risk level decreased, and from 2005 to 2015, the landscape ecological risk index continued to rise. We conclude that the natural environment of Ningxia determines the characteristics of the spatial distribution of landscape ecological risk, while the use and modification of land through human activities are the main driving factors of landscape ecological risk change. The results of landscape ecological risk assessment at different scales based on the landscape index will provide support for regional environmental management.

Regionalization and Analysis of PM2.5 and O3 Synergetic Prevention and Control Areas Based on Remote Sensing Data

Site-based air pollution monitoring data cannot support the regionalization of air pollution prevention and control areas. Faced with this problem, this study proposed a method of regionalizing synergetic prevention and control areas based on multi-source remote sensing data and GIS spatial statistical analysis methods and carried out quantitative analyses of PM2.5 and O3 air pollution in China from 2015 to 2020. The results showed that there was an obvious decrease in PM2.5 concentrations, and O3concentrations remained stable; PM2.5 pollution mostly occurred in autumn and winter, and O3 pollution occurred in spring and summer. A significant spatial inconsistency was shown between the change rate of PM2.5 and O3 concentrations, in which the proportions of PM2.5 decreasing and O3 increasing, PM2.5 and O3 both decreasing, PM2.5 and O3 both increasing, and PM2.5 increasing and O3 decreasing accounted for 38.34%, 35.12%, 15.24%, and 10.89%, respectively. The results also showed that the boundary of PM2.5 and O3 synergetic prevention and control areas was dynamic during 2015 and 2020, showing a trend of expanding from 2015 to 2018 and then becoming smaller after 2019. Generally, the scope of PM2.5 and O3 synergetic prevention and control areas was concentrated in "2+26" cities, Fenwei plain, north of the Yangtze River Delta, and Shandong. In contrast, the regional scopes of "PM2.5 first" and "O3 first" were relatively stable. Areas of "PM2.5 first" were mainly carried out in Liaoning-Jilin, Hubei-Hunan-Jiangxi, Chengdu-Chongqing, and Taklimakan-Hexi Corridor, whereas "O3 first" areas were mainly in specific regions of the Pearl River Delta, Yangtze River Delta, and surrounding areas of Bohai Bay. Remote sensing-based PM2.5 and O3 mapping has the advantages of full-coverage and fine spatial simulation, which can support the regionalization of synergetic prevention and control areas and implementation of policies.

Analysis of Spatiotemporal Characteristics and Influencing Factors for the Aid Events of COVID-19 Based on GDELT

The uncertainty of COVID-19 and the spatial inequality of anti-pandemic materials have made international aid an important means for many countries to cope with this global public health crisis. It is of far-reaching significance to analyze the spatiotemporal characteristics and influencing factors of international aid events for the global joint fight against COVID-19 and the sustainability of global public health business. The data on aid events from 23 January 2020 to 31 October 2021, were from the GDELT database. China, the United States, the United Kingdom, and Canada were selected as the study objects because they provided more aid. Their spatiotemporal characteristics of main aid flows, the response characteristics of the aid requests, and the characteristics of verbal aid to cash in were studied using spatial statistical analysis methods. The influencing factors of aid allocation also were studied by regression analysis. The results found that: the international aid flow of each country was consistent in spatial distribution, mainly to countries with severe pandemics and neighboring countries. However, there were differences in the recipients. China mainly aided developing countries, while the United States, the United Kingdom, and Canada mainly aided developed countries. Relatively speaking, China was more responsive to aid requests and more aggressive in cashing in on verbal aid. The countries considered the impact of their economic interests when they planned to aid. At the same time, there were obvious “bandwagon effect” and “small country tendency” on the aid events.

Spatiotemporal Coupling Effect of Regional Economic Development and De-Carbonisation of Energy Use in China: Empirical Analysis Based on Panel and Spatial Durbin Models

The synergistic development of economic construction and low-carbon transformation of energy systems must be promoted for building a green, low-carbon, and cyclic economic system and achieving the “double carbon” goal in China. Based on the panel data of 30 provincial-level administrative regions in China from 2015 to 2019, the global entropy method, coupling coordination degree model, and spatial statistical analysis methods are applied to analyse the factors affecting the coupling effect. The coordination degree increased in the study period, with Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang being the regions with the highest values. The spatial distribution of the coupling coordination degree is strongly positively correlated with the eastern provincial-level administrative regions located in the high–high concentration area of the Moran scatterplot and western provincial-level administrative regions concentrated in the low–low concentration area. The spatial association pattern is stable in the study period, with only two provinces exhibiting a transition: Shandong province made the transition to high–high agglomeration areas, and Liaoning province made the transition to low–low agglomeration areas. The level of regional economy, urbanization process, energy consumption structure, and level of investment in science and innovation enhance the coupling coordination degree, whereas the industrial structure deteriorates this degree.

Spatial structure characteristics and effects of self-driving tourism flows before and after the new crown epidemic: Taking Yunnan Province as an example

<p>To obtain the impact of the new crown epidemic on the spatial structure of self-driving tourism flow during the Spring Festival Golden Week, social network and spatial statistical analysis methods are used to integrate road traffic flow big data and travelogue data to analyze the spatial structure characteristics of self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2018 and 2021. The results show that: 1) The self-driving tourism flow in Yunnan Province during the Spring Festival Golden Week in 2021 shows the “dragonfly” spatial clustering characteristics of “two centers, one axis and two wings”, and the new sub-core area of Qujing is added to the core area of Kunming in 2018, and the self-driving tourists are affected by the epidemic. 2) During the Spring Festival Golden Week in 2021, compared with 2018, there is no significant change in the spatial structure of tourism flow into Yunnan from outside the province, but the degree of intermediary centrality of provincial boundary nodes is significantly weakened, and the self-driving tourism flow at Wenshan junction and Lijiang junction decreased by 72.54% and 87.26%. The New Crown epidemic hindered the development of self-drive tours into Yunnan from Guangxi and Sichuan. 3) During the New Crown epidemic, tourists were less willing to visit hotspot cities, and showed overall behavioral preference characteristics of avoiding crowd gathering and focusing on health and safety. Under the New Crown epidemic, the tendency of long-distance self-driving shifts to close distance self-driving between neighboring cities centered on Kunming.</p><div><p> </p></div>

Spatial and Temporal Distribution Characteristics of Flower-Viewing Tourism and Its Influencing Factors in China

Despite the rapid development of flower-viewing tourism in China in recent years, there is almost no systematic research on it. Therefore, this study analyzes the spatial and temporal distribution characteristics of flower-viewing tourism and its influencing factors in China using the spatial statistical analysis methods and the geographic detector method. The study uses the Point-of-Interest data of flower-viewing tourist attractions from networks such as Qunar and Ctrip, the flower observation data from China Phenological Observation Network, Chinese network news, and Weibo, and the statistical data from yearbooks. The results are as follows: 1) The spatial attribution type of flower-viewing tourism in China is aggregated into areas, including two high-density aggregated areas, three medium-density aggregated areas, and one general-density aggregated area. Furthermore, five major types of flower-viewing tourist attractions have formed several aggregated areas. 2) The time of flower viewing in China starts from about February and lasts about eight months till October each year. Florescence and flowering time of different ornamental flowers in different regions are different. 3) The spatial and temporal distribution characteristics of flower-viewing tourism in China are mainly affected by ornamental flower phenology, spatial distribution characteristics of flower-viewing resources, regional permanent population size, youth population size, female population size, regional GDP, and added value of the tertiary sector. These conclusions clarify the spatial and temporal distribution characteristics of flower-viewing tourism and its influencing factors in China. They could provide a scientific basis and useful reference for the coordination and sustainable development of regional flower-viewing tourism in China.

Spatiotemporal Distribution of Continuous Air Pollution and Its Relationship with Socioeconomic and Natural Factors in China.

Continuous air pollution (CAP) incidents last even longer and generate greater health hazards relative to conventional air pollution episodes. However, few studies have focused on the spatiotemporal distribution characteristics and driving factors of CAP in China. Drawing on the daily reported ground monitoring data on the ambient air quality in 2019 in China, this paper identifies the spatiotemporal distribution characteristics of CAP across 337 Chinese cities above the prefecture level using descriptive statistics and spatial statistical analysis methods, and further examines the spatial heterogeneity effects of both socioeconomic factors and natural factors on CAP with a Multiscale Geographically Weighted Regression (MGWR) model. The results show that the average proportion of CAP days in 2019 reached 11.50% of the whole year across Chinese cities, a figure equaling to about 65 days, while the average frequency, the maximum amount of days and the average amount of days of CAP were 8.02 times, 7.85 days and 4.20 days, respectively. Furthermore, there was a distinct spatiotemporal distribution disparity in CAP in China. Spatially, the areas with high proportions of CAP days were concentrated in the North China Plain and the Southwestern Xinjiang Autonomous Region in terms of the spatial pattern, while the proportion of CAP days showed a monthly W-shaped change in terms of the temporal pattern. In addition, the types of regions containing major pollutants during the CAP period could be divided into four types, including “Composite pollution”, “O3 + NO2 pollution”, “PM10 + PM2.5 pollution” and “O3 + PM2.5 pollution”, while the region type “PM10 + PM2.5 pollution” covered the highest number of cities. The MGWR model, characterized by multiple spatial scale impacts among the driving factors, outperformed the traditional OLS and GWR model, and both socioeconomic factors and natural factors were found to have a spatial non-stationary relationship with CAP in China. Our findings provide new policy insights for understanding the spatiotemporal distribution characteristics of CAP in urban China and can help the Chinese government make prevention and control measures of CAP incidents.

Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data.

Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based view, an effective evaluation of the green space exposure inequity at the community level remains challenging. In this study, we presented a green space exposure inequity assessment framework, integrating the Green View Index (GVI), deep learning, spatial statistical analysis methods, and urban rental price big data to analyze green space exposure inequity at the community level toward a “15-minute city” in Zhengzhou, China. The results showed that green space exposure inequality is evident among residential communities. The areas in the old city were with relatively high GVI and the new city districts were with relatively low GVI. Moreover, a spatially uneven association was observed between the degree of green space exposure and housing prices. Especially, the wealthier communities in the new city districts benefit from low green space, compared to disadvantaged communities in the old city. The findings provide valuable insights for policy and planning to effectively implement greening strategies and eliminate environmental inequality in urban areas.

THE EFFECT OF VEGETATION EXISTENCE AS CLIMATE ADAPTATION IN THE OLD CITY OF SEMARANG

The increase in air temperature, which is quite extreme due to climate change, causes the comfort level of outdoor activities to be increasingly disturbed. Various efforts have been made to overcome the increase in air temperature by creating a greener environment and providing vegetation. This study aims to produce a pattern of vegetation distribution and its effect on the climate for decreasing outdoor air temperature in the Old City of Semarang, Indonesia. The data used in this study are primary in the form of air temperature data at 09.30 WIB and data on regional vegetation conditions at four stations. Secondary data is also used, mainly in real-time monitoring of regional activities on CCTV Online media owned by the Semarang City government. The data analysis method uses GIS and Ecotec-based spatial simulations and descriptive statistical data analysis methods to obtain vegetation distribution patterns and characteristics. The result is that the presence of vegetation as much as 5% of the total land area is still less influential in reducing air temperature due to climate change. The difference between this research and previous research is that it is located in a cultural heritage area. It requires special attention in providing vegetation so as not to damage the physical structure of the building. This study also found that the density of human activities around the vegetation site can inhibit microclimate modification. The vegetation coverage factor greatly affects the microclimate produced in the surrounding area.

Perubahan Aktivitas Ekonomi dan Pemanfaatan Lahan di Koridor Jalan Pantura Setelah Beroperasi Jalan Tol Cipali

The existence of Pantura road as a physical facility is to support the daily needs of the people surrounding the Pantura Eretan Kulon Road. However, the operation of the Cikopo-Palimanan (Cipali) toll road in June 2015 caused the movement switch from Pantura Road to the Cipali toll road given a change in economic activity and land use in Pantura Eretan Road Corridor. The purposes of this study are to identify changes in economic activities and land use after the operation of the Cipali toll road by comparing the economic activities and land changes in the year of 2014 and 2019. Spatial analysis method and descriptive statistical analysis method were used in this study. The results of the analysis show that the operation of the Cipali toll road have led to a decrease in the number of business activities, especially the food business, a decreased income in the Pantura Road Corridor which is still below the Regency Minimum Wage (UMK) Indramayu and based on BPS is still low, changes conversion of the widest land use function in trade and services to become yard land which has no economic value, and a decrease in land use based on the function of buildings in the corridor of Jalan Pantura which is dominated by restaurants. On the other hand, the operation of the Cipali toll road results in a decrease of traffic volume on Jalan Pantura due to the reduced traffic generation so that the traffic congestion is reduced.

Understanding land surface temperature impact factors based on local climate zones

The local climate zone (LCZ) and land surface temperature (LST) have gained considerable attention as urbanization continues to increase. However, the study of LSTs lacks a regional complexity perspective. In order to explore the law of urban thermal environment, impact factors of LSTs are identified using GIS spatial analysis and statistical analysis methods in conjunction with parameter models that reflect urban spatial morphologies on the LCZ scale. The research results show that the LST ranges from 24.90 °C (LCZA) to 33.26 °C (LCZ2) in the summer of 2017 and from 2.53 °C (LCZ7) to 2.89 °C (LCZ3) in winter; LST ranged from 22.00 °C (LCZ2) to 28.19 °C (LCZE) in summer 2019, and from -4.79 °C (LCZ10) to -2.12 °C (LCZ3) in winter. Different LCZs had different impacts on LSTs. LST is always positively correlated with the floor area ratio, with a maximum correlation coefficient of 0.682 in LCZ2. It exhibits the highest positive correlation (correlation coefficient = 0.421) with average building height in LCZ3 and the highest negative correlation (correlation coefficient = -0.706) in LCZ7; vegetation and water bodies have a cooling effect. These results can serve as a valuable reference for building cool communities and improving the living environment of residents.

The Temporal and Spatial Differentiation Characteristics of Three Industry Convergence Development in Deeply Impoverished Areas in China

The integrated development of the three industries is the focal point and breakthrough point for the realization of industrial poverty alleviation in the deeply impoverished areas. This paper, taking 169 poverty-stricken counties in deeply impoverished areas as the research object, calculated the level of three industrial integration development in deeply impoverished areas from 2013 to 2016. Based on this, the spatial statistical analysis method was used to explore the spatial and temporal differentiation characteristics of the three industrial integration development levels in deeply impoverished areas. The results show that the level of integrated development of the three industries in the deeply impoverished areas of China is generally low, and there is great room for improvement. There are significant spatial correlation and spatial heterogeneity, and the phenomenon of bipolar agglomeration is more and more obvious. This study can provide a theoretical basis and decision-making reference for the formulation and implementation of poverty alleviation policies in deeply impoverished areas.

Detection of spatial-temporal variations in forest canopy surface temperature in response to urbanization: a case study from Longyan, China

Urban forests are fundamental components of localized surface energy budgets. Understanding the factors controlling urban forest surface temperatures (UFSTs) should be helpful in mitigating the negative effects of urbanization on urban energy budgets. This study aimed to identify the factors controlling the spatial-temporal pattern of UFSTs by utilizing a variety of data layers and spatial statistical analysis methods. Our results showed that UFST values become more spatially heterogeneous as urbanization progresses. Elevation and degree of slope were the main factors explaining the increase in spatial heterogeneity. Human activities were also significantly related to variations in UFST. Interactions between human activities and almost all environmental factors were related to higher UFST values. Therefore, human activity directly impacts on the spatial heterogeneity of UFST and indirectly affects variations in landscape patterns. Human activities compatible with ecologically sustainable development should be considered for mitigating the deterioration of urban thermal environments.