Principal Component Regression in Spatial Lag Model: Teen Employment in the City of Rosario, Argentina

2018 ◽  
Vol 51 (1) ◽  
pp. 50-72
Author(s):  
Alejandro Izaguirre ◽  
Laura Di Capua ◽  
José Luis Pellegrini
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Teen Employment ◽  
Lag Model ◽  
The City

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Analiza zróżnicowania sytuacji kobiet na lokalnych rynkach pracy w Polsce w latach 2010–2016

2019 ◽  
Vol 2 (341) ◽  
pp. 99-115
Author(s):  
Karolina Lewandowska‑Gwarda
Keyword(s):  
Seemingly Unrelated Regression ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Unrelated Regression ◽  
Lag Model

Głównym celem artykułu jest ocena sytuacji kobiet na lokalnych rynkach pracy w Polsce oraz analiza jej zróżnicowania w czasie i przestrzeni. Podjęto w nim również próbę specyfikacji determinant badanego zjawiska. W analizach wykorzystano taksonomiczny miernik rozwoju, metody geograficznych systemów informacyjnych, metody eksploracyjnej analizy danych przestrzennych oraz wielorównaniowy model o równaniach pozornie niezależnych z autoregresją przestrzenną SUR‑SLM (Seemingly Unrelated Regression Spatial Lag Model). Badania przeprowadzono na podstawie danych statystycznych dla NUTS4 w latach 2010, 2012, 2014 i 2016. Na podstawie uzyskanych wyników zauważono, że zróżnicowanie sytuacji kobiet na lokalnych rynkach pracy w Polsce nie jest duże, niemniej jednak w nieco lepszej sytuacji są Polki mieszkające w okolicach stolicy oraz w zachodniej części kraju. Stwierdzono również, że sytuacja kobiet na lokalnych rynkach pracy nie zmieniła się znacząco w czasie. Dodatkowo potwierdzono, że nie tylko czynniki ekonomiczne, ale w dużej mierze również społeczne wpływają na analizowane zjawisko.

Spatial Aspects of Mortality Rates and Neighborhood Environmental Characteristics in Seoul Mega City Region, South Korea

2015 ◽  
Vol 6 (4) ◽  
pp. 44-64
Author(s):  
Yoohyung Joo ◽  
Hee Yeon Lee
Keyword(s):  
Spatial Patterns ◽  
Statistical Significance ◽  
Mortality Rates ◽  
Spatial Disparity ◽  
City Region ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Spatial Aspect ◽  
Lag Model ◽  
Gwr Model

This study of the spatial patterns of standardized mortality rates (SMRs) in Seoul Mega City Region (SMCR) explores whether neighborhood characteristics affect mortality rates and identifies important determinants of spatial disparity in mortality rates in SMCR. Spatial patterns of mortality rates show a strong positive spatial autocorrelation, suggesting that mortality rates are spatially clustered. A spatial lag model and a GWR model were used to reflect the spatial aspect of mortality rates. The spatial lag model showed better model fitness by considering spatial dependence of mortality rates. It indicates that a higher level of residential deprivation, a less walkable environment, less economic affluence and less social participation are all associated with higher mortality rates with statistical significance. This study suggests that health and welfare policy could incorporate urban planning to consider the neighborhood factors which determine mortality rates in order to improve the health of neighborhood residents.

scholarly journals Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology

2019 ◽  
Vol 8 (2) ◽  
pp. 50 ◽  
Author(s):  
Tara Smith ◽  
J. Sandoval
Keyword(s):  
Spatial Variability ◽  
Census Tract ◽  
Social Characteristics ◽  
Block Group ◽  
Saint Louis ◽  
Multi Scale ◽  
Spatial Lag Model ◽  
Group Data ◽  
Lag Model ◽  
The City

The current study spatially examines the local variability of robbery rates in the City of Saint Louis, Missouri using both census tract and block group data disaggregated and standardized to the 250- and 500-m raster grid spatial scale. The Spatial Lag Model (SLM) indicated measures of race and stability as globally influencing robbery rates. To explore these relationships further, Geographically Weighted Regression (GWR) was used to determine the local spatial variability. We found that the standardized census tract data appeared to be more powerful in the models, while standardized block group data were more precise. Similarly, the 250-m grid offered greater accuracy, while the 500-m grid was more robust. The GWR models explained the local varying spatial relationships between race and stability and robbery rates in St. Louis better than the global models. The local models indicated that social characteristics occurring at higher-order geographies may influence robbery rates in St. Louis.

scholarly journals Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
Family Income ◽  
Census Data ◽  
Principal Component ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Spatial Lag Model ◽  
Economic Parameters ◽  
The City

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.

scholarly journals PEMODELAN REGRESI SPASIAL PADA TPT DI DKI JAKARTA, JAWA BARAT, DAN BANTEN TAHUN 2018

2021 ◽  
Vol 2020 (1) ◽  
pp. 728-738
Author(s):  
Muhammad Suprapto ◽  
Gama Putra Danu Sohibien
Keyword(s):  
Lagrange Multiplier ◽  
Moran’S I ◽  
Test Model ◽  
Moran's I ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lm Test ◽  
Lag Model

Pengangguran merupakan masalah utama yang dihadapi di berbagai negara baik negara maju maupun negara berkembang. Masalah pengangguran tidak terlepas kaitannya dengan dimensi wilayah atau dependensi spasial. Keberadaan dependensi spasial menunjukkan bahwa tingkat pengangguran di suatu wilayah akan berhubungan dengan tingkat pengangguran wilayah tetangganya. Penelitian ini bertujuan untuk memberikan gambaran umum Tingkat Pengangguran Terbuka (TPT) dan variabel-variabel yang diduga mempengaruhinya, mendapatkan model terbaik dalam menjelaskan pengaruh variabel-variabel independen terhadap TPT, dan menganalisis pengaruh variabel-variabel independen dari model terbaik terhadap TPT serta hubungan pengangguran antar wilayah. Analisis gambaran umum TPT dilakukan dengan pemetaan. Sedangkan untuk mendapatkan model terbaik dalam menjelaskan pengaruh variabel-variabel independen terhadap TPT diawali dengan pembentukan model regresi linier berganda. kemudian dilanjutkan dengan diagnosis keberadaan efek spasial dengan menggunakan Moran’s I dan Lagrange Multiplier (LM) test. Model terbaik yang terbentuk adalah Spatial Lag Model dengan variabel independen yang signifikan. variabel-variabel independen yang mempengaruhi TPT di DKI Jakarta, Jawa Barat, dan Banten adalah persentase penduduk status kawin dan persentase penduduk miskin.

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