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 Spatiotemporal Patterns of Land-Use Changes in Lithuania

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 619
Author(s):  
Daiva Juknelienė ◽  
Vaiva Kazanavičiūtė ◽  
Jolanta Valčiukienė ◽  
Virginija Atkocevičienė ◽  
Gintautas Mozgeris
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Management ◽  
Land Use Changes ◽  
Ordinary Least Squares ◽  
Primary Objective ◽  
Moran’S I ◽  
Land Uses ◽  
Moran's I ◽  
Management Policies

The spatially explicit assessment of land use and land-use change patterns can identify critical areas and provide insights to improve land management policies and associated decisions. This study mapped the land uses and land-use changes in Lithuanian municipalities since 1971. Additionally, an analysis was conducted of three shorter periods, corresponding to major national land-use policy epochs. Data on land uses, available from the Lithuanian National Forest Inventory (NFI) and collected on an annual basis with the primary objective of conducting greenhouse gas (GHG) accounting and reporting for the land use, land-use change, and forestry (LULUCF) sectors, were explored. The overall trend in Lithuania during the last five decades has been an increase in the area of forest and built-up land and decrease in the area of producing land, meadow/pasture, wetlands, and other land uses. Nevertheless, the development trends for the proportions of producing land and meadow/pasture changed trajectories several times, and the breakpoints were linked with important dates in Lithuanian history and associated with the reorganization of land management and land-use relations. Global Moran’s I statistic and Anselin Local Moran’s I were used to check for global and local patterns in the distribution of land use in Lithuanian municipalities. The proportions of producing land and pasture/meadow remained spatially autocorrelated during the whole period analysed. Local spatial clusters and outliers were identified for all land-use types used in GHG inventories in the LULUCF sector at all the time points analysed. Ordinary least squares (OLS) regression was used to explain the land-use change trends during several historical periods due to differing land management policies, utilizing data from freely available databases as the regressors. The percentage of variance explained by the models ranged from 37 to 65, depending on the land-use type and the period in question.

scholarly journals Applications of GIS and Very High-Resolution RS Data for Urban Land Use Change Studies in Mongolia

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
D. Amarsaikhan ◽  
V. Battsengel ◽  
E. Egshiglen ◽  
R. Gantuya ◽  
D. Enkhjargal
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Large Scale ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Capital City ◽  
Geographical Information ◽  
Topographic Map

The aim of this study is to analyze the urban land use changes occurred in the central part of Ulaanbaatar, the capital city of Mongolia, from 1930 to 2008 with a 10-year interval using geographical information system (GIS) and very high-resolution remote sensing (RS) data sets. As data sources, a large-scale topographic map, panchromatic and multispectral Quickbird images, and TerraSAR synthetic aperture radar (SAR) data are used. The primary urban land use database is developed using the topographic map of the study area and historical data about buildings. To extract updated land use information from the RS images, Quickbird and TerraSAR images are fused. For the fusion, ordinary and special image fusion techniques are used and the results are compared. For the final land use change analysis and RS image processing, ArcGIS and Erdas imagine systems installed in a PC environment are used. Overall, the study demonstrates that within the last few decades the central part of Ulaanbaatar city is urbanized very rapidly and became very dense.

scholarly journals THE EFFECT OF LAND USE CHANGE ON LAND SURFACE TEMPERATURE IN THE NETHERLANDS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 745-748 ◽  
Author(s):  
S. Youneszadeh ◽  
N. Amiri ◽  
P. Pilesjo
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
The Netherlands ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Land Use Changes ◽  
Geographical Information

The Netherlands is a small country with a relatively large population which experienced a rapid rate of land use changes from 2000 to 2008 years due to the industrialization and population increase. Land use change is especially related to the urban expansion and open agriculture reduction due to the enhanced economic growth. This research reports an investigation into the application of remote sensing and geographical information system (GIS) in combination with statistical methods to provide a quantitative information on the effect of land use change on the land surface temperature. In this study, remote sensing techniques were used to retrieve the land surface temperature (LST) by using the MODIS Terra (MOD11A2) Satellite imagery product. As land use change alters the thermal environment, the land surface temperature (LST) could be a proper change indicator to show the thermal changes in relation with land use changes. The Geographical information system was further applied to extract the mean yearly land surface temperature (LST) for each land use type and each province in the 2003, 2006 and 2008 years, by using the zonal statistic techniques. The results show that, the inland water and offshore area has the highest night land surface temperature (LST). Furthermore, the Zued (South)-Holland province has the highest night LST value in the 2003, 2006 and 2008 years. The result of this research will be helpful tool for urban planners and environmental scientists by providing the critical information about the land surface temperature.

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

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.

scholarly journals Space-time description of dengue outbreaks in Cruzeiro, São Paulo, in 2006 and 2011

2014 ◽  
Vol 60 (6) ◽  
pp. 565-570 ◽  
Author(s):  
Renata Marzzano de Carvalho ◽  
Luiz Fernando Costa Nascimento
Keyword(s):  
Spatial Autocorrelation ◽  
Dengue Fever ◽  
Temporal Distribution ◽  
Sao Paulo ◽  
Moran’S I ◽  
Spatial And Temporal Distribution ◽  
Moran's I ◽  
P Values ◽  
The City ◽  
Global Moran’S I

Objective: to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of São Paulo (SP). Methods: an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran’s I, month to month, and the Kernel estimator. Results: in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran’s I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran’s I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran’s I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation. Conclusion: we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.

Assessment of land-use change in coastal areas through geographical information systems

2020 ◽  
Vol 13 (10) ◽  
Author(s):  
Verónica Lango-Reynoso ◽  
Karla Teresa González-Figueroa ◽  
Fabiola Lango-Reynoso ◽  
María del Refugio Castañeda-Chávez ◽  
Jesús Montoya-Mendoza
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Geographical Information Systems ◽  
Design Methodology ◽  
Scientific Information ◽  
Coastal Ecosystems ◽  
Coastal Areas ◽  
Geographical Information ◽  
Coastal Zones ◽  
Computer Based

Objective: This article describes and analyzes the main concepts of coastal ecosystems, these as a result of research concerning land-use change assessments in coastal areas. Design/Methodology/Approach: Scientific articles were searched using keywords in English and Spanish. Articles regarding land-use change assessment in coastal areas were selected, discarding those that although being on coastal zones and geographic and soil identification did not use Geographic Information System (GIS). Results: A GIS is a computer-based tool for evaluating the land-use change in coastal areas by quantifying variations. It is analyzed through GIS and its contributions; highlighting its importance and constant monitoring. Limitations of the study/Implications: This research analyzes national and international scientific information, published from 2007 to 2019, regarding the land-use change in coastal areas quantified with the digital GIS tool. Findings/Conclusions: GIS are useful tools in the identification and quantitative evaluation of changes in land-use in coastal ecosystems; which require constant evaluation due to their high dynamism.

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. 

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 ◽  
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.

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 Land-Use Change Impacts from Sustainable Hydropower Production in EU28 Region: An Empirical Analysis

2021 ◽  
Vol 13 (9) ◽  
pp. 4599
Author(s):  
Mohd Alsaleh ◽  
Muhammad Mansur Abdulwakil ◽  
Abdul Samad Abdul-Rahim
Keyword(s):  
Carbon Dioxide ◽  
Land Use ◽  
European Union ◽  
Land Use Change ◽  
Carbon Dioxide Emissions ◽  
Energy Production ◽  
Land Use Changes ◽  
Ordinary Least Squares ◽  
The European Union ◽  
Hydropower Production

Under the current European Union (EU) constitution approved in May 2018, EU countries ought to guarantee that estimated greenhouse-gas releases from land use, land-use change, or forestry are entirely compensated by an equivalent accounted removal of carbon dioxide (CO2) from the air during the period between 2021 and 2030. This study investigates the effect of sustainable hydropower production on land-use change in the European Union (EU28) region countries during 1990–2018, using the fully modified ordinary least squares (FMOLS). The results revealed that land-use change incline with an increase in hydropower energy production. In addition, economic growth, carbon dioxide emissions, and population density are found to be increasing land-use changes, while institutional quality is found to be decreasing land-use change significantly. The finding implies that land-use change in EU28 region countries can be significantly increased by mounting the amount of hydropower energy production to achieve Energy Union aims by 2030. This will finally be spread to combat climate change and environmental pollution. The findings are considered robust as they were checked with DOLS and pooled OLS. The research suggests that the EU28 countries pay attention to the share of hydropower in their renewable energy combination to minimize carbon releases. Politicians and investors in the EU28 region ought to invest further in the efficiency and sustainability of hydropower generation to increase its production and accessibility without further degradation of forest and agricultural conditions. The authorities of the EU28 region should emphasize on efficiency and sustainability of hydropower energy with land-use management to achieve the international commitments for climate, biodiversity, and sustainable development, reduce dependence on fossil fuel, and energy insecurity.

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