scholarly journals Autokorelasi Spasial Untuk Analisis Pola Pengawasan Kawasan Lindung Di Kota Ambon Maluku

Teknika ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 36-43
Author(s):  
Anna Simatauw ◽  
Eko Sediyono ◽  
Sri Yulianto Joko Prasetyo
Keyword(s):  
Spatial Association ◽  
Moran’S I ◽  
Moran's I ◽  
Global Moran’S I

Kota Ambon memiliki kawasan lindung yang ditetapkan untuk melindungi kelestarian lingkungan dan sangat sensitif terhadap dampak negatif untuk memenuhi kebutuhan manusia dalam penggunaan lahan. Penelitian ini bertujuan untuk menganalisis pola pengawasan kawasan lindung dalam bentuk data perubahan penutupan lahan pada 50 wilayah di Kota Ambon dengan menggunakan uji autokorelasi indeks Global Moran’s I dan Local Indicator of Spatial Association (LISA), selanjutnya melakukan peramalan pola spasial pada data perubahan penutupan lahan di tahun 2020. Penelitian ini menunjukkan terdapat autokorelasi spasial dari perubahan penutupan lahan untuk kawasan lindung tetapi korelasi melemah dengan nilai indeks Moran’s I berubah dari 0,283362665 menjadi -0,042523054. Peramalan tahun 2020 menunjukkan terdapat autokorelasi spasial pada tingkat perubahan penutupan lahan yang berkorelasi negatif dengan nilai indeks Moran’s I -0,011095491.

Analysis on Spatial Difference of the Rural Resident’s per Capita Net Income in Qinhuangdao City Based on ESDA

2014 ◽  
Vol 955-959 ◽  
pp. 3893-3898
Author(s):  
Yu Hong Wu
Keyword(s):  
Spatial Data ◽  
Rural Economy ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Net Income ◽  
Gis Technology ◽  
Moran's I ◽  
Per Capita ◽  
Global Moran’S I

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

scholarly journals Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Tawanda Manyangadze ◽  
Moses J Chimbari ◽  
Emmanuel Mavhura
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Association ◽  
District Level ◽  
Moran’S I ◽  
Household Food Security ◽  
Economic Factors ◽  
Hiv Incidence ◽  
Moran's I ◽  
Global Moran’S I ◽  
Socio Economic Factors

This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males, unemployed females, percentage of poor households i.e., poverty prevalence, night lights index, literacy rate,household food security, and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios (LLR), global Moran’s I, local indicator of spatial association - LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression (GWPR) and semi-parametric GWPR (s-GWPR) were used to determine the spatial association between HIV incidence and socio-economic factors. HIV incidence (number of cases per 1000) ranged from 0.6 (Buhera district) to 13.30 (Mangwe district). Spatial clustering of HIV incidence was observed (Global Moran’s I = - 0.150; Z score 3.038; p-value 0.002). Significant clusters of HIV were observed at district level. HIV incidence and its association with socioeconomic factors varied across the districts except percentage of females unemployed. Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.

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

2017 ◽  
Vol 8 (4) ◽  
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.

scholarly journals Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Mukemil Awol ◽  
Zewdie Aderaw Alemu ◽  
Nurilign Abebe Moges ◽  
Kemal Jemal
Keyword(s):  
Multilevel Analysis ◽  
Health Survey ◽  
Associated Factors ◽  
Demographic And Health Survey ◽  
Community Level ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Depth Analysis ◽  
Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

scholarly journals ANALISIS PEMANFAATAN KAWASAN WILAYAH PESISIR MENGGUNAKAN LOCAL INDICATORS OF SPATIAL ASSOCIATION (LISA) (STUDI KASUS : KABUPATEN KULON PROGO)

2017 ◽  
Vol 8 (2) ◽  
pp. 781
Author(s):  
Tirsa Ninia Lina ◽  
Eko Sediyono ◽  
Sri Yulianto Joko Prasetyo
Keyword(s):  
Spatial Association ◽  
Moran’S I ◽  
Moran's I

Kawasan pesisir Kabupaten Kulon Progo terdiri dari empat kecamatan, yaitu kecamatan Galur, Panjatan, Wates, dan Temon. Kawasan pesisir ini rentan terhadap dampak negatif aktifitas manusia seperti penggunaan tanah atau pemanfaatannya yang sering tumpang tindih. Tujuan penelitian ini untuk menganalisis autokorelasi spasial terhadap pemanfaatan kawasan wilayah pesisir di Kabupaten Kulon Progo. Penelitian ini menggunakan salah satu pengujian autokorelasi spasial yaitu Local Indicators of Spatial Association (LISA) dengan indikator Local Moran's I, yang menghasilkan signifikansi secara statistik tinggi (hotspots), signifikansi secara statistik rendah (coldspots), dan pencilan (outlier). Hasil dari penelitian ini menunjukkan bahwa kecamatan yang termasuk kategori hotspots (H-H) diantaranya Temon dengan lima hotspots pada kawasan permukiman perdesaan, pertanian lahan kering, industri, sempadan pantai, dan suaka alam, Panjatan dengan tiga hotspots pada kawasan permukiman perkotaan, perdagangan, dan sempadan sungai, Galur dengan dua hotspots pada kawasan pertanian lahan basah dan perdagangan, dan Wates dengan satu hotspots pada kawasan industri.Kata kunci: kawasan pesisir, Kabupaten Kulon Progo, Local Indicators of Spatial Association, LISA, Local Moran's I.

scholarly journals SPATIAL ASSOCIATION PATTERNS OF SMOKING, TOBACCO OUTLET DENSITY, AND SECONDHAND SMOKE IN THAILAND

2021 ◽  
Vol 56 (5) ◽  
pp. 351-361
Author(s):  
Pittaya Thammawongsa ◽  
Wongsa Laohasiriwong ◽  
Nakarin Prasit ◽  
Surachai Phimha
Keyword(s):  
Spatial Autocorrelation ◽  
Secondhand Smoke ◽  
Spatial Regression ◽  
Spatial Association ◽  
Moran’S I ◽  
Data Set ◽  
Moran's I ◽  
High Prevalence ◽  
Smoking Behaviors ◽  
Outlet Density

Thailand has a higher prevalence of smoking behaviors which puts people at risk of morbidity and mortality. This study aimed to determine the spatial association of smoking behaviors and their associated factors among the population of Thailand. This study was conducted using a data set from the National Statistical Office of Thailand, 2017. A Moran’s I, local indicators of spatial association (LISA), and spatial regression were used to identify the spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors among Thai people. According to the results, among 88,689 participants, the prevalence of smoking behaviors was 18.00 per 1,000 population. There was global spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors with the Moran’s I values of 0.120 and 0.375, respectively. The LISA analysis identified significant positive spatial local autocorrelation of smoking behaviors in the form of nine high-high clusters of tobacco outlets density and ten high-high prevalence clusters of secondhand smoke. The prevalence of secondhand smoke predicted smoking behaviors by 62.8 percent. There were spatial associations between tobacco outlet density and secondhand smoke problems that led the youngsters to start smoking. It is a general recommendation to strictly enforce policies and laws to control smoking, and cover all regions in Thailand.

scholarly journals Spatial-temporal Patterns and Risk Factors for Human Leptospirosis in Thailand, 2012-2018

2021 ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Karine Chalvet-Monfray ◽  
Suchada Geawduanglek ◽  
Phrutsamon Wongnak ◽  
Julien Cappelle
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Significant Risk ◽  
Temporal Correlation ◽  
Moran’S I ◽  
Moran's I ◽  
Temperature Range ◽  
Flood Exposure ◽  
Flooded Area ◽  
Global Moran’S I

Abstract Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.

scholarly journals Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Huling Li ◽  
Hui Li ◽  
Zhongxing Ding ◽  
Zhibin Hu ◽  
Feng Chen ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Cumulative Number ◽  
Moran's I ◽  
Spatial Statistical Analysis ◽  
Global Concern ◽  
And Cluster Analysis ◽  
Global Moran’S I

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

scholarly journals What do we know about the behavior of animal rabies in Chile through the last years?

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Raul Alegria-Moran ◽  
Daniela Miranda ◽  
Alonso Parra ◽  
Lisette Lapierre
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Winter Season ◽  
Central Zone ◽  
Domestic Dog ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Epidemiological Characterization

ObjectiveThis study aims to analyze the evolution of the epidemiologicalbehavior of rabies in Chile during the period 2003 to 2013, throughthe epidemiological characterization of a number of variables anddescription of spatial and temporal patterns of animal cases.IntroductionRabies is a zoonotic disease caused by an RNA virus from thefamily Rhabdoviridae, genus Lyssavirus. Worldwide distributed,control of rabies has been considered to be particularly amenable toa “One Health” strategy (1). In Chile, rabies was considered endemicin domestic dog population until the late 1960s, when a surveillanceprogram was established, decreasing the number of human casesrelated to canine variants until the year 1972 (2). Rabies is recognizedas a endemic infection in chiropterans of Chile and prompted thesurveillance of the agent in this and other species (3).MethodsAn epidemiological characterization of the registered cases fromthe National Program for Prevention and Control of Rabies wascarried. During the period 2003-2013, 927 cases were reported.Descriptive statistics and descriptive mapping, recording origin of thesample, number of cases per region, animal reservoir implicated andviral variant were performed. A spatial autocorrelation analysis wascarried using Moran’s I indicator for the detection of spatial clusters(4), using the Local Indicators of Spatial Association (LISA) statistics(5), at national and regional level of aggrupation (north, central andsouth zone). Temporal descriptive analysis was carried.Results927 positive cases were recorded. 920 (99.2%) cases came frompassive surveillance, while 7 (0.8%) cases by active surveillance, totalpositivity was 77.02% and 1.37% respectively. Positivity was reportedmainly in the central zone (88.1%), mainly in Valparaiso (19.1%),Metropolitana (40.6%) (Figure 1), Maule (11.8%) regions concentratedin urban centers. Main positive reservoirs were bats (99.8%),specificallyTadarida brasiliensisand viral variant 4 was the mostcommonly diagnosed. LISA test gives a Moran’s I indicator of 0.1537(p-value = 0.02) for the central zone (Table 1). Rabies tend to decreasein fall and winter season (2.9 cases vs 13 cases during summer).ConclusionsWildlife rabies in bats remains endemic in Chile, concentrated inurban areas. The main reservoirs are insectivorous bats. There is asignificant spatial autocorrelation of animal rabies cases in the centralzone of Chile. Results are relevant to the design of preventive andcontrol measures.

scholarly journals Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

2021 ◽  
Vol 14 (4) ◽  
pp. 155-167 ◽  
Author(s):  
Parichat Wetchayont ◽  
Katawut Waiyasusri
Keyword(s):  
Population Density ◽  
Early Stage ◽  
Adult Population ◽  
Spatial Association ◽  
Incidence Rates ◽  
Moran’S I ◽  
Moran's I ◽  
Spatial Spread ◽  
Spatio Temporal ◽  
The Difference

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

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