ANALISIS DATA KEMISKINAN DI JAWA TENGAH MENGGUNAKAN METODE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSIONS (MGTWR)

Metode regresi merupakan salah satu metode statistika yang dapat digunakan untuk menganalisis data kemiskinan. Akan tetapi untuk data spasial model regresi biasa menjadi tidak sesuai. Salah satu metode regresi spasial yang digunakan untuk data spasial adalah Geographically Weighted Regression (GWR). Akan tetapi jika variabel waktu juga dimasukkan ke dalam model, maka model yang digunakan adalah Geographically and Temporally Weighted Regression (GTWR). Pada kenyataannya tidak semua variabel prediktor dalam model GWR mempunyai pengaruh secara spasial. Beberapa variabel prediktor berpengaruh secara global, sedangkan yang lainnya dapat mempertahankan pengaruh spasialnya. Oleh karena itu, model GWR dikembangkan menjadi model Mixed Geographically Weighted Regression (MGWR). Model MGWR merupakan gabungan dari model regresi linier global dengan model GWR. Hal ini berlaku juga untuk model GTWR yang dikembangkan menjadi model Mixed Geographically and Temporally Weighted Regression (MGTWR). Hasil penelitian menunnjukkan bahwa faktor-faktor yang mempengaruhi Persentase Kemiskinan di Jawa Tengah tahun 2010-2012 secara lokal adalah persentase keluarga prasejahtera. Sedangkan variabel Tingkat Partisipasi Angkatan Kerja, Indeks Pembangunan Manusia, Upah Minimum Regional dan Banyaknya Akte Pemilik Tanah hanya berpengaruh secara global pada semua lokasi pengamatan. Model MGTWR dengan pembobot fungsi kernel gaussian lebih layak digunakan untuk menganalisis tingkat kemiskinan di Jawa Tengah karena mempunyai nilai R2 terbesar. Kata Kunci : GWR, GTWR, MGWR, MGTWR, Regresi, Statistika Spasial, Kemiskinan

The Effect of Kernel and Bandwidth Specification in Geographically Weighted Regression Models on the Accuracy and Uniformity of Mass Real Estate Appraisal

The article presents a study which examines the performance of kernel and bandwidth specification in geographically weighted regression (GWR) models in mass real estate appraisal. The kernels employed in the study are the bi-square kernel and the Gaussian kernel. Data from the sales of single-family homes in Norfolk, Virginia from 2010 to 2012 are highlighted

Geographically Weighted Spline Nonparametric Regression dengan Fungsi Pembobot Bisquare dan Gaussian Pada Tingkat Pengangguran Terbuka Di Pulau Kalimantan

Geographically weighted spline nonparametric regression merupakan pengembangan regresi nonparametrik untuk data spasial dengan estimator parameter bersifat lokal setiap lokasi pengamatan yang diaplikasikan pada kasus tingkat pengangguran terbuka. Tingkat pengangguran terbuka menjadi alat ukur kualitas kesejahteraan di suatu wilayah yang mengindikasikan besarnya persentase penduduk usia kerja yang aktif secara ekonomi. Tujuan penelitian ini yaitu untuk mengidentifikasi faktor-faktor yang mempengaruhi tingkat pengangguran terbuka 56 Kabupaten/Kota di Kalimantan. Metode yang digunakan adalah geographically weighted spline nonparametric regression dengan pembobot fungsi kernel eksponensial. Model terbaik geographically weighted spline nonparametric regression dengan pembobot fungsi kernel eksponensial pada orde 1 titik knot 1 dengan nilai R-Square sebesar 86,410 persen, nilai AIC sebesar 12,152, nilai RMSE sebesar 0,584 serta nilai CV terkecil adalah fungsi kernel bisquare sebesar 77,175. Adapun faktor-faktor yang berpengaruh signifikan terhadap tingkat pengangguran terbuka yaitu  tingkat partisipan angkatan kerja, jumlah penduduk, indeks pembangunan manusia, harapan lama sekolah dan upah minimum

Imputación de datos espaciales de calidad del aire usando sig-spline e índice de ajuste en redes urbanas de monitoreo

RESUMEN: Este trabajo presenta un procedimiento para abordar la falta de datos espaciales de calidad del aire en zonas urbanas, con base en el uso de Sistemas de Información Geográfi ca (SIG) y las técnicas de interpolación espacial como una alternativa a los métodos convencionales de imputación estadística. Se comparan dos algoritmos de interpolación espacial: IDW y spline. El procedimiento considera el proceso de interpolación espacial, la validación cruzada con el índice de (IOA), y el análisis de la densidad de muestreo y del coefi ciente de variación utilizando diferentes estadísticos de error. Los mapas de interpolación se complementan con los mapas de gradiente y de gradiente direccional que pueden servir como complementos en la defi nición de puntos de muestreo críticos. El procedimiento se aplica a la imputación de datos de tres contaminantes: NO2 , PM10 (partículas de 10 micras de diámetro) y SST (sólidos suspendidos totales) a partir de muestras de datos observados en la ciudad de Medellín (Colombia).ABSTRACT: This paper presents a procedure to address the lack of spatial air quality data in urban areas, based on the use of Geographic Information Systems (GIS) and spatial interpolation techniques as an alternative to conventional methods of statistical imputation. Two spatial interpolation algorithms are compared: IDW and spline. The procedure considers the spatial interpolation process, the cross validation with the index of agreement (IOA), and the analysis of the effect of sampling density and the coeffi cient of variation (CVOi ), using different error statistics. The interpolation maps are complemented with gradient and directional gradient maps that may serve as complementary aides in the defi nition of critical sampling points. The procedure is applied to data imputation of three pollutants NO2 , PM10 (particulate matter of diameter 10 microns) and TSP (total suspended solids) from observed data samples in the city of Medellín (Colombia)

Evaluating Spatial Model Accuracy in Mass Real Estate Appraisal: A Comparison of Geographically Weighted Regression and the Spatial Lag Model

Geographically weighted regression (GWR) has been shown to greatly increase the performance of ordinary least squares-based appraisal models, specifically regarding industry standard measurements of equity, namely the price-related differential and the coefficient of dispersion (COD; Borst and McCluskey, 2008; Lockwood and Rossini, 2011; McCluskey et al., 2013; Moore, 2009; Moore and Myers, 2010). Additional spatial regression models, such as spatial lag models (SLMs), have shown to improve multiple regression real estate models that suffer from spatial heterogeneity (Wilhelmsson, 2002). This research is performed using arms-length residential sales from 2010 to 2012 in Norfolk, Virginia, and compares the performance of GWR and SLM by extrapolating each model\u27s performance to aggregate and subaggregate levels. Findings indicate that GWR achieves a lower COD than SLM

Real estate valuation using regression models and artificial neural networks: An applied study in Thessaloniki

In recent years a number of different methods based in statistical methods and machine learning have been dominating the area of computer-assisted appraisal, the most popular ones being the spatial auto-regressive (SAR) models, the geographically weighted regression (GWR) and artificial neural networks. In this presentation the above techniques are first analyzed. Then they are applied, alongside an ensemble and a stacked model, in the area of Thessaloniki (Greece) two times: once after enhancing the database with spatial attributes derived from GIS, and once more using only simple, easy to derive spatial attributes that do not need such an extensive framework.  The results of each model are presented, compared with each other and discussed in detail

Deep Spatio-temporal Learning Model for Air Quality Forecasting

In recent years, air pollution has seriously affected people’s production and life, so the air prediction has become a research hotspot in recent years. When analyzing air data, it is found that this type of data has not only temporal correlation, but also spatial correlation. For these temporal and spatial characteristics, this paper studies deep spatio-temporal learning method to global prediction. The purpose is to learn the evolution rule behind the spatio-temporal sequence, and give an estimation for future state. To be specific, we propose two novel forecasting models based on video processing technology: Spatio-temporal Orthogonal Cube model (STOR-cube) and Spatio-temporal Dynamic Advection model (ST-DA), which effectively capture the spatio-temporal correlation and accurately predict the long-term air quality. STOR-cube contains three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motion, and an output branch for coupling the first two mutually orthogonal branches to generate a prediction frame. ST-DA constructs a spatio-temporal reasoning network to learn the characteristics of the spatio-temporal domain, and its impact on the future is explicitly modeled by pixel motion. Experiments results on the real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones. Moreover, our model can be extended to other spatio-temporal data prediction tasks

The importance of scale in spatially varying coefficient modeling

While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the "spatial scale" of each data relationship is crucially important to make SVC modeling more stable, and in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (i) geographically weighted regression (GWR) with a fixed distance or (ii) an adaptive distance bandwidth (GWRa), (iii) flexible bandwidth GWR (FB-GWR) with fixed distance or (iv) adaptive distance bandwidths (FB-GWRa), (v) eigenvector spatial filtering (ESF), and (vi) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely GWR and ESF, where SVC estimates are naively assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR)

Influencing factors for the human development index in West Java using geographically and temporally weighted regression kernel functions

Human Development Index (HDI) is a competitive index that serves as one of the crucial metrics for evaluating the effectiveness of enhancing the quality of human resources. HDI values from different areas can be compared. This study aims to spatially and temporally explore the HDI data from districts or cities in West Java and examine the factors that influence HDI in each of these districts or cities using the GTWR Great Circle Distance Fixed Kernels model. In this study, we used a combination of cross-sectional data from districts or cities in West Java and time series data with seven annual periods from 2015-2021. The GTWR Great Circle Distance Fixed Kernels model was expected to display coefficient values at each location and time simultaneously, providing more in-depth information and analysis results at each location and time. The analysis results using the GTWR Great Circle Distance Fixed Kernels model show that HDI in West Java carries a positive influence on the location and time. This finding should be of particular concern to the relevant government, particularly the factors presenting a natural effect on HDI based on location and time. The positive influence obtained by an area at a particular time will also have a positive impact on other regions, and if there is a negative influence, it will undoubtedly affect other regions as well. Analysis of the HDI model in West Java using the GTWR Great Circle Distance Fixed Exponential Kernel model also presents better results in comparison to the Global OLS model and the GTWR model without the Great Circle Fixed Exponential Kernel. The final parameter estimator results are displayed in the form of a geographic map to facilitate ease of understanding

Geographical and temporal weighted regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling