Moran Scatterplot Research Articles

Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces

The Spatial linkages between regions are an important part of a regional economy as an analysis of relations and interactions between regions. Regional economic studies tend to focus only on the independence of a region so that it does not consider the spatial effects that occur between one region and another. This study focuses on looking at spatial autocorrelation which will produce spatial patterns and spatial linkages between regions of the gross regional domestic product and the human development index, with the findings of seeing spatial interactions and patterns of similarities or differences in the formation of the two variables between regions. The research area focuses on 60 districts and cities in 5 provinces of Southern Sumatera with the 2015-2019 research year, the analysis method uses Geographic Information Systems with Geoda software, the results of the calculations will produce Moran’s I, LISA Significance and Clustered maps. The outcomes show that there has been a spatial relationship as certain autocorrelation of GRDP and HDI, the formation of the economy and human capital has a spatial relationship, results of Moran'I show that the positive value of the two variables has a group pattern and has a level of formation of GRDP and HDI with the same characteristics, moran scatterplot shows the similarity with the resulting region divided into 4 quadrants. The LISA cluster map and the LISA marking are the similarities in the findings of the GRDP and HDI results, but both variables have no findings of Low-high patterns.

Pola Sebaran Permukiman Kumuh di Kabupaten Tangerang dan Keragaman Spasial Faktor yang Mempengaruhinya

Rapid development and high population density are among the factors that cause slum settlements Tangerang Regency. Factors that cause slum settlements in Tangerang Regency have not been identified specifically and the handling of slum settlements are still seen to be uniform between regions. The purpose of this research is to identify the pattern of distribution of slum settlements in Tangerang Regency and the spatial diversity of factors that influence it. Methods used to identify the distribution pattern of slums are Moran's Index, Moran's Scatterplot, and Local Indicator Spatial Autocorrelation. Meanwhile, to understand the spatial diversity of the factors that affect slum settlements, multiple regression analysis was performed using forward stepwise regression, followed by Geographically Weighted Regression (GWR) analysis. Results shows that the pattern of spatial distribution of slum settlements in 2016 and 2018 was concentrated in the northern and southern areas of Tangerang Regency. The distribution pattern of slums shows a centered movement towards the district border with the surrounding areas, namely DKI Jakarta, Tangerang City, and South Tangerang City. The relationship between the Village Development Index and the Land Value Zone has a fairly weak positive correlation, the relationship between the Kelurahan Development Index and the percentage of slum area does not have a significant correlation, while the Land Value Zone with the percentage of slum area has a weak positive correlation. Slum settlements in Tangerang Regency are influenced by physical and non-physical factors including the number of slum location points, the percentage of residential buildings that do not have a building permit, and the percentage of areas affected by inundation. The results of the GWR analysis show that each factor that has a significant influence on slum settlements in Tangerang Regency has a different influence locally in each observed area.

Spatial pattern and predictors of malaria in Ethiopia: Application of auto logistics regression.

IntroductionMalaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia.MethodsA weighted sample of 15,239 individuals with rapid diagnosis test obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran’s I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran’s I statistic was used in identifying exposed areas. The auto logistics spatial binary regression model was used to investigate the predictors of malaria.ResultsThe final auto logistics regression model was reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR = 2.401, 95% CI: (2.125–2.713) ]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR = 52.55, 95%CI: (40.54–68.12)] followed by Beneshangul [AOR = 34.95, 95%CI: (27.159–44.963)]. Similarly, individuals in Amhara [AOR = 0.243, 95% CI:(0.195–0.303], Oromiya [AOR = 0.197, 955 CI: (0.158–0.244)], Dire Dawa [AOR = 0.064, 95%CI(0.049–0.082)], Addis Ababa[AOR = 0.057,95%CI:(0.044–0.075)], Somali[AOR = 0.077,95%CI:(0.059–0.097)], SNNPR[OR = 0.329, 95%CI: (0.261–0.413)] and Harari [AOR = 0.256, 95%CI:(0.201–0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for one meter increase in altitude, the odds of positive rapid diagnostic test (RDT) decreases by 1.6% [AOR = 0.984, 95% CI: (0.984–0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR = 1.671, 95% CI: (1.504–1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression.ConclusionsThis study found that the incidence of malaria in Ethiopia had a spatial pattern which is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who lived in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramics tiles roof houses. Moreover, using a protected anti-mosquito net was reducing the risk of malaria incidence.

Specifying Spatial Dependence for Teak Stands Specific to Solomon Island-Derived Clones in Tawau, Sabah, Malaysia: A Preliminary Study

The magnitude of spatial dependence on teak tree growth was examined based on a teak plantation managed by the research and development team at Sabah Softwood Berhad, Brumas camp, Tawau, Sabah, Malaysia. A sample of 432 and 445 georeferenced individual tree points specific to Solomon Island-derived clones that were 6 and 7 years old, respectively, were analyzed, as previous findings showed that this was the genotype that thrived the most. This study aims to show that spatial dependence exists in the 6- and 7-year-old teak tree blocks of the plantation and that there are changes in the magnitude of spatial dependence when it is analyzed as a continuous plot. Moran’s I values and Moran scatterplots as well as semivariograms and thematic maps were used to satisfy the hypothesis regarding the relationship between spatial dependence and the growth of the physical parameters: the diameter at breast height (DBH), height, and the volume of the teak tree. The Moran’s I values that were calculated rejected the null hypothesis, suggesting the existence of strong spatial dependence for all of the physical parameters and for both the 6- and 7-year-old samples. The semivariograms were plotted and showed an increasing trend as the lag distance between trees increased and showed changes as the trees aged. These findings prove significant spatial dependence in the growth of the physical parameters of teak trees. Hence, growth model methodologies based on spatial distribution must be developed to further understand the spatial distribution of teak tree plantations.

Dinámica de la pobreza multidimensional subnacional en los países en desarrollo: los casos de Ecuador y Uruguay

This paper studies deprivation dynamics at the subnational level, introducing a Local Multidimensional Poverty Index (LMPI), and focusing on multidimensional poverty in Ecuador and Uruguay between the last two available censuses, 1990–2010 and 1996–2011, respectively. As a first step, we construct the LMPI at the municipal level using microdata from both counties. Subsequently, we explore spatial and temporal dynamics through a set of tools such as the salter graph, Moran’s I, Moran scatterplot, and spatial transition matrix. The results indicate that compared to Ecuador, Uruguay was initially in a better position in terms of the LMPI. However, Ecuador achieved a generalized reduction of the LMPI during the period of analysis, reaching levels close to that of Uruguay. Nevertheless, spatial persistence in the LMPI is observed.

Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics

An outbreak of the COVID-19 pandemic caused by the SARS CoV 2 has profoundly affected the world. This study aimed to identify the spatio-temporal clustering of COVID-19 patterns using spatial statistics. Local Moran’s I spatial statistic and Moran scatterplot were first used to identify high-high and low-low clusters and low-high and high-low outliers of COVID-19 cases. Getis-Ord’s〖 G〗_i^* statistic was then applied to detect hotspots and coldspots. We finally illustrated the used method by using a dataset of 10,742 locally transmitted cases in four COVID-19 waves in 63 prefecture-level cities/provinces in Vietnam. The results showed that significant low-high spatial outliers of COVID-19 cases were first detected in the north-eastern region in the first wave and in the central region in the second wave. Whereas, spatial clustering of high-high, low-high and high-low was mainly found in the north-eastern region in the last two waves. It can be concluded that spatial statistics are of great help in understanding the spatial clustering of COVID-19 patterns.

Spatial Analysis for the Identification of Priority Areas Dengue in West Sumatera Province, Indonesia

Dengue is transmitted by mosquitoes in the genus Aedes, particularly Aedes aegypti and Aedes albopictus. West Sumatra Province is a tropical area in Indonesia where the two main mosquito genera are endemic in almost all regions year-round. This study analysed distribution of dengue, and defined the priority dengue areas in West Sumatra in 2017 using spatial autocorrelation (Moran’s I) and Moran’s scatter plot. The result revealed the global Moran’s index of the case patterns to be a cluster or positive correlation (I > 0, p < 0.01), and Moran scatter plot indicated associations between districts and their neighbours. Dengue cases in each district were described as high-high (HH), high-low (HL, hotspot), low-high (LH, cold spot), or low-low (LL). Districts identified as HH should be prioritized, and dengue must be controlled in HL and LH districts. In LL districts, dengue programs should be maintained. The results of this study could help implement strategies to prevent and control dengue.

Socioeconomic conditions on poverty levels a case study: Central Java Province and Yogyakarta in 2016

Abstract: This study aims to analyze how the influence of infrastructure availability, socioeconomic conditions, and the effect of location on poverty levels. The descriptive analysis is used to give a general description of poverty by using thematic charts and maps. The poverty map is analyzed by spatial autocorrelation of poverty levels by using a Moran Scatterplot and the Local Indicators of Spatial Association (LISA) Map. The results of the study indicate the existence of spatial linkages to poverty. The Increasing of other variables outside the model in neighboring regions will increase the level of poverty in a region. The infrastructures of road extension, clean water infrastructure, economic growth, quality of education, and health have a significant influence on the level of poverty, while the percentage of satisfactory sanitation did not demonstrate to affect the significant effect on poverty. The conclusion is that the level of poverty in the provinces of Central Java and Yogyakarta has an irregular distribution and a clustered spatial pattern.

Primary Health Care in Northern and Northeastern Brazil: mapping team distribution disparities.

This study analyzes the spatial pattern of implementation of Primary Health Care (PHC) teams in Northern and Northeastern Brazil. This is an ecological study on the rates of Community Health Workers (ACS), Family Health Team (eSF), Oral Health Team (eSB), and Family Health Extended Center (NASF) based on data from the Ministry of Health (MoH). The analysis of the area data identified patterns of spatial dependence of the municipalities for the rates, using Moran indices and scatterplots to visualize critical areas' clusters (95% confidence). Municipalities of the North (n=450) and Northeast (n=1,794) had 132,174 ACS, 18,405 eSF, 13,017 eSB, and 2,205 NASF. The proportion of municipalities with rates within the recommended by the MoH were: ACS (>1.33), 96% in the North and 98.5% in the Northeast; eSF (>2.9/1,000), 54% and 80% in the respective regions; eSB (>2.9/10,000) 28% and 59% in these respective regions. NASF teams were deployed in 70% of the North and 89% of the Northeast. Except for ACS, the North was a critical team area, mainly in Pará, Rondônia, Amazonas, and Amapá. In the Northeast, these areas were smaller and concentrated mainly in western Bahia and eastern Maranhão. The Northeast showed a better composition of teams and a smaller extent of critical areas.

Spatio-Temporal Variation of Health Production Efficiency Considering Environmental Pollution in China Based on Modified EBM and Spatial Econometric Model.

In order to strengthen the construction of China's health industry and improve the health of the people, based on the data of 31 provinces and cities in China from 2009 to 2019, the improved EBM model is used to measure the health production efficiency of each region, and Moran index is used to study the Spatio-temporal variation of health production efficiency of each province. Finally, the spatial econometric model is applied to study the influencing factors of the Spatio-temporal variation of health production efficiency. The results show that generally speaking, the average efficiency of 31 provinces and cities is above 0.7, and the average efficiency of some regions is above 1. From the perspective of time variation, the average efficiency value in the eastern region and the middle region increases from 0.816 to 0.882 and from 0.851 to 0.861, respectively. However, the average efficiency value in the western region and northeast region decreases from 0.861 to 0.83 and from 0.864 to 0.805, respectively. From the perspective of spatial distribution, HH agglomeration and LL agglomeration exist in most regions. By comparing Moran scatter plots in 2009 and 2019, it is found that the quadrants of most regions remain unchanged, and LL agglomeration is the main agglomeration type in local space. There is a significant spatial dependence among different regions. From the perspective of spatial empirical results, Pgdp, Med, and Pd have a positive effect on health production efficiency. The direct effect and indirect effect of Pgdp, Med, and Gov all pass the significance test of 1%, indicating that there are spatial spillover effects of the three indicators. Each region should reasonably deal with the spillover effect of surrounding regions, vigorously develop economic activities, carry out cooperation with surrounding regions and apply demonstration effect to accelerate the development of overall health production.

Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

Spatial Spillover Effects of Renewable Energy on Carbon Emissions in Less-developed Areas of China.

The purpose of this paper is to determine the spatial spillover effects of renewable energy on carbon emissions in China's less-developed areas. However, few studies have considered this issue from the perspective of less-developed areas. Based on panel data of 21 provinces in China from 2000 to 2017, this paper analyzes the spatial spillover effects of renewable energy on carbon emissions using Moran's I and Spatial Durbin Model (SDM). The results suggest that, first, Moran's I ranges from 0.378 to 0.519, Moran scatter plot presents that provinces are located in the high-high (HH) and low-low (LL) quadrants, indicating provincial carbon emissions in the study area have a significant spatial correlation and agglomeration. Second, under the three matrices, the direct effect coefficients of renewable energy are -0.2522, -0.2639 and -0.2601, this shows that renewable energy is beneficial to local carbon emissions reduction. In contrast, the indirect effect coefficients of renewable energy are 0.0605, 0.1012 and 0.1125, which means higher renewable energy consumption in a single area is conducive to the improvement of carbon emissions to neighbouring areas. Third, urbanization, industrialization, physical capital and other variables have different impacts on local and nearby carbon emissions. This study provides empirical evidence to achieve carbon emission reduction targets by government policymakers.

Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model

As a kind of air pollution, haze has complex temporal and spatial characteristics. From the perspective of time, haze has different causes and levels of pollution in different seasons. From the perspective of space, the concentration of haze in adjacent areas will affect each other, showing some correlation. In this paper, we construct a multi-convolution haze-level prediction model for predicting haze levels in different areas of Beijing, which uses the remote sensing satellite image of the Beijing divided into nine regions as input and the haze pollution level as output. We categorize the predictions into four seasons in chronological order and use frequency histograms to analyze haze levels in different regions in different seasons. The results show that the haze pollution in the southern regions is significantly different from that in the northern regions. In addition, the haze tends to be clustered in adjacent areas. We use Global Moran’s I to analyze the predictions and find that haze is related to the geographical location in summer and autumn. We also use Local Moran’s I, Moran scatter plot, and Local Indicators of Spatial Association (LISA) to study the spatial characteristics of haze in adjacent areas. The results show, for the spatial distribution of haze in Beijing, that the southern regions present a high-high agglomeration, while the northern regions exhibit a ‘low-low agglomeration. The temporal evolution of haze on the seasonal scale, according to the chronological order of winter, spring, and summer to autumn, shows that the haze gradually becomes agglomerated. The main finding is that the haze pollution in southern Beijing is significantly different from that of northern regions, and haze tends to be clustered in adjacent areas.

Spatial Analysis of the 2018 Logistics Performance Index Using Multivariate Kernel Function to Improve Geographically Weighted Regression Models

This paper analyzes the 2018 Logistics Performance Index (LPI) from the World Bank to determine the spatial effects of countries’ logistics performance. Although the standardized ordinary least square (OLS) models show good results, the spatial lags and Moran’s I of LPI suggest the OLS assumptions are violated. Consequently, an improved geographically weighted regression (IGWR) model using multivariate kernel functions (MKF) is implemented. Through the analysis of the Moran scatter plot, the authors identified the countries that have different logistics performance development trends in the four quadrants representing the relationship between the spatial lags and the LPI. Using trade activity (i.e., import/export) in the MKF, the authors compared different MKF types and bandwidths to ensure the model’s predictability and accuracy and found that the adaptive Gaussian MKF is suitable. Finally, the IGWR model indicates both positive and negative influencing factors on LPI overall score. Specifically, the improvements of LPI are more associated to economic variables in mid- and low-income countries around the world, and are more related to import of construction equipment in the Middle East. Also, business environment is more important in Latin America and the Pacific. European countries are more sensitive to customs efficiency, whereas Pacific-Asian countries are more sensitive to quality of infrastructure and have higher coefficients than African and American countries. This spatial heterogeneity is related to the specific factors that promote the development of their logistics performance.

Identifikasi Tingkat Pengangguran Terbuka Berdasarkan Kecamatan Di Kabupaten Pekalongan Menggunakan Spatial Model

Unemployment is an economic problem that the central and regional governments must resolve comprehensively and integrated. The open unemployment rate in Pekalongan Regency in the last three years has increased. It is influenced by several factors, one of which is the population growth rate. Likewise, the labor force participation rate has increased. It shows that the increasing availability of the workforce is not accompanied by additional employment and is not proportional to population growth. This study aims to determine the level of open unemployment and labor force participation in the sub-district, whether there is a relationship and mutual influence between sub-districts in Pekalongan Regency. The method used to see the correlation between sub-districts is the spatial analysis method. The method used to analyze the correlation between regions is the Moran's Index and the Moran Scatterplot, while to prove the dependence or spatial effect between regions partially used the LISA test. The test results show the Moran Index for the open unemployment rate of 0,915 and the labor force participation rate of 0,737 with a significance level of 5%. This measurement value shows that the Moran index value is close to 1, which means autocorrelation between neighboring sub-districts on the open unemployment rate and the labor force participation rate. While the LISA test results show that there are seven spatially related H-H districts, meaning that there is a strong correlation between the observed sub-districts and the surrounding districts. The strong correlation between observation sub-districts and their surroundings can be used as a reference for making policies in overcoming open unemployment between sub-districts. The policy for handling open unemployment has become more comprehensive and integrated among sub-districts by seeing that each sub-district has a spatial correlation and affects open unemployment in the district.

Applying the Super-EBM model and spatial Durbin model to examining total-factor ecological efficiency from a multi-dimensional perspective: evidence from China.

Ecological efficiency mainly emphasizes the importance of balancing the relationships between natural resources, energy, the ecological environment and economic growth, which has aroused widespread concern worldwide. China's rapid economic development has inevitably been accompanied by serious resource exhaustion, environmental pollution and ecological deterioration in the past several decades, which has brought huge challenges to China's sustainable development. Therefore, establishing the evaluation framework of total-factor ecological efficiency (TFEE) and identifying its driving force have a great significance for improving China's sustainable development capabilities. First, an ecological efficiency evaluation framework is established based on the theory of total-factor analysis. Second, the super efficient hybrid distance model considers undesirable output and measures TFEE nationwide in 30 provinces and four regions during the period 2003-2017. Finally, the spatial effect of TFEE and its influencing factors are examined by using a spatial Durbin model. The empirical results show that (1) nationwide and regional TFEEs have different degrees of decline during the study period. There were significant differences among the 30 provinces and four regions. Beijing, Tianjin and Shanghai are efficient, while the other provinces have not been as effective. The TFEEs of the four regions are not effective with an ordering of eastern > northeast > central > western. (2) Moran's I index shows that the TFEE nationwide has a positive spatial autocorrelation with strong spatial agglomeration. However, the spatial distribution pattern of TFEE in China was unstable and labile. The Moran scatter plot indicates that China's provincial TFEE has not only spatial dependence characteristics but also differences in spatial correlation. (3) Most factors are bound up with TFEE to various degrees: technological progress (TP), industrial agglomeration (IG) and human capital (HC) play a positive role, while industrial structure (IS), the level of urbanization (CITY) and energy intensity (EI) play a negative role. Additionally, environmental regulation (GZ) shows a U-type relationship with TFEE. The level of economic development (GDP) and foreign direct investment (FDI) cannot have a significant impact on TFEE at this stage. (4) The spatial Durbin model results show that TFEE has a significant spatial spillover effect, and the improvement of the TFEE of a province will increase the TFEE of neighbouring provinces. The confirmed spatial spillover effects of technological progress (TP), industrial structure (IS), the level of urbanization (CITY), industrial agglomeration (IG) and human capital (HC) can significantly impact the TFEE of neighbouring provinces. Among them, technological progress (TP), the level of urbanization (CITY) and human capital (HC) can significantly improve the TFEE of neighbouring provinces, and the level of economic development (GDP) and foreign direct investment (FDI) can significantly inhibit the improvement of TFEE in neighbouring provinces.

The impact of the spatial agglomeration of foreign direct investment on green total factor productivity of Chinese cities

Based on data of 285 Chinese cities from 2003 to 2017, this paper mainly studies the impact of the spatial agglomeration of Foreign direct investment (FDI) on the green total factor productivity(TFP) of Chinese cities by SDM model. It is measured that China's urban green TFP generally has been developing well from 2003 to 2017, and progress in green technology plays an important role in improving urban green TFP. Both global Moran index and local Moran scatter plots show that FDI and green TFP are characterized by strong spatial agglomeration. This suggests green TFP is closely related to the spatial agglomeration of FDI in a region. The paper finds that FDI plays a positive role in promoting green TFP in high-high and high-low cluster cities, and the technology spillover effect of highly agglomerated FDI is more significant than that of decentralized FDI, thus promoting the upgrading and agglomeration of green TFP in itself and surrounding cities. The positive benefits of low-high and low-low cluster cities are not significant. Therefore, it is necessary to go beyond its policy of administrative regions and give full play to radiation effect of High-high FDI agglomeration cities and promote the green TFP of their surrounding cities.

Long-Term Spatial Associations and Regional Convergence of Rural Literacy Rate in India

Although there are claims that inequality in India has stagnated or even increased in the post-liberalization period, careful analysis of data shows that these views are mostly unsubstantiate. The main aspects of social inequality can be structured, to a large extent, along urban-rural and gender lines. In this paper, we explore a nonparametric approach to highlight the form of the long-term spatial associations of the georeferenced units. Relying on the method of Markov chains, we develop a stationary form for the Moran scatter plot, one of the techniques of the exploratory spatial data analysis. As an illustration, we highlight the forms of spatial autocorrelation and heterogeneity of long-term literacy rates in rural districts in India observed by the two censuses of 1991 and 2001. We use the same data to estimate a spatial β-convergence model. It appears that the growth of literacy rate is higher in rural districts with the lowest literacy and proceeds by spatial diffusion. Beyond his correspondence with the β-convergence model, the proposed nonparametric approach has the advantage of identifying the location of spatial associations

Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR)

Measurement and Spatio-temporal evolution of China’s Regional Eco-efficiency

Based on panel data from 2008 to 2017, the Super-SBM model that considers undesired output has been to measure the eco-efficiency of 30 provinces in China. Meanwhile, the spatial autocorrelation analysis is incorporated into the spatiotemporal evolution law in order to calculate the global and local Moran’s I index, and combining the Moran scatter plot to explore the spatiotemporal evolution and agglomeration characteristics of regional eco-efficiency. The results show that the overall level of China’s eco-efficiency has improved, but the regional differences are obvious, shows a gradient descending pattern of “East, Northeast, Middle and West”. The regions with familiar eco-efficiency level showed the volatility clustering phenomenon, and that the regions and their neighbors have potentially similar trends. Eco-efficiency in China was improved followed by the improvement of local polarization situation and regional differences have narrowed.