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Approximately linear INGARCH models for spatio-temporal counts

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2023年第2期72卷 476-497页

作者： Jahn, Malte Weiss, Christian H. Kim, Hee-Young Helmut Schmidt Univ Dept Math & Stat D-22043 Hamburg Germany Korea Univ Dept Bigdata Sci Sejong South Korea

Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow for negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified and flexible spatio-temporal (B)INGARCH framework for modelling unbounded (bounded) counts is proposed. These models combine negative dependencies with kinds of a long memory. They are easily adapted to special marginal features or cross-dependencies: When modelling precipitation data (counts of rainy hours), we account for zero-inflation, while for cloud-coverage data (counts of okta), we deal with missing data and additional cross-correlation. A copula related to the spatial error model shows an appealing performance.

关键词： Gaussian copula INGARCH models soft clipping softplus spatial error model spatio-temporal counts

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spatial modelLING OF COVID-19 INCIDENCE RATE IN CANADA 24

SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Fatholahi, Sarah N. Pan, Charlotte Wang, Lanying Li, Jonathan Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

The COVID-19 was first declared by World Health Organization (WHO) as global pandemic on March 11th 2020. While most of COVID-related studies have focused on epidemiological perspective, the spatial analysis of disease outbreak is also important to provide perceptions of transmission rates. Therefore, this paper attempts to identify the potential factors contributing to the COVID-19 incidence rate at provincial-level in Canada. Three statistical regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM) were applied to 14 independent variables including socio-demographic, economic, weather, health and facilities related factors. The results indicated that three factors including median income, diabetes and unemployment significantly affected the COVID-19 rates in Canada. Among three global models, the SLM performed the best to explain the key variables and spatial variability of disease incidence with a R-2 value of 61%. However, in this study, the application of local regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) have not been considered and this could be a scope for the future research.

关键词： Statistical Regression COVID-19 Ordinary Least Squares spatial error model spatial Lag model Canada

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spatial analysis of subjective well-being in Japan

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JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022年第1期5卷 87-110页

作者： Li, Anqi Sato, Takaki Matsuda, Yasumasa Tohoku Univ Grad Sch Econ & Management Sendai Miyagi Japan Tohoku Univ Adv Inst Yotta Informat Sendai Miyagi Japan

This study investigates subjective well-being in Japan using a survey of 22,539 respondents in 46 prefectures in December 2019. We applied a Bayesian hierarchical model to the self-reported well-being respondents, supposing that well-being is decomposed into regional and individual factors. As a result, regional heteroscedasticity and individual factors are identified jointly, which clarifies the interesting features of Japanese subjective well-being. From the identified regional factors in prefectural levels, we find that Social Welfare Expenditure (SWE) per capita and Ratio of Forest Area (RFA) are positively related to subjective well-being. Some prefectures in Capital Region, which are at the bottom of happiness ranking, are correlated with lower SWE and FRA. In addition, coastal areas in Tohoku region damaged by the 2011 tsunami and nuclear plant accidents also have relatively lower subjective well-being. This finding suggests that residents in the regions have not recovered and require additional mental and physical public support.

关键词： Bayesian hierarchical model Great East Japan Earthquake and Tsunami Happiness survey Regional heteroscedasticity spatial error model Subjective well-being

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A spatial analysis of feed conversion ratio: a case study of broiler chickens in Musanze District, Northern Rwanda

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AFRICAN GEOGRAPHICAL REVIEW 2022年第3期41卷 397-410页

作者： Nisengwe, J. F. Regis Gill, Tom Ader, David Goertz, Hans Univ Tennessee Smith Int Ctr Forestry Wildlife & Fisheries FWF Inst Agr Knoxville TN 37996 USA

The poultry production model is an efficiently intensified small-scale production model. One of the best ways to measure the efficiency of poultry production is the feed conversion ratio (FCR). This study investigated factors that influence the FCR while considering spatial autocorrelations. A random sample of 320 broiler chicken farmers in Musanze district, northern Rwanda was collected and the spatial error model (SEM) was used for analysis. Results revealed aspatial autocorrelation and relationships between FCR and some factors. This study advances our understanding of sustainable intensification and contributes to the design of future investments and interventions in broiler chicken *** StartedCompletedRejected

关键词： spatial error model poultry production feed conversion ratio sustainable intensification

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Investigating the influence of transport accessibility on urban-rural income gaps

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APPLIED ECONOMICS 2024年第59期56卷 8650-8665页

作者： Li, Ling Zhou, Haidong Chen, Yuan Liu, Bingsheng Shen, Yinghua Zheng, Meiyu Tianjin Univ Coll Management & Econ Tianjin Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing Peoples R China

Transport accessibility is an essential element in the planning of transport policies and plays a crucial role in territorial cohesion, but the role of transport accessibility for urban-rural integration has been rarely studied. This article explores the relationship between transport accessibility and urban-rural income gaps through the spatial error model, using the weighted average travel time (WATT) indicator and the Theil index of urban-rural income. The results show that transport accessibility has a U-shaped influence on the urban-rural income gaps within cities of China. As transport accessibility gradually improves, the urban-rural income gaps within cities tend to first narrow and then widen. Meanwhile, their relationship can show heterogeneity across regions, with improving transport accessibility and narrowing the urban-rural income gaps in the eastern and western cities, respectively. Such a result suggests that it is essential to reinforce transport links between western cities and other cities with high economic potential, which could improve transport accessibility and further narrow the urban-rural income gaps of western cities. For transport planning, policymakers should consider transport accessibility and its socio-economic impact on urban-rural integration.

关键词： Transport accessibility urban-rural income gaps Theil index spatial error model C51 O18 R41

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How does spatial proximity to the high-speed railway system affect inter-city market segmentation in China: a spatial panel analysis

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EURASIAN GEOGRAPHY AND ECONOMICS 2022年第1期63卷 55-81页

作者： Ying He Lv Bingyang Yu, Danlin Chinese Acad Social Sci Inst Econ Beijing Peoples R China Renmin Univ China China Financial Policy Res Ctr Beijing Peoples R China Renmin Univ China Sch Sociol & Populat Studies Beijing 100872 Peoples R China Montclair State Univ Dept Earth & Environm Studies Montclair NJ 07043 USA

The construction of the high-speed railway (HSR) system in China has improved cities' interconnectedness. We contend that the improved interconnectedness will likely strengthen economic interaction among cities, particularly cross-border trade integration. Employing a spatial panel fixed effect error model with prefecture-level city data from 2008 to 2013, this paper evaluates how cities' proximity to the HSR system affects urban market integration in China. We find that cities' proximity to the HSR system exerts a positive influence on reducing market segmentation,ceteris paribus. This study for the first time assesses the impacts of cities' proximity to HSR on inter-city market integration in China. The study will facilitate proactive regional policy decisions related to the future development of HSR and other critical infrastructure investment and development. Results obtained from the study could be of broader impact on policies of marketization reform and development for Eurasian countries.

关键词： High-speed rail system spatial proximity measures inter-city market segmentation panel model spatial error model china

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Spatiotemporal evolution and influencing factors of urban industrial carbon emission efficiency in the Mid-Yangtze River urban agglomeration of China

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PHYSICS AND CHEMISTRY OF THE EARTH 2024年 135卷

作者： Lv, Tiangui Geng, Can Zhang, Xinmin Hu, Han Li, Zeying Zhao, Qiao Jiangxi Univ Finance & Econ Sch Publ Finance & Publ Adm Nanchang 330013 Peoples R China Jiangxi Univ Finance & Econ Sch Appl Econ Nanchang 330013 Peoples R China Hunan Univ Sch Publ Adm Changsha 410082 Peoples R China

Carbon emissions from energy consumption caused by industrial production have become an important factor in global warming, and China now has the highest global carbon emissions, with enormous pressure to reduce emissions. The Mid-Yangtze River urban agglomeration (MYRUA), which is an important region for China, also faces the contradiction of balancing environmental protection and economic production. Currently, there is little discussion on low-carbon pollution research in the MYRUA in China. Therefore, this study takes the MYRUA as the research object, uses a slack-based model (SBM) and exploratory spatial data analysis (ESDA) to explore the spatiotemporal evolution of the industrial carbon emission efficiency (ICEE) of the MYRUA in China from 2006 to 2019, and empirically analyses the factors influencing the ICEE based on a spatial econometric model. The results show that (1) in the period 2006 - 2019, the ICEE in the MYRUA region generally exhibited a " jagged " trend of decreasing, then increasing until finally decreasing again. spatially, ICEE generally exhibited a dynamic trend of being low throughout, with high -efficiency areas tending to move to the center and low-efficiency areas being scattered. (2) The global Moran ' s I coefficient for ICEE ranged between 0.002 and 0.213, with the highest Moran ' s I value and significant spatial correlation occurring in 2006, after which the value gradually decreased. On the local autocorrelation, the ICEE in 2006 exhibited an agglomeration effect and weakened after 2010. (3) In 2019, the level of industrialization (LI) and the level of external openness (LEO) in the MYRUA changed from nonsignificant to significant positive correlations, and the LEO had the largest impact. The energy consumption structure (ECS) was always significantly negatively correlated. The levels of social development (LSD) and environmental regulation (ER) changed from showing a significant positive correlation to showing a nonsignificant corr

关键词： Industrial carbon emission efficiency Slack-based model ESDA spatial error model Mid-Yangtze River urban agglomeration

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GIS-based spatial modelling of COVID-19 death incidence in Sao Paulo, Brazil

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ENVIRONMENT AND URBANIZATION 2021年第1期33卷 229-238页

作者： Urban, Rodrigo Custodio Kondo Nakada, Liane Yuri Pontifical Catholic Univ Campinas PUC Campinas Postgrad Program Urban Infrastruct Syst Ctr Exact Environm & Technol Sci Campinas Brazil Univ Campinas FEC Unicamp Dept Infrastruct & Environm Campinas Brazil

Seeking to understand the socio-spatial behaviour of the COVID-19 virus in the most impacted area in Brazil, five spatial regression models were analysed to assess the disease distribution in the affected territory. Results obtained using the Spearman correlation test provided evidence for the correlation between COVID-19 death incidence and social aspects such as population density, average people per household, and informal urban settlements. More importantly, all analysed models using four selected explanatory variables have proven to represent at least 85 per cent of reported deaths at the district level. Overall, our results have demonstrated that the geographically weighted regression (GWR) model best explains the spatial distribution of COVID-19 in the city of Sao Paulo, highlighting the spatial aspects of the data. spatial analysis has shown the spread of COVID-19 in areas with highly vulnerable populations. Our findings corroborate reports from the recent literature, pointing out the need for special attention in peripheral areas and informal settlements.

关键词： geographically weighted regression model informal urban settlements people per household population density SARS-CoV-2 Sao Paulo spatial error model

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Factors affecting land value in an Indian city

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JOURNAL OF PROPERTY RESEARCH 2022年第3期39卷 268-292页

作者： Binoy, B., V Naseer, M. A. Kumar, P. P. Anil Natl Inst Technol Calicut Dept Architecture & Planning Kozhikode India

Land valuation is the process of determining the value of the landed property, excluding all human-made improvements. This paper presents a comprehensive assessment and modelling of the land value and its determinants. Thiruvananthapuram, the capital city of Kerala state, is selected as the study area. Land value modelling applied in this study is divided into three stages. The first stage uses a standard hedonic price model for land value estimation and spatial autocorrelation diagnostics. The spatial error model is appropriate for variable selection and modelling based on Moran's I and Lagrange Multiplier test statistics in the second stage. The last stage of the study uses the spatial error model for land value analysis, and the results are compared with OLS regression. Proximity to major highways, disaster history, concentration of commercial establishments, and permissible FAR are the major factors affecting land value in the study area. Few other parameters like water frontage and noise pollution have a reverse relationship with similar studies in developed countries. The results indicate that factors influencing land value in Indian cities are different from the European and American cities. The study provides critical insights into the land price variation of an Indian city at a micro-level..

关键词： Ordinary least squares geographic information system Automatic Valuation Method hedonic modelling spatial error model

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Innovative use of spatial regression models to predict the effects of green infrastructure on land surface temperatures

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ENERGY AND BUILDINGS 2022年第0期254卷 111564-111564页

作者： Bartesaghi-Koc, Carlos Osmond, Paul Peters, Alan Univ Adelaide Sch Architecture & Built Environm ECMS Adelaide SA Australia Univ New South Wales UNSW Sch Built Environm Sydney NSW Australia

Understanding the complex and dynamic interplay and cumulative effects of green infrastructure (GI) and urban form on land surface temperatures (LST) is important to design and implement heat mitigation strategies. Past research has mostly employed two-dimensional (2D) indicators, simple correlations and conventional regression models using coarse-level analytical approaches that obviate spatial autocorre-lation effects. For the first time, this study applies a holistic approach to evaluate GI and urban settings as complex dynamic systems. The objectives of this paper are to: (1) develop novel 'spatially-based' predic-tive models that account for spatial dependencies;(2) implement a fine-scale analytical unit (<50 m) for a more precise and accurate analysis;(3) incorporate the 'multi-temporal' diurnal and seasonal variations into predictions;and (4) propose the novel combination of 2D and 3D morphological, compositional and configurational parameters of GI and urban form derived from very high resolution (VHR) remotely-sensed data (<2m), using Sydney metropolitan region as case study. Results show a strong spatial asso-ciation of LST at fine scale (<50 m) and spatial autocorrelation among residuals in traditional models. spatial error model (SEM) exhibits a superior performance over conventional multivariate regression, however, results presented significant heteroscedasticity caused by the large temperature variability in certain areas, although this problem was partially solved. Future studies should incorporate unmeasured factors related to material-specific properties (i.e. albedo, emissivity), and capture the thermal variation within urban areas by segmenting datasets into zones with relatively homogenous thermal and physical properties. Overall, ground imperviousness mostly defines the LST profile of a place, with a relative warming effect of 0.23 degrees C and 0.61 degrees C during day;and 0.18 degrees C and 0.41 degrees C at night per 10% of area incre-ment

关键词： Surface urban heat island Predictive modelling Mitigation Strategies Land Surface temperature spatial error model Multivariate Regression Ordinary Least Square Regressions

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