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

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

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Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v3i1.2404 ◽

2012 ◽

Vol 3 (1) ◽

pp. 217

Author(s):

Rokhana Dwi Bekti

Keyword(s):

Spatial Analysis ◽

Spatial Autocorrelation ◽

Moran’S I ◽

Poor People ◽

Spatial Autocorrelation Analysis ◽

Autocorrelation Analysis ◽

Moran's I ◽

Relationship Of ◽

The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

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Spatial Analysis of COVID-19 and Exploration of Its Environmental and Socio-Demographic Risk Factors Using Spatial Statistical Methods: A Case Study of Iran

Health in Emergencies & Disasters Quarterly ◽

10.32598/hdq.5.3.358.1 ◽

2020 ◽

Vol 5 (3) ◽

pp. 145-154

Author(s):

Mohsen Shariati ◽

◽

Mahsa Jahangiri-rad ◽

Fatima Mahmud Muhammad ◽

Jafar Shariati ◽

...

Keyword(s):

Risk Factors ◽

Spatial Autocorrelation ◽

Confidence Level ◽

Hot Spot ◽

Moran’S I ◽

Moran's I ◽

Hot Spot Analysis ◽

The Relationship ◽

Global Moran’S I ◽

Spot Analysis

Background: Iran detected its first COVID-19 case in February 2020 in Qom province, which rapidly spread to other cities in the country. Iran, as one of those countries with the highest number of infected people, has officially reported 1812 deaths from a total number of 23049 confirmed infected cases that we used in the analysis. Materials and Methods: Geographic distribution by the map of calculated incidence rates for COVID -19 in Iran within the period was prepared by GIS 10.6 Spatial autocorrelation (Global Moran’s I) and hot spot analysis were used to assess COVID -19 spatial patterns. The ordinary least square method was used to estimate the relationship between COVID -19 and the risk factors. The next step was to explore Geographically Weighted Regression (GWR) models that might better explain the variation in COVID -19 cases based on the environmental and socio-demographic factors. Results: The spatial autocorrelation (Global Moran’s I) result showed that COVID-19 cases in the studied area were in clustered patterns. For statistically significant positive z-scores, the larger the z-score is, the more intense the clustering of high values (hot spot), such as Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran. Hot spot analysis detected clustering of a hot spot with confidence level 99% for Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran, as well. The risk factors were removed from the model step by step. Finally, just the distance from the epicenter was adopted in the model. GWR efforts increased the explanatory value of risk factor with better special precision (adjusted R-squared=0.44) Conclusion: The highest CIR was concentrated around Qom. Also, the greater the distance from the center of prevalence (Qom), the fewer the patients. Hot spot analysis also implies that the neighboring provinces of prevalence centers exhibited hot spots with a 99% confidence level. Furthermore, the results of OLS analysis showed the significant correlation of CIR is with the distance from epicenter (Qom). The GWR can result in the spatial granularity providing an opportunity to well understand the relationship between environmental spatial heterogeneity and COVID-19 risk as entailed by the infection of CIR with COVID-19, which would make it possible to better plan managerial policies for public health.

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Analisis Moran’s I, Geary’s C, dan Getis-Ord G pada Penerapan Jumlah Penderita Kusta di Kabupaten Gowa

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v2i2.12577 ◽

2020 ◽

Vol 2 (2) ◽

pp. 151

Author(s):

S. Sukarna ◽

Wahidah Sanusi ◽

Hafilah Hardiono

Keyword(s):

Infectious Disease ◽

Spatial Analysis ◽

Spatial Autocorrelation ◽

Spatial Patterns ◽

Moran’S I ◽

Analysis Method ◽

Moran's I ◽

Surrounding Areas ◽

Geary’S C ◽

Quadrat Analysis

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy

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Geospatial Analyses of Pain Intensity and Opioid Unit Doses Prescribed on the Day of Discharge Following Orthopedic Surgery

Pain Medicine ◽

10.1093/pm/pnz311 ◽

2019 ◽

Vol 21 (8) ◽

pp. 1644-1662

Author(s):

Patrick Tighe ◽

François Modave ◽

MaryBeth Horodyski ◽

Matthew Marsik ◽

G Lipori ◽

...

Keyword(s):

Spatial Autocorrelation ◽

Pain Intensity ◽

Spatial Patterns ◽

Hot Spots ◽

Social Deprivation ◽

Moran’S I ◽

Area Deprivation ◽

Opioid Overdose ◽

Moran's I ◽

Geospatial Analyses

Abstract Objective Inappropriate opioid prescribing after surgery contributes to opioid use disorder and risk of opioid overdose. In this cross-sectional analysis of orthopedic surgical patients, we examined the role of patient location on postoperative pain intensity and opioids prescribed on hospital discharge. Methods We used geospatial analyses to characterize spatial patterns of mean pain intensity on the day of discharge (PiDoD) and opioid units prescribed on the day of discharge (OuPoD), as well as the effect of regional social deprivation on these outcomes. Results At a 500-km radius from the surgery site, the Global Moran’s I for PiDoD (2.71 × 10−3, variance = 1.67 × 10−6, P = 0.012) and OuPoD (2.19 × 10−3, SD = 1.87, variance = 1.66 × 10−6, P = 0.03) suggested significant spatial autocorrelation within each outcome. Local indicators of spatial autocorrelation, including local Moran’s I, Local Indicator of Spatial Autocorrelation cluster maps, and Getis-Ord Gi* statistics, further demonstrated significant, specific regions of clustering both OuPoD and PiDoD. These spatial patterns were associated with spatial regions of area deprivation. Conclusions Our results suggest that the outcomes of pain intensity and opioid doses prescribed exhibit varying degrees of clustering of patient locations of residence, at both global and local levels. This indicates that a given patient’s pain intensity on discharge is related to the pain intensity of nearby individuals. Similar interpretations exist for OuPoD, although the relative locations of hot spots of opioids dispensed in a geographic area appear to differ from those of hot spots of pain intensity on discharge.

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Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

Jurnal Buana Informatika ◽

10.24002/jbi.v8i4.1446 ◽

2017 ◽

Vol 8 (4) ◽

Cited By ~ 1

Author(s):

Matheus Supriyanto Rumetna ◽

Eko Sediyono ◽

Kristoko Dwi Hartomo

Keyword(s):

Land Use ◽

Land Use Change ◽

Spatial Autocorrelation ◽

Land Use Changes ◽

Geographical Information ◽

Moran’S I ◽

Complete Spatial Randomness ◽

Moran's I ◽

Global Moran’S I ◽

Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

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

Scientific Research Journal ◽

10.24191/srj.v9i2.9405 ◽

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.

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

Scientific Research Journal ◽

10.24191/srj.v9i2.5389 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Asra Hosseini

Keyword(s):

Spatial Autocorrelation ◽

Smart Cities ◽

Moran’S I ◽

Moran's I ◽

Urban Planner ◽

Universal Feature ◽

Autocorrelation Method ◽

Selection Of ◽

Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring 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% ofneighbourhoods, 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.

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Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽

10.1186/s40795-021-00424-4 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Biruk Shalmeno Tusa ◽

Sewnet Adem Kebede ◽

Adisu Birhanu Weldesenbet

Keyword(s):

Public Health ◽

Spatial Distribution ◽

Scan Statistics ◽

Moran’S I ◽

Demographic And Health Surveys ◽

Targeted Prevention ◽

Hotspot Analysis ◽

Mixed Effect ◽

Moran's I ◽

Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

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Approaches to measuring the relevance of geographical dimension of societa inequalities

Geografie ◽

10.37040/geografie2009114010052 ◽

2009 ◽

Vol 114 (1) ◽

pp. 52-65 ◽

Cited By ~ 6

Author(s):

Pavlína Netrdová ◽

Vojtěch Nosek

Keyword(s):

Spatial Autocorrelation ◽

Demographic Data ◽

Moran’S I ◽

Theil Index ◽

Moran's I ◽

Similar Information ◽

Index Decomposition ◽

Geographical Dimension

The article focuses on geographical dimension of societal inequalities, especially on approaches to its analysing. Two distinct methods of analysing the relative geographical inequality are utilized: Theil index decomposition and spatial autocorrelation measured by Moran’s I coefficient. Both employed methods should bring, in theory, very similar information. This fact is explored empirically by comparing both methods and by their application on detailed economic, social and demographic data on municipalities in Czechia. Conclusions, predominantly of epistemological nature, are intended to assess advantages and limitations of individual methods and their possible application in practice.

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Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

Sustainability ◽

10.3390/su132112277 ◽

2021 ◽

Vol 13 (21) ◽

pp. 12277

Author(s):

Xinba Li ◽

Chuanrong Zhang

Keyword(s):

Real Estate ◽

Spatial Patterns ◽

Price Change ◽

Housing Prices ◽

Housing Price ◽

Real Estate Market ◽

Moran’S I ◽

Moran's I ◽

Price Changes ◽

The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

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