scholarly journals Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Oluyemi A. Okunlola ◽  
Mohannad Alobid ◽  
Olusanya E. Olubusoye ◽  
Kayode Ayinde ◽  
Adewale F. Lukman ◽  
...  
Keyword(s):  
Spatial Model ◽  
Spatial Regression ◽  
Spatial Models ◽  
Cold Spot ◽  
Precipitation Data ◽  
Error Structure ◽  
Robust Approach ◽  
Spatial Linear Model ◽  
Spatial Autoregressive ◽  
Definition Of

AbstractIn this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

Nonparametric spatial regression with spatial autoregressive error structure

Statistics ◽  
2015 ◽  
Vol 50 (1) ◽  
pp. 60-75
Author(s):  
Hongxia Wang ◽  
Jinguan Lin ◽  
Jinde Wang
Keyword(s):  
Spatial Regression ◽  
Error Structure ◽  
Spatial Autoregressive

Introduction

2018 ◽  
Author(s):  
Justin Buchler
Keyword(s):  
Spatial Model ◽  
Spatial Models ◽  
Spatial Theory ◽  
Roll Call ◽  
Policy Space ◽  
Policy Decisions ◽  
Roll Call Voting ◽  
Legislative Session ◽  
History Of ◽  
Conventional Models

Spatial theory is divided between models of elections and models of roll call voting, neither of which alone can explain congressional polarization. This chapter discusses the history of spatial theory, why it is important to link the two strands of spatial models, and the value of reversing the order of conventional models. Conventional models place an election before policy decisions are made. This chapter proposes a unified spatial model of Congress in which the conventional order is reversed. First, there is a legislative session, then an election in which voters respond retrospectively, not to the locations candidates claim to hold, but to the bundles of roll call votes that incumbents cast to incrementally adopt their locations in the policy space. Such a model is best suited to explaining three puzzles: why do legislators adopt extreme positions, how do they win, and what role do parties play in the process?

scholarly journals Spatial analysis for political scientists

2021 ◽  
pp. 1-17
Author(s):  
Jessica Di Salvatore ◽  
Andrea Ruggeri
Keyword(s):  
Comparative Politics ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Spatial Models ◽  
Building Blocks ◽  
Spatial Relationships ◽  
Electoral Studies ◽  
Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

scholarly journals SPATIAL CONVERGENCE OF THE GROSS VALUE OF PRODUCTION OF FIREWOOD IN THE MESOREGIONS OF THE BRAZILIAN NORTHEAST

2018 ◽  
Vol 42 (2) ◽  
Author(s):  
Luiz Moreira Coelho Junior ◽  
Kalyne de Lourdes da Costa Martins ◽  
Magno Vamberto Batista da Silva
Keyword(s):  
Spatial Dependence ◽  
Classical Model ◽  
Spatial Models ◽  
Information Criterion ◽  
Spatial Error ◽  
Spatial Convergence ◽  
Spatial Autoregressive ◽  
Classical Linear Regression ◽  
Object Of Study ◽  
Brazilian Northeast

ABSTRACT This paper analyzed the process of convergence in the gross value of wood production in mesoregions of Northeast Brazil, in the period of 1994 and 2013. The object of study was the Gross Value of Production (GVP) of firewood per km2 of the mesoregions of the Northeast of Brazil. In the methodology the Absolute Convergence Model was applied and estimated through the classical model and spatial models. In the spatial approach we used the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). From the results obtained, the following conclusions were reached: The mesoregions of the Northeast of Brazil had an average fall of 3.94% a.a. of the GVP/km2 of native wood for the period 1994 to 2013. Considering the classical linear regression model, convergence was verified and also the presence of spatial dependence for GVP/km2 of firewood. In order to correct the spatial dependence, the SAR and SEM Models were adequate and according to Akaike's Information Criterion and used the rook matrix the SEM was configured the best model. This study showed the importance of the involvement of the spatial question in the models, either by the overlap of information of the GVP and in the development of public policies that positively affect the neighborhood.

scholarly journals ANALISIS REGRESI SPASIAL DAN POLA PENYEBARAN PADA KASUS DEMAM BERDARAH DENGUE (DBD) DI PROVINSI JAWA TENGAH

2017 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
Inna Firindra Fatati ◽  
Hari Wijayanto ◽  
Agus M. Sholeh
Keyword(s):  
Spatial Regression ◽  
Spatial Models ◽  
Hemorrhagic Fever ◽  
Spatial Effect ◽  
Health Centers ◽  
Central Java ◽  
Local Moran ◽  
Spatial Regression Analysis ◽  
Different Characteristics ◽  
Central Java Province

Dengue Hemorrhagic Fever (DHF) is one of the diseases that threaten human health. The cases of dengue fever in the district / city certainly has different characteristics, geographic condition, the potential of the region, health facilities, as well as other matters that lie behind them. Based on local moran index values are visualized through thematic maps, some area adjacent quadrant tends to be in the same group. There are two significant quadrant in describing the pattern of spread of dengue cases namely quadrant high-high and lowlow. This indicates a spatial effect on the number of dengue cases, so that the spatial regression analysis. Based on the value of  and AIC, autoregressive spatial models (SAR) is good enough to be used in modeling the number of dengue cases in the province of Central Java. Factors that influence the number of dengue cases Central Java province in 2015 is the number of health centers per 1000 population, the number of polindes per 1000 population, population density (X3), percentage of people with access to drinking water sustainable decent (X6), the percentage of water quality net free of bacteria, fungi and chemicals (X7), and the number of facilities protected springs (X8).

scholarly journals The pencil of the 4th and 3rd order surfaces obtained as a harmonic equivalent of the pencil of quadrics through a 4th order space curve of the 1st category

2012 ◽  
Vol 10 (2) ◽  
pp. 193-207 ◽  
Author(s):  
Gordana Djukanovic ◽  
Marija Obradovic
Keyword(s):  
Cross Sections ◽  
Spatial Model ◽  
Spatial Models ◽  
Space Curve ◽  
Parabolic Cylinder ◽  
Triaxial Ellipsoid ◽  
Architectural Practice ◽  
Ordered Space ◽  
Pass Through ◽  
Frontal Projection

This paper shows the process of inverting the 4th ordered space curve of the first category with a self-intersecting point (with two planes of symmetry) and determining its harmonic equivalent. There are harmonic equivalents for five groups of surfaces obtained through the 4th order space curve of the 1st category. Mapping was done through a system of circular cross-sections. Both classical and relativistic geometry interpretations are presented. We also designed spatial models - a spatial model of the pencil of quadrics and a spatial model of the pencil of equivalent quadrics. Besides the boundary surfaces, one surface of the 3rd order, which is an equivalent to a triaxial ellipsoid, passes through this pencil of surface of the 4th order. The center of inversion is located on the contour of the ellipsoid. The parabolic cylinder is mapped into its equivalent, by mapping the contour parabola of the cylinder, in the frontal projection, in relation to the center and the sphere of inversion into a contour curve of the 4th order surface. The generating lines of the parabolic cylinder, which are in a projecting position and pass through the antipode, are mapped into circles (also in a projecting position) whose diameters are from the center of inversion to the contour line. The application of the 4th order surfaces in architectural practice is also presented.

scholarly journals The tourism space of Barlinek in the context of spatial models

Turyzm ◽  
2018 ◽  
Vol 28 (2) ◽  
pp. 31-40
Author(s):  
Bartłomiej Łuć
Keyword(s):  
Spatial Model ◽  
Swot Analysis ◽  
Spatial Models ◽  
Tourism Development ◽  
Field Studies ◽  
Tourism Attractions

This article describes the tourism space of Barlinek in the context of a spatial model. On the basis of field studies and analyses of tourism attractions and elements of tourism development, the author has compared and adapted the models developed by S. Liszewski (1995) and B. Włodarczyk (2011). Moreover the author has developed an extended SWOT analysis of Barlinek’s tourism space.

scholarly journals Spatial model of water quality in Southeast Asian urban lake based on HBI using macrozoobenthos diversity as a proxy

2021 ◽  
Author(s):  
Andri Wibowo
Keyword(s):  
Water Quality ◽  
Model Selection ◽  
Water Temperature ◽  
Spatial Model ◽  
Aquatic Organism ◽  
Biotic Index ◽  
Urban Lake ◽  
Anthropogenic Pressures ◽  
Robust Approach ◽  
Temperature Variable

AbstractUrban lake is one of ecosystem that has experienced anthropogenic pressures and this can affect its water quality. One of a robust approach to assess the water quality is by using Hilsenhoff Biotic Index (HBI). This tool is quite versatile since it can be applied by using any aquatic organism as proxy including macrozoobenthos. This invertebrate group also has an advantage since it is common and easy to collect. Here this study is first, aiming to provide HBI based water quality spatial model using macrozoobenthos as a proxy applied in urban lake in West Java in Southeast Asia and second to seek the best model that can represent the water quality variables in particular dissolved oxygen (DO), pH, and temperature. Based on the spatial model and HBI, either inlet or outlet parts of the lake, it has better water quality in comparison to central parts. Based on HBI values, water quality in inlet and outlet parts (HBI = 6.7) is categorized as fair and poor (HBI = 6.9) for the central parts of the lake. The increase in HBI and decrease in water quality are positively correlated with the increase in water temperature variable in comparison to water DO and pH variables. Akaike model selection confirms that the macrozoobenthos diversity can be used as a proxy for increase in water temperature (Ψ)HBI (~temp)(AIC = −10.264) followed by combination of water temperature increase and decrease in DO(Ψ)HBI (~temp+DO)(AIC = −9.042398).

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

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