scholarly journals Predicting the magnitude of residual spatial autocorrelation in geographical ecology

Ecography ◽  
2021 ◽  
Author(s):  
Daehyun Kim
Keyword(s):  
Spatial Autocorrelation ◽  
Geographical Ecology

scholarly journals Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology

Ecography ◽  
2007 ◽  
Vol 30 (3) ◽  
pp. 375-384 ◽  
Author(s):  
Bradford A. Hawkins ◽  
José Alexandre F. Diniz-Filho ◽  
Luis Mauricio Bini ◽  
Paulo De Marco ◽  
Tim M. Blackburn
Keyword(s):  
Parameter Estimation ◽  
Spatial Autocorrelation ◽  
Geographical Ecology

scholarly journals Spatial autocorrelation and red herrings in geographical ecology

2003 ◽  
Vol 12 (1) ◽  
pp. 53-64 ◽  
Author(s):  
José Alexandre Felizola Diniz-Filho ◽  
Luis Mauricio Bini ◽  
Bradford A. Hawkins
Keyword(s):  
Spatial Autocorrelation ◽  
Geographical Ecology

Analisis Pola Kebakaran Lahan di Kalimantan Timur dengan MODIS dan VIIRS

2020 ◽  
Vol 21 (1) ◽  
pp. 84
Author(s):  
Aditya Pranata Wibowo ◽  
Frederik Samuel Papilaya
Keyword(s):  
Spatial Autocorrelation ◽  
Nearest Neighbor ◽  
Directional Distribution

Kebakaran adalah suatu peristiwa timbulnya reaksi oksidasi saat adanya bahan bakar, oksigen, dan energi panas yang menyebabkan pembakaran yg tidak terkendali dan menyebabkan kerugian. Pemantauan secara spasial terhadap kebakaran menjadi suatu hal yang sangat penting untuk dikaji. Penelitian ini bertujuan untuk mengetahui daerah manakah yang paling banyak mengalami kebakaran dan juga pola dari kebakaran tersebut. Sumber data didapatkan dari website FIRMS yang merupakan data yang bersifat kuantitatif dan data primer. Metode analisis yang digunakan adalah metode analisis Spatial Autocorrelation, Average Nearest Neighbor, Optimized Hotspot, Directional Distribution (Standard Devitional Ellipse), dan Temporal Collect Event. Hasil yang telah didapatkan dari analisis bermetode Directional Distribution, Optimized Hotspot, dan Temporal Collect Event adalah terdapat banyak titik api yang berada di Kecamatan Kutai Kartanegara, Kutai Barat, Kutai Timur, Berau, dan Paser. Dalam analisis Spatial Autocorrelation ditemukan bahwa terdapat korelasi antara lokasi terjadinya titik api dan ukuran sejati dari titik api. Analisis Average Nearest Neighbor menunjukkan bahwa titik api terkumpul pada beberapa tempat dan tidak tersebar merata.

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.

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.

Spatial Autocorrelation in California Land Birds

1998 ◽  
Vol 12 (3) ◽  
pp. 612-620 ◽  
Author(s):  
Walter D. Koenig
Keyword(s):  
Spatial Autocorrelation

Geospatial socio‐economic/demographic data: The existence of spatial autocorrelation mixtures in georeferenced data—Part I

2021 ◽  
Author(s):  
Daniel A. Griffith ◽  
Khushboo Agarwal ◽  
Meifang Chen ◽  
Changho Lee ◽  
Emily Ann Panetti ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Demographic Data
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