Model Geographically Weighted Regression dengan Fungsi Pembobot Adaptive dan Fixed Kernel pada Kasus Kematian Ibu di Jawa Timur

Model Geographically Weighted Regression (GWR) merupakan pengembangan dari model regresi linier berganda yang dapat menghasilkan penduga parameter model yang bersifat lokal untuk setiap titik atau lokasi di mana data diamati. Model GWR dapat digunakan apabila data memenuhi asumsi heterogenitas spasial yang diakibatkan oleh perbedaan kondisi data antara satu lokasi dengan lokasi lain. Penelitian ini bertujuan untuk menentukan model GWR terbaik dengan pembobot adaptive kernel dan fixed kernel pada kasus kematian ibu di Jawa Timur tahun 2018. Data yang digunakan pada penelitian ini adalah data kematian ibu sebagai variabel respon dan rumah tangga berperilaku hidup bersih sehat, kunjungan ibu hamil dengan K4, ibu hamil mendapat tablet Fe3, persalinan yang ditolong tenaga kesehatan, serta jumlah fasilitas kesehatan sebagai variabel prediktor. Berdasarkan kriteria pemilihan model terbaik yang dilihat dari nilai AIC terkecil dapat disimpulkan bahwa model GWR dengan fungsi pembobot adaptive bi- square kernel merupakan model terbaik untuk data kematian ibu. Faktor yang mempengaruhi kasus kematian ibu berdasarkan pengujian parameter secara parsial yaitu kunjungan ibu hamil dengan K4 dan jumlah fasilitas kesehatan.

Aplikasi Metode Geographically Weighted Regression Determinan Kemiskinan Multidimensi Kabupaten/kota di Kawasan Timur Indonesia

Masih terjadi ketidakmerataan kemiskinan antar wilayah di Indonesia dimana sebagian besar provinsi di Kawasan Timur Indonesia (KTI) memiliki nilai Angka Kemiskinan Multidimensi (AKM) yang lebih tinggi daripada Kawasan Barat Indonesia (KBI) dan angka nasional. Beberapa provinsi di KTI bahkan memiliki angka kemiskinan multidimensi dengan perbedaan nilai yang cukup jauh dibandingkan angka kemiskinan moneter wilayah tersebut. Wilayah KTI yang memiliki nilai AKM tinggi cenderung mengelompok secara geografis. Penelitian ini bertujuan untuk menganalisis variabel-variabel yang berpengaruh pada AKM kabupaten/kota di KTI dengan mempertimbangkan pengaruh spasial. Analisis yang digunakan dalam penelitian ini yaitu analisis inferensia dengan Geographically Weighted Regression (GWR). Hasil penelitian menunjukkan bahwa terdapat efek dan heteregenitas spasial pada pemodelan kemiskinan multidimensi di KTI. Rasio ketergantungan, IPM, rasio rumah sakit terhadap penduduk, persentase desa dengan dengan permukaan jalan mayoritas aspal, dan persentase desa tanpa sinyal internet memiliki pengaruh signifikan terhadap AKM yang berbeda-beda untuk setiap kabupaten/kota di KTI. Pemodelan menggunakan GWR menunjukkan hasil yang lebih baik daripada model regresi global.

Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

Estimation model of PM2.5 Concentrations Based on Spatiotemporal Adaptability and Satellite Remote Sensing

Based on satellite remote sensing AOD, we can estimate and monitor the continuous changes of PM2.5, which solved the disadvantages of traditional ground station discrete monitoring. Four-dimensional spatiotemporal heterogeneity is not considered in the construction of traditional empirical regression models, such as geographically weighted regression model (GWR) and spatiotemporal geographically weighted regression model (gtwr). To solve this four-dimensional spatiotemporal nonstationarity, this article proposes and constructs a spatiotemporal adaptive fine particulate matter (PM2.5) concentration estimation model: 4D-GTWR by introducing a DEM (Digital elevation model) and time effects into a GWR model. This method solves the heterogeneity between the three-dimensional space and one-dimensional time by constructing a four-dimensional space kernel function and obtaining its weight. Based on PM2.5 ground observation data and meteorological data collected from December 2017 to February 2018 in Zhengzhou City, Henan Province, PM2.5 estimations are obtained from MODIS MYD-3K AOD data using the GWR, TWR, GTWR and 4D-GTWR models. The results showed that the MAE (mean absolute error) of the 4D-GTWR model decreased by 54.13%, 54.06% and 37.90%, compared to those of the GWR, TWR and GTWR models, respectively, and that the PM2.5 concentrations predicted by the 4D-GTWR model were closest to the measured values. The R2 (the correlation coefficient) of the 4D-GTWR model was 0.9496, which was better than those of the GWR (R2 =0.7761), TWR (R2 =0.7763) and GTWR (R2=0.8811) models. The 4D-GTWR model can not only improve the precision of PM2.5 estimations but can also reveal the four-dimensional spatial heterogeneity of PM2.5 concentrations and the differentiation of the DEM's influence on the spatial dimensions.