scholarly journals Low spatial autocorrelation in mountain biodiversity data and model residuals

Ecosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Mathieu Chevalier ◽  
Heidi Mod ◽  
Olivier Broennimann ◽  
Valeria Di Cola ◽  
Sarah Schmid ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Biodiversity Data ◽  
Model Residuals

scholarly journals Environmental DNA reveals that rivers are conveyer belts of biodiversity information

2015 ◽  
Author(s):  
Kristy Deiner ◽  
Emanuel A. Fronhofer ◽  
Elvira Meächler ◽  
Jean-Claude Walser ◽  
Florian Altermatt
Keyword(s):  
Spatial Autocorrelation ◽  
Empirical Data ◽  
Spatial Scales ◽  
Environmental Dna ◽  
River Networks ◽  
Water Interface ◽  
Biodiversity Data ◽  
Land Water ◽  
Biodiversity Information ◽  
Game Changer

AbstractDNA sampled from the environment (eDNA) is becoming a game changer for uncovering biodiversity patterns. By combining a conceptual model and empirical data, we test whether eDNA transported in river networks can be used as an integrative way to assess eukaryotic biodiversity for broad spatial scales and across the land-water interface. Using an eDNA metabarcode approach we detected 296 families of eukaryotes, spanning 19 phyla across the catchment of a river. We show for a subset of these families that eDNA samples overcome spatial autocorrelation biases associated with classical community assessments by integrating biodiversity information over space. Additionally, we demonstrate that many terrestrial species can be detected; thus revealing that eDNA in river-water also incorporates biodiversity information across terrestrial and aquatic biomes. Environmental DNA transported in river networks offers a novel and spatially integrated way to assess total biodiversity for whole landscapes and will transform biodiversity data acquisition in ecology.“Eventually, all things merge into one, 32 and a river runs through it.” — Norman Maclean

scholarly journals Using Spatial Regression Models in Identifying the Drivers of Forest Structure in the Hyrcanian Forests of Iran

2020 ◽  
Author(s):  
Seyed Jalil Alavi ◽  
Vria Mardanpour ◽  
Carsten F. Dormann
Keyword(s):  
Regression Model ◽  
Spatial Autocorrelation ◽  
Tree Height ◽  
Generalized Least Squares ◽  
Forest Growth ◽  
Hyrcanian Forests ◽  
Simultaneous Autoregressive Model ◽  
Model Residuals ◽  
Simultaneous Autoregressive ◽  
Better Than

Abstract Background: Understanding the relationships between forest structure, in particular attainable height, and the environment is important for sustainable forest management. Similarly, modeling structural attributes improve our understanding of forest growth dynamics and may identify key drivers of long-term changes in the forest ecosystem. Due to the inherent complexity of these relationships, quantification of some drivers of forest growth is often not available, resulting in spatially auto-correlated errors of the regression model. Methods: To explore the tree height-environment relationships of oriental beech we compared the performance of a standard regression model (multiple linear regression, MLR) to those accommodating a spatial correlation structure, specifically a Generalized Least Squares model with exponential correlation structure (GLS) and three variations of the Simultaneous Autoregressive Model (SAR): the spatial lag model (SLM), the spatial Durbin model (SDM) and the spatial error model (SEM). Across 127 0.1 ha circular sample plots in the primeval World Heritage Hyrcanian Forests of Iran, we collected data on tree height and edaphic and topographic. Within each plot, the height of all trees with DBH ≥ 7 cm was measured. Results: The results showed that SAR and GLS models reduced spatial autocorrelation of model residuals and improved model fit, with both SDM and SEM slightly superior to the SLM in removing spatial autocorrelation in the model residuals. SDM performs better than SEM in terms of RMSE and adjusted R2. Conclusions: Although SAR-based models performed marginally better than GLS, we still recommend GLS for spatial analyses due to their easier implementation and ease-of-use compared to SAR models. However, when the computation time is a concern, SAR-based models can be more useful because of faster execution. Keywords: spatial autocorrelation; Hyrcanian forests; multiple linear regression model; simultaneous autoregressive model; generalized least squares

Fitting Spatial Models of Geometric Deviations of Free-Form Surfaces Determined in Coordinate Measurements

2010 ◽  
Vol 17 (4) ◽  
pp. 599-610 ◽  
Author(s):  
Małgorzata Poniatowska ◽  
Andrzej Werner
Keyword(s):  
Spatial Autocorrelation ◽  
Measurement Data ◽  
Spatial Models ◽  
Spatial Modelling ◽  
Free Form ◽  
Spatial Process ◽  
Geometric Deviations ◽  
Coordinate Measurements ◽  
Model Residuals ◽  
Free Form Surfaces

Fitting Spatial Models of Geometric Deviations of Free-Form Surfaces Determined in Coordinate MeasurementsLocal geometric deviations of free-form surfaces are determined as normal deviations of measurement points from the nominal surface. Different sources of errors in the manufacturing process result in deviations of different character, deterministic and random. The different nature of geometric deviations may be the basis for decomposing the random and deterministic components in order to compute deterministic geometric deviations and further to introduce corrections to the processing program. Local geometric deviations constitute a spatial process. The article suggests applying the methods of spatial statistics to research on geometric deviations of free-form surfaces in order to test the existence of spatial autocorrelation. Identifying spatial correlation of measurement data proves the existence of a systematic, repetitive processing error. In such a case, the spatial modelling methods may be applied to fitting a surface regression model representing the deterministic deviations. The first step in model diagnosing is to examine the model residuals for the probability distribution and then the existence of spatial autocorrelation.

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.

Sharing and publishing of biodiversity data: recent trends and future suggestions

2014 ◽  
Vol 22 (3) ◽  
pp. 293
Author(s):  
Huang Xiaolei ◽  
Qiao Gexia
Keyword(s):  
Biodiversity Data ◽  
Recent Trends

Spatial Autocorrelation Analysis as an Inferential Tool in Population Genetics

1991 ◽  
Vol 138 (2) ◽  
pp. 518-521 ◽  
Author(s):  
Robert R. Sokal ◽  
Neal L. Oden
Keyword(s):  
Population Genetics ◽  
Spatial Autocorrelation ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis
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