scholarly journals Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

2022 ◽  
Vol 14 (2) ◽  
pp. 291
Author(s):  
Zhengyu Wang ◽  
Yaolin Liu ◽  
Yang Zhang ◽  
Yanfang Liu ◽  
Baoshun Wang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Land Subsidence ◽  
Urban Areas ◽  
Spatial Scales ◽  
Moran’S I ◽  
Anthropogenic Factors ◽  
Spatial Correlations ◽  
Moran's I ◽  
Average Value ◽  
Gwr Model

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

scholarly journals Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaoxiao Liu ◽  
Rizwan Shahid ◽  
Alka B. Patel ◽  
Terrence McDonald ◽  
Stefania Bertazzon ◽  
...  
Keyword(s):  
First Nations ◽  
Rural Areas ◽  
Urban Areas ◽  
Hot Spot ◽  
Local Level ◽  
Moran’S I ◽  
Local Health ◽  
Case Definitions ◽  
Moran's I ◽  
Remote Areas

Abstract Background Knowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada. Methods Five administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran’s I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots). Results Among 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran’s I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192–195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168–171) compared to Rural areas (Fox Creek: 65, 95%CI 63–68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots. Conclusions The findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.

scholarly journals Kemiskinan dan Ketimpangan Pembangunan kabupaten/Kota di Provinsi Lampung

2021 ◽  
Vol 10 (1) ◽  
pp. 31-45
Author(s):  
Resha Moniyana ◽  
Ahmad Dhea Pratama
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Research Area ◽  
Moran’S I ◽  
Poor People ◽  
High Poverty ◽  
Spatial And Temporal Patterns ◽  
Moran's I ◽  
Different Characteristics ◽  
Poverty Levels

The analysis results used in the problem of poverty are increasingly developing as the understanding of the problem of poverty becomes more complex in the spatial and temporal patterns, seeing the patterns and characteristics of a phenomenon with spatial imaging and study of patterns is the main objective of this study by looking at the pattern of the percentage of poor people and the level of inequality. The method used is processing Moran's I spatial data, Moranscatterplot and LISA, testing development inequality with the Williamson Index, The research area covers 15 districts/cities in 2015-2019. Spatial linkages The percentage of poor people between districts/cities in Lampung Province has a positive Moran's I value, has a spatial pattern with the same characteristics and is clustered. Development inequality is negative Moran's I, Development inequality has a spatial pattern with different characteristics in 2015 -2019. Poverty analysis indicates that during the 5-year study period, 5 districts in Lampung Province were still trapped in high poverty levels, The results of regional development inequality with the Williamson index indicate 3 regions with high levels of inequality, 4 areas of moderate inequality and 8 regions with low levels of inequality.

scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

scholarly journals ANALISIS POLA SPASIAL KEBAKARAN LAHAN DI KABUPATEN PULANG PISAU TAHUN 2001-2019

2020 ◽  
Vol 9 (1) ◽  
pp. 23
Author(s):  
Frederik Samuel Papilaya
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Spatial Correlations ◽  
Hotspot Analysis ◽  
Fire Event ◽  
Moran's I ◽  
Central Kalimantan ◽  
The Republic ◽  
The Relationship

Forest and land fires have become a problem for the Republic of Indonesia in the last few decades. Statistically based on MODIS data for 19 years from 2001 to 2019, Central Kalimantan is the province with the most number of fires totaling 255,334 fire spot or 18% of fire occurance from 34 provinces in Indonesia. This study will look at spatial patterns and spatial correlations of forest and land fires in Pulang Pisau District from 2001 to 2019. Spatial patterns of hotspots were analyzed using a statistical Getis-Ord (Gi *) analysis, the relationship between hotspots was analyzed using Spatial Autocorrelation Moran's I (Index). The results of the hotspot analysis show the high significance of the hotspots that occurred in the subdistricts of Sebangau Kuala, Kahayan Kuala, Kahayan Hilir and Jabiren. The result of spatial autocorrelation of hotspots from 2001-2019 (except in 2010) is that hotspots pattern are clustered. Based on the findings it can be concluded statistically that 99% of the likelihood of a group fire event occurred intentionally. This hotspot incident that arose intentionally can be a clue for the Local Government to be able to better engage the community to prevent and overcome the hotspot by providing coaching and trainingKeywords: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau Kebakaran hutan dan lahan telah menjadi masalah bagi Republik Indonesia dalam beberapa dekade terakhir. Secara statistik berdasarkan data MODIS selama 19 tahun mulai dari tahun 2001 sampai 2019, Kalimantan Tengah merupakan provinsi yang paling banyak memiliki titik api sejumlah 255.334 titik atau sebesar 18% kejadian kebakaran hutan dan lahan dari 34 provinsi di Indonesia. Penelitian ini akan melihat pola spasial dan korelasi spasial kebakaran hutan dan lahan di Kabupaten Pulang Pisau pada tahun 2001 sampai 2019. Pola spasial hotspot dianalisa dengan menggunakan analisis Getis-Ord (Gi*) statistic, hubungan antar titik api dianalisa dengan menggunakan Spatial Autocorrelation Moran’s I (Index). Hasil analisis hotspot menunjukan signifikasi tinggi hotspot yang terjadi pada kecamatan Sebangau Kuala, Kahayan Kuala, Kahayan Hilir dan Jabiren. Hasil spatial autocorrelation kejadian titik api dari tahun 2001-2019 (kecuali tahun 2010) adalah titik api memiliki pola yang merupakan berkelompok (clustered). Berdasarkan temuan bisa disimpulkan secara statistik bahwa 99% kemungkinan kejadian titik api yang berkelompok tersebut terjadi secara disengaja. Kejadian titik api yang muncul secara disengaja ini dapat menjadi petunjuk bagi Pemerintah Daerah untuk dapat lebih mengajak masyarakat dalam mencegah dan menanggulangi titik api dengan memberikan pembinaan dan pelatihan. Kata Kunci: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau

scholarly journals Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

2014 ◽  
Vol 11 (8) ◽  
pp. 2401-2409 ◽  
Author(s):  
W. J. Fu ◽  
P. K. Jiang ◽  
G. M. Zhou ◽  
K. L. Zhao
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Forest Ecosystem ◽  
Forest Litter ◽  
Zhejiang Province ◽  
Moran’S I ◽  
Carbon Density ◽  
Southeastern China ◽  
Moran's I ◽  
Density Values

Abstract. Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south–north) × 6 km (east–west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha−1 to 8841.3 kg ha−1, with an average of 1786.7 kg ha−1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

scholarly journals Study of the Impact of the Growth of Settlement Areas Against Decreased Capacity of Shallow Aquifer and Groundwater in Semarang City (2014-2017)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yudo Prasetyo
Keyword(s):  
Land Cover ◽  
Land Subsidence ◽  
Urban Areas ◽  
Environmental Changes ◽  
Negative Impact ◽  
Shallow Aquifer ◽  
Land Use Classification ◽  
Residential Areas ◽  
Average Value ◽  
The Impact

The growth of urban areas dominated by residential and industrial land cover will encourage the high use of clean water and land loading (compaction due to building loads). The use of water in people's daily lives and industrial activities still relies on nature, namely in the form of groundwater or aquifers. Continuous water collection, especially in big cities in Indonesia, will have a negative impact on the environment which results in changes in the environment itself. Environmental changes due to the impact of taking water that might occur are land subsidence (LS). For this reason, this study will examine the relationship of the impact of the development of residential areas in the city of Semarang on the decline of shallow aquifer capacity (SAC) and LS.Observation of changes in KAD in this study was observed in the type of shallow aquifer using shallow wells (MAT) data. Whereas for observing land subsidence using the PS InSAR method. For the growth of the residential area of Semarang, it will be focused on the land cover of residential areas in the 2014-2017 period. The overlapping method is used to correlate the effect of changes in KAD and PMT in Semarang City.PS InSAR processing results obtained an average value of average land subsidence per year with a range of 0 ± 3.4 cm to 4.5 ± 3.4 cm and the results of processing obtained the largest land subsidence information found in the District of North Semarang, East Semarang, West Semarang, Pedurungan and Genuk. The change in KAD in the amount of 60% to> a decrease of> 80% occurred in Genuk Sub-District, North Semarang, West Semarang, Pedurungan, Gayamsari. Whereas settlements with population levels based on land use classification maps for settlements are in Genuk, West Semarang, Gayamsari, Pedurungan, Tembalang, and Banyumanik Districts

scholarly journals Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Amanda Gabriela De Carvalho ◽  
João Gabriel Guimarães Luz ◽  
João Victor Leite Dias ◽  
Anuj Tiwari ◽  
Peter Steinmann ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Cutaneous Leishmaniasis ◽  
Urban Areas ◽  
Neglected Tropical Diseases ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Mato Grosso ◽  
Global And Local

Neglected tropical diseases characterized by skin lesions are highly endemic in the state of Mato Grosso, Brazil. We analyzed the spatial distribution of leprosy and Cutaneous Leishmaniasis (CL) and identified the degree of overlap in their distribution. All new cases of leprosy and CL reported between 2008 and 2017 through the national reporting system were included in the study. Scan statistics together with univariate Global and Local Moran’s I were employed to identify clusters and spatial autocorrelation for each disease, with the spatial correlation between leprosy and CL measured by bivariate Global and Local Moran’s I. Finally, we evaluated the demographic characteristics of the patients. The number of leprosy (N = 28,204) and CL (N = 24,771) cases in Mato Grosso and the highly smoothed detection coefficients indicated hyperendemicity and spatial distribution heterogeneity. Scan statistics demonstrated overlap of high-risk clusters for leprosy (RR = 2.0; p <0.001) and CL (RR = 4.0; p <0.001) in the North and Northeast mesoregions. Global Moran’s I revealed a spatial autocorrelation for leprosy (0.228; p = 0.001) and CL (0.311; p = 0.001) and a correlation between them (0.164; p = 0.001). Both diseases were found to be concentrated in urban areas among men aged 31-60 years, of brown-skinned ethnicity and with a low educational level. Our findings indicate a need for developing integrated and spatially as well as socio-demographically targeted public health policies.

scholarly journals Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain)

2015 ◽  
Vol 24 (2) ◽  
pp. e022 ◽  
Author(s):  
Maria L. Loureiro ◽  
Jesús Barreal
Keyword(s):  
Rural Areas ◽  
Spatial Dependence ◽  
Urban Areas ◽  
Fixed Effects ◽  
Random Effect ◽  
Temporal Trends ◽  
Relative Size ◽  
Moran’S I ◽  
Contributing Factors ◽  
Moran's I

<p><em>Aim of study:</em> The goal of this paper is to analyse the importance of the main contributing factors to the occurrence of wildfires. <strong></strong></p><p><em>Area of study:</em> We employ data from the region of Galicia during 2001-2010; although the similarities shared between this area and other rural areas may allow extrapolation of the present results.</p><p><em>Material and Methods:</em> The spatial dependence is analysed by using the Moran’s I and LISA statistics. We also conduct an econometric analysis modelling both, the number of fires and the relative size of afflicted woodland area as dependent variables, which depend on the climatic, land cover variables, and socio-economic characteristics of the affected areas. Fixed effects and random effect models are estimated in order to control for the heterogeneity between the Forest Districts in Galicia.</p><p><em>Main results</em>: Moran’s I and LISA statistics show that there is spatial dependence in the occurrence of Galician wildfires. Econometrics models show that climatology, socioeconomic variables, and temporal trends are also important to study both, the number of wildfires and the burned-forest ratio.</p><p><em>Research highlights:</em> We conclude that in addition to direct forest actions, other agricultural or social public plans, can help to reduce wildfires in rural areas or wildland-urban areas. Based on these conclusions, a number of guidelines are provided that may foster the development of better forest management policies in order to reduce the occurrence of wildfires.</p><p><strong>Keywords:</strong> Cause-effect relationship; climatology; spatial and temporal indicators; fixed effects; random effects; socio-economic factors.</p>

scholarly journals Vulnerability to the transmission of human visceral leishmaniasis in a Brazilian urban area

2017 ◽  
Vol 51 (0) ◽  
Author(s):  
Celina Roma Sánchez de Toledo ◽  
Andréa Sobral de Almeida ◽  
Sergio Augusto de Miranda Chaves ◽  
Paulo Chagastelles Sabroza ◽  
Luciano Medeiros Toledo ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Urban Areas ◽  
Urban Infrastructure ◽  
Vulnerability Index ◽  
Moran’S I ◽  
Protection Factor ◽  
Moran's I ◽  
Human Visceral Leishmaniasis ◽  
The Social ◽  
Global Moran’S I

ABSTRACT OBJECTIVE To analyze the determinants for the occurrence of human visceral leishmaniasis linked to the conditions of vulnerability. METHODS This is an ecological study, whose spatial analysis unit was the Territorial Analysis Unit in Araguaína, State of Tocantins, Brazil, from 2007 to 2012. We have carried out an analysis of the sociodemographic and urban infrastructure situation of the municipality. Normalized primary indicators were calculated and used to construct the indicators of vulnerability of the social structure, household structure, and urban infrastructure. From them, we have composed a vulnerability index. Kernel density estimation was used to evaluate the density of cases of human visceral leishmaniasis, based on the coordinates of the cases. Bivariate global Moran’s I was used to verify the existence of spatial autocorrelation between the incidence of human visceral leishmaniasis and the indicators and index of vulnerability. Bivariate local Moran’s I was used to identify spatial clusters. RESULTS We have observed a pattern of centrifugal spread of human visceral leishmaniasis in the municipality, where outbreaks of the disease have progressively reached central and peri-urban areas. There has been no correlation between higher incidences of human visceral leishmaniasis and worse living conditions. Statistically significant clusters have been observed between the incidences of human visceral leishmaniasis in both periods analyzed (2007 to 2009 and 2010 to 2012) and the indicators and index of vulnerability. CONCLUSIONS The environment in circumscribed areas helps as protection factor or increases the local vulnerability to the occurrence of human visceral leishmaniasis. The use of methodology that analyzes the conditions of life of the population and the spatial distribution of human visceral leishmaniasis is essential to identify the most vulnerable areas to the spread/maintenance of the disease.

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