High-precision modeling of CNCs’ spatial errors based on screw theory

A method for establishing machine tool’s spatial error model is put forward based on screw theory, which is different from the traditional error modeling method. By analyzing the position relationship between the ideal coordinate vector and the actual coordinate vector jointly affected by linear errors and angular errors, a single-axis screw conversion matrix error expression is brought up based on screw theory. Meanwhile, the comprehensive spatial error model of the CNC machine tool is derived by considering the influence of the workpiece motion chain and the tool motion chain on the model. Further, to compensating spatial errors of CNCs, such screw theory-based model is embedded in the error compensation system by means of integration of a few specific application examples. And in order to evaluate the compensation effects, an integrated evaluation method of quantitative spatial diagonal calculation and MATLAB simulation is proposed. Application results show that the screw theory-based spatial error model of tool has a very substantial compensation effect, which makes the position error of the machine tool decreased by about 80%.

Pemodelan Data Kemiskinan di Provinsi Jawa Barat Menggunakan Regresi Spasial Data Panel

Persentase kemiskinan di Provinsi Jawa Barat masih tergolong cukup tinggi dan masih menjadi fokus perhatian pemerintah. Faktor-faktor yang memengaruhi kemiskinan penting untuk diketahui agar pemerintah mampu membuat suatu kebijakan yang tepat untuk menekan angka kemiskinan. Oleh karena itu, pada penelitian ini, dilakukan suatu analisis untuk mengetahui faktor-faktor yang memengaruhi tingkat kemiskinan di Provinsi Jawa Barat. Analisis pada penelitian ini menggunakan metode regresi spasial data panel karena diduga pada data kemiskinan di Provinsi Jawa Barat terdapat efek spasial dan unit amatan diamati pada jangka waktu tertentu. Model yang terbentuk dari analisis ini adalah Fixed Effect Spatial Error Model karena interaksi spasial yang terbentuk pada data kemiskinan di Provinsi Jawa Barat nyata pada error. Model ini juga berhasil menjadi model terbaik dibandingkan model lainnya berdasarkan kriteria nilai R-square terbesar. Analisis data kemiskinan di Provinsi Jawa Barat menggunakan metode regresi spasial data panel memperoleh hasil bahwa usia harapan hidup, pengeluaran per kapita disesuaikan, dan rata-rata lama sekolah berpengaruh secara signifikan terhadap persentase penduduk miskin di Provinsi Jawa Barat.

Penerapan Spatial Error Model (SEM) untuk Mengetahui FaktorFaktor yang Memengaruhi Kriminalitas

Masyarakat berisiko menjadi korban tindak kriminalitas. Semakin tinggi risiko maka semakin tidak aman suatu daerah. Berdasarkan data Badan Pusat Statistik (BPS) dari tahun 2009-2020 kriminalitas di Jawa Tengah selalu menempati posisi sepuluh besar tertinggi di Indonesia. Walaupun pada tahun 2020 kasus kriminalitas di Jawa Tengah menurun pada kasus curat dan curanmor, namun kasus seperti narkoba, curas, dan uang palsu bertambah. Hal ini dapat merugikan psikologis dan ekonomis sehingga masyarakat menentangnya. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi kriminalitas di Jawa Tengah dengan menggunakan regresi spasial. Hasil analisis yang diperoleh Tingkat Pengangguran Terbuka, Upah Minimum Kabupaten/Kota, Harapan Lama Sekolah, dan proporsi penduduk laki-laki memengaruhi tingkat kriminalitas di Jawa Tengah. Kemudian terdapat interaksi spasial antar 35 kabupaten/kota di Jawa Tengah dengan Spatial Error Model (SEM).

Determinan Tingkat Kemiskinan Kabupaten/Kota Di Provinsi Jawa Timur Tahun 2017-2019 Menggunakan Spatial Error Model dengan pendekatan Fixed Effect

Pada tahun 2019 terjadi pengelompokan tingkat kemiskinan kabupaten/kota di Jawa Timur. Tingkat kemiskinan yang tinggi berada di wilayah utara, sementara wilayah bagian tengah hingga bagian selatan Jawa Timur sudah memiliki tingkat kemiskinan yang tergolong rendah. Hal ini mengindikasikan tingkat kemiskinan kabupaten/kota di Jawa Timur memiliki keterkaitan spasial antarwilayah. Pola keterkaitan spasial ini juga terlihat di tahun 2017 dan 2018. Maka dari itu, penelitian ini bertujuan mengidentifikasi keterkaitan spasial tingkat kemiskinan kabupetan/kota di Jawa Timur dan variabel-variabel yang memengaruhi tingkat kemiskinan tersebut dari tahun 2017 sampai 2019. Model yang diterapkan adalah SEM dengan pendekatan FEM. Penelitian ini mencakup seluruh wilayah di Jawa Timur. Variabel dependen dari penelitian ini merupakan tingkat kemiskinan. Variabel yang diduga memengaruhi tingkat kemiskinan adalah pertumbuhan ekonomi, IPM, dan jumlah penduduk. Data keseluruhan variabel dikutip dari BPS. Periode penelitian ini dari tahun 2017 sampai 2019. Periode penelitian ini dipilih dengan pertimbangan ketersediaan data untuk berbagai variabel yang diperlukan. Dari model terbaik diperoleh pertumbuhan ekonomi, IPM, dan jumlah penduduk berpengaruh signifikan dalam penurunan tingkat kemiskinab. Selain faktor tersebut ada faktor lain yang dapat memengaruhi tingkat kemiskinan yang berada pada wilayah yang dianggap bertetanggaan.

The Effect of Socioeconomic and Environmental Factors on Obesity

This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.

Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China

In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban heat environment of a commercial area is determined by the land use factors of the surrounding area. Fourth, the UHI effect in industrial and commercial areas could be precisely mitigated by not locating industrial areas beside residential areas, and setting up buffer zones between commercial areas and surrounding traditional residential areas. Overall, the results of this study innovatively deepen the understanding of the impact of the percentage of different urban land use types on the urban heat environment at the scale of planning management units, which is conducive to formulating precise regulation measures for mitigating UHI effects and improving public health.

The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

Modeling Method of CNC Machine Tools’ Volume Error Considering Abbe Error

Abbe error is an important factor affecting high-precision machine tools, and the traditional modeling method does not consider Abbe error. Aiming at this problem, based on the traditional error model of machine tools and the formation mechanism of Abbe error, this paper establishes a machine tool spatial error model that considers Abbe error. Then combined with a specific machine tool, based on the measurement of 21 geometric errors of the machine tool to obtain relevant error data, through the combination of qualitative and quantitative accuracy evaluation methods, two models of traditional error model and error model considering Abbe error are analyzed. The accuracy of the machine tool is compared, and the comparison of the compensation effects of the two error models after compensation is also analyzed. The example verification shows that the machine tool spatial error model considering Abbe error is effective and feasible, and the compensation effect is better. It provides an important modeling method for improving the machining accuracy of precision machine tools.

Perbandingan Spatial Autoregressive Model dan Spatial Error Model dalam Pemodelan Indeks Pembangunan Manusia di Provinsi Jawa Timur

Indeks Pembangunan Manusia (IPM) merupakan indikator untuk mengukur keberhasilan upaya pembangunan kualitas hidup manusia yang telah dicapai. Pertumbuhan IPM di suatu wilayah dapat dipengaruhi oleh faktor geografis yaitu besarnya angka IPM di suatu wilayah dapat memengaruhi angka IPM pada wilayah yang berdekatan sehingga faktor geografis diduga dapat memengaruhi dan memberikan efek dependensi spasial pada nilai IPM di Provinsi Jawa Timur. Penelitian ini bertujuan untuk melakukan pemodelan pada faktor yang berpengaruh terhadap Indeks Pembangunan Manusia di Provinsi Jawa Timur. Unit pengamatan pada penelitian ini adalah 38 kabupaten/kota di Provinsi Jawa Timur. Data yang digunakan adalah data sekunder dari Badan Pusat Statistik Jawa Timur tahun 2017. Metode analisis yang digunakan dalam penelitian ini adalah metode Spatial Autoregressive Model (SAR) dan Spatial Error Model (SEM). Hasil penelitian menunjukkan bahwa berdasarkan nilai uji Lagrange Multiplier (lag) dan Lagrange Multiplier (error) terdapat dependensi lag dan error. Variabel prediktor yang secara signifikan berpengaruh terhadap nilai IPM pada model SAR dan SEM antara lain Angka Harapan Hidup, Rata-rata Lama Sekolah, Angka Harapan Lama Sekolah dan Kemampuan daya beli masyarakat. Berdasarkan hasil penelitian didapatkan model SEM dengan nilai R2 terbesar dan nilai AIC terkecil sehingga model SEM lebih baik digunakan untuk menganalisis nilai IPM di Provinsi Jawa Timur dibandingkan model SAR dan model regresi OLS.