Spatial Regression Methods Research Articles

Cultural and creative industries and local economic growth: exploring the urban channel

ABSTRACT The role of cultural and creative industries (CCIs) in driving regional growth is a topic of ongoing debate, with conflicting empirical findings. This study investigates CCIs’ impact on regional dynamics from two angles. First, it recognises heterogeneity within CCIs, including technological, symbolic and artistic forms, as well as varying levels of creativity across space. Second, it considers the influence of urbanisation on the growth potential of CCIs. Using an original regional-level database on CCIs employment, the research analyses European regions’ growth patterns in relation to different forms of CCIs. Panel and spatial regression methods are employed, and endogeneity is addressed by an instrumental variable approach. The study uncovers effects of CCIs in different urban contexts, adding insightful elements for the design of regional policies.

Can China’s belt and road initiative narrow the income gap between the Han people and ethnic minority residents? Evidence from a regression discontinuity design

The Belt and Road (B&R) policy is a region-oriented economic policy implemented by the Chinese government since 2010, involving the largest population and covering the largest area in the world. Whether it can significantly narrow the income gap between Han and ethnic minority residents has not been investigated in the literature. To avoid endogenous problems in the model, we used the spatial breakpoint regression method to investigate 2,037 county-level administrative regions in China from 2014 to 2021 and empirically evaluated the impact of the policy on the income gap between Han and ethnic minority residents. Results show that, compared to the counties without B&R policy, the average income gap between minority counties with B&R and Han has increased by 1.468–1.518 times, which was nearly 1.3 times the sample mean of survey data, indicating that the income gap has been widened significantly by B&R. However, the policy has a narrowing effect on the income gap between minorities and Han in the 20% sample with the widest income gap between minorities and Han. From the dynamic effect of years, with the effect of B&R, the income gap between minorities and Han is an inverted U-shaped curve, first increasing and then decreasing.

Association of transit-oriented development with transit use: Effects of gentrification and spatial autocorrelation

The empirical evidence in the existing literature primarily supports the positive association of transit-oriented development (TOD) with transit use. However, indirect relationships between TOD and transit use may be observed due to TOD-induced gentrification and spatial autocorrelation. This research examined the relationships between TOD, gentrification, and transit use with the consideration of spatial autocorrelation. The study observations were from the gentrifiable neighborhoods in Taipei City, Taiwan, and were analyzed using simultaneous equation models and spatial regression methods. Empirical results reveal three major findings: 1) A positive and direct association of TOD-ness with the modal share of transit substantially occurs and confirms the findings in the existing literature. 2) A negative and indirect association of TOD with transit use mediated by gentrification may occur under the moderation of rising car ownership. 3) A positive spatial autocorrelation of transit use substantially occurs between adjacent neighborhoods. These findings prompt local administrations to implement TOD policies containing or being accompanied by instruments inhibiting car ownership throughout urban areas rather than just along major transit corridors.

Artificial intelligence in land use prediction modeling: a review

<p><span lang="EN-US">This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.</span></p>

Impact of Spatial Characteristics on Gendered Retail Consumption in Seoul: A Gender-Sensitive Urban Planning Perspective

This study examines the impact of spatial characteristics on gendered retail consumption in Seoul, South Korea, providing empirical evidence for gender-sensitive urban planning. Gender-sensitive urban planning integrates gender perspectives into all stages of urban development, aiming to address the diverse needs and experiences of all genders spatially. While existing research has predominantly focused on gender differences in labor participation, this study shifts the focus to retail consumption, which is a critical aspect of daily life. Our research analyzes the spatial attributes of urban spaces at the neighborhood scale and their influence on aggregated retail consumption by gender. The aggregated retail sales by census output area (jipgyegu) represent the aggregated retail consumption. Utilizing spatial regression methods, this study identifies significant spatial autocorrelations and clustering patterns in retail sales data. The findings reveal that traditional markets, less-developed commercial areas, and specific retail sector (retailing, medical, and educational services) densities positively impact SMW (subtraction of men’s retail sales from women’s retail sales), while city center areas, developed commercial districts, special tourism zones, and specific retail sector (restaurants and entertainment) densities have negative impacts.

Penerapan Geographically Weighted Panel Regression dan Data Envelopment Analysis dalam Pemodelan Kemiskinan di Kalimantan Timur

Indonesia currently still needs to focus on achieving sustainable development goals agreed by all countries in the world. Indonesia presently ranks 82nd out of 163 nations in terms of SDG accomplishment, indicating that there is still plenty of potential for improvement. One of the goals that hasn't been accomplished is ‘no poverty’. Regarding the poverty cases, among all province in Indonesia, East Kalimantan is important to be analyzed, because Penajam Paser Utara and Kutai Kartanegara in East Kalimantan are scheduled to become Indonesia's next capital, Nusantara. The goal of this research is to investigate the variables that influence poverty in East Kalimantan and determine the effectiveness of poverty alleviation in the regencies/cities in East Kalimantan. This research used indicator data of poverty from 2019-2021 retrieved from Statistics Indonesia. This research use spatial panel data analysis regression method or Geographically Weighted Panel Regression (GWPR) and Data Envelopment Analysis (DEA). In GWPR model, this research compared adaptive gaussian, adaptive bisquare, adaptive exponential, fixed gaussian, fixed bisquare, and fixed exponential kernel. The findings of this investigation revealed that fixed exponential is the kernel that has lowest AIC and the highest adj-𝑅2. The variables that determine poverty of regencies/cities in East Kalimantan are expenditure per capita, life expectancy, and number of village with higher education facilities. Furthermore, according to DEA, only three cities were effective in addressing poverty: Mahakam Ulu, Paser, and Penajam Paser Utara.

In the Name of Mazu: The Use of Religion by China to Intervene in Taiwanese Elections

Abstract Foreign policy objectives can be achieved through religious institutions as a form of soft power. However, empirical evidence on this form of soft power is limited. In this study, we provide rigorous empirical evidence of electoral influence through the channel of religious institutions. To test the efficacy of this channel of influence, we study the Chinese government’s attempts to influence Taiwanese elections through religious institutions. By leveraging spatial regression methods, we demonstrate that Mazu temples influence electoral behavior through a localized process. The density of urban Mazu temples in Kaohsiung, Taiwan, which are subject to influence from China, is associated with an increase in vote share for the pro-China party in their neighborhoods. However, the influence on voters in urban and village areas is not homogeneous. In contrast to urban temples, the relationship between village Mazu temples and the electoral results is insignificant.

Impact of fiscal decentralisation on economic growth in Vietnam—A spatial regression approach

AbstractApproaching by spatial regression method, this study examines the impact of fiscal decentralisation on economic growth in 63 provinces/cities of Vietnam in the period 2010–2020. Findings suggest that, in general, revenue decentralisation and expenditure decentralisation not only have a positive impact on the economic growth of provinces/cities but also have spillover effects on other locations in improving GRDP per capita. The above findings provide some important policy implications for improving fiscal decentralisation to promote economic growth in Vietnam.

Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

Road crashes are a major cause of fatalities worldwide, posing significant challenges for road-safety experts in selecting appropriate crash-frequency estimation models. This study introduces localized safety performance functions (C-SPFs), which explore the spatial variation of crash frequency and the spatial correlation between dependent variables. The exploratory spatial regression method is employed to identify optimal spatial associations. The study further predicts crashes using geographically weighted Poisson regression (GWPR) and generalized Poisson regression. Results indicate that C-SPFs offer greater accuracy than do models calibrated solely on annual average daily traffic. Moreover, the proposed model is especially relevant for jurisdictions facing higher heavy-vehicle traffic and frequent crashes. The development of C-SPFs and the use of GWPR provide valuable tools for policymakers and road-safety experts in enhancing crash-frequency estimation accuracy. Implementing these techniques can aid in prioritizing safety measures and countermeasures, especially in regions with significant heavy-vehicle traffic and crash occurrences. Additionally, the integration of spatial-analysis techniques and localized models can lead to more effective transportation planning and targeted road-safety interventions, ultimately contributing to reducing the burden of road crashes on a global scale.

City-level decoupling between livestock and crop production and its effect on fertilizer usage: Evidence from China

China is one of the largest producers of livestock production and also with tremendous fertilizer consumption in crop production, regional decoupling between livestock and crop production often results in fertilizer overuse and environmental pollution. However, city-level coupling analysis between livestock and crop production is rare, and its impact on fertilizer usage also remains unclear. Here, we evaluated the nitrogen (N) nutrient supply from the livestock breeding sector and the N nutrient demand of cropland during the 2007–2020 period in a typical agricultural region in China. The city-level coupling degree of livestock and crop production and the effect on fertilizer usage were explored with spatial analysis and regression methods. Our results show that the province level has a relatively high coupling degree. However, significant spatial heterogeneity was found at the city level, especially in western Sichuan Province due to the highly unbalanced distribution of livestock and crop production, and this decoupling phenomenon may hinder fertilizer reduction. Furthermore, we reveal that technological development is not an effective way to achieve sustainable agriculture without other policy instruments, such as livestock spatial relocation, which must be considered when formulating crop-livestock integration policies. The findings expand city-level knowledge of the livestock-crop system and provide important implications for adjusting agricultural practices to realize sustainable agricultural development.

Human health risk assessment of Triclosan in water: spatial analysis of a drinking water system.

Triclosan (TCS) has been increased in the water during the COVID-19 pandemic because it cannot remove by conventional water treatment. In addition, it can accumulate in the human body over time through long-term exposure. Therefore, the occurrence of TCS in the water treatment plant (WTP) and tap water, and its human health risk assessment through tap water ingestion, dermal absorption, and inhalation routes in Isfahan, Iran, were investigated. Moreover, spatial regression methods were used for the prediction of water quality parameters, TCS concentration, and total hazard quotient (HQ). The average TCS concentration in the influent and effluent of WTP and tap water was 1.6, 1.4, and 0.4 μg/L, respectively. Conventional WTP has low efficiency in the removal of TCS (12.6%) from water. The average values of total HQ for males were 7.79×10-5, 4.97×10-4, and 4.97×10-5 and for females were 3.31×10-5, 2.11×10-4, and 2.11×10-5 based on RfDEPA, RfDMDH, and RfDRodricks, respectively that were in the low-risk levels (HQ<1). Furthermore, TCS concentration in tap water and the ingestion rate of drinking water had the highest effect on the risk of TCS exposure from tap water. The non-carcinogenic health risk of TCS in water was low. The results of this study may be useful for promoting WTP processes to remove emerging pollutants.

PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI SUMATERA UTARA MENGGUNAKAN REGRESI SPASIAL

The quality of life in North Sumatra currently still needs improvement, where poverty rates and human development are still lagging. These problems can be reflected in the community welfare indicator, namely the Human Development Index (IPM). One of these problems can be solved by knowing the factors that determine the Human Development Index in North Sumatra. The Human Development Index is suspected to contain elements of spatial dependency, therefore in this study the spatial regression method will be used with a spatially dependent effect model. The results of the analysis and discussion obtained are that the SAR model is the appropriate model in the case of the Human Development Index in North Sumatra. Predictor variables that significantly influence the Human Development Index in North Sumatra are Pure Enrollment Rate (APM) at Senior High School level , Poverty Rate , Open Unemployment Rate , and PDRB on the basis of price .

Modeling of Risk Factors of Childhood Stunting Cases in Malang Regency using Geographically Weighted Regression (GWR)

Background: Stunting is one of the fundamental problems in Indonesia's human development, with complex risk factors such as inadequate environmental sanitation. Objective: This study aims to conduct an analysis and mapping of spatially correlated sanitation risks on the prevalence of stunting among toddlers in Malang Regency, Indonesia. Methods: The field observations were conducted in 390 villages of urban and rural areas in Malang Regency. The secondary data of weighing toddlers in 2020 were collected from Malang Regency Health Office. The data included the identity of the residents and the history of the toddlers, while data from monthly program reports were collected from the Central Bureau of Statistics of Malang Regency. Furthermore, the study applied one and eight variables of Y and X. The Y variable was the number of stunting case toddlers, while X included the percentage of access to safe drinking water, percentage of access to permanent hygienic latrines, the topography of the region, percentage of Integrated Healthy Centre access, percentage of Exclusive Breastfeeding, Population density, ODF (Open Defecation Free) village and percentage of Integrated Healthy Centre Empowerment. Modeling the case of childhood stunting with the spatial regression method using Geographically Weighted Regression can be considered a sound approach. Results: The result showed that the distribution model of risk factors causing toddler stunting showed a spatial autocorrelation tendency with a clustered pattern in the area. The risk factors that were found to be spatially correlated with the incidence of stunting in children under five years of age in Malang district included access to proper drinking water (X1), access to latrines (X2), access to integrated health services (X3), exclusive breastfeeding coverage (X4), regional topography (X5), population density (X6), and empowerment of integrated health services (X8). However, the ODF (X7) did not show any correlation with stunting cases in the area. Conclusion: Spatial diversity and geographic variation affect the distribution pattern of risk factors causing stunting in Malang Regency. Therefore, stunting control with specific and sensitive interventions should be based on regional and local characteristics.

Spatial Effect Analysis of Total Financing and Factors Affecting Them at Banking in Indonesia

This study aims to formulate a pattern of spatial linkages between provinces in Indonesia based on the amount of bank financing through the influencing factors, namely third-party funds (DPK), Non-Performing Financing (NPF), MSME financing loans and GRDP. The data used is 2021 data. By using the Spatial Regression method with Moran' s I Test, it will look for relationships and inter-regional (spatial) relationships in the amount of bank financing based on the observed factors. From the results of data analysis, the Moran's I value obtained is 0.4439. Then based on the test results |2,2444|&gt;1.96, the decision making according to the existing hypothesis is H0 rejected. It can be concluded that there are spatial dependencies between provinces based on the amount of bank financing in each province according to the observed factors, namely DPK, NPF, MSME financing credit and GRDP. The expected output in implementing this developed strategy is to increase the amount of banking financing that is evenly distributed in each province in order to achieve the objectives from the economic aspect while taking into account the benefits for banking institutions. The improvement in the economic aspects in question includes increasing the usability of money, increasing the usability of goods, maintaining the country's economic stability and as a bridge to increase national income.

Exploration of the Role of Human Capital in China’s High-Quality Economic Development and Analysis of Its Spatial Characteristics

The launch of high-quality development has become an inevitable choice at this stage to cope with the new international situation, transform and improve the industrial structure, and pursue the Chinese dream. The aim of this study was to scientifically measure the stage of high-quality economic development and analyze the role of human capital in such development. In this paper, we construct an indicator system based on innovation, sharing, green economy, opening, and coordination. The spatial distribution of high-quality economic development is evaluated by using the genetic algorithm–projection pursuit model, and its impact is studied by using the spatial panel quantile regression method. The results show that the development level of regional economic quality is increasing annually, revealing a situation in which the east is leading, the center is rising, and the west is catching up. Among the factors affecting high-quality economic development, in this region, it has a positive inverted U-shaped influence on neighboring regions. Regarding quality improvement, the impact of human capital presents a U-shaped curve. In other words, it benefits from the catch-up effect of the lower-level provinces and the first mover advantage of the higher-level provinces. Based on this, corresponding countermeasures are put forward.

Analysis of Factors Influencing Illegal Waste Dumping Generation Using GIS Spatial Regression Methods

Illegal municipal waste dumping practices in developing countries may be impacted by many factors such as socioeconomic, demographic, availability of waste collection facilities, recycling sites, and spatial characteristics. This study uses spatial regression analysis to identify which factors primarily impact illegal waste dumping practices. For this purpose, 8 variables explain the data for the 177 subdistricts used in the spatial regression analysis. This study used ordinary least squares (OLS) and geographically weighted regression (GWR) methods to build a regression model of the factors identified. OLS analysis showed that only elevation and population density were found to become determinants of illegal waste dumping activity based on spatial regression methods. Elevation above sea level is positively correlated while population density is negatively correlated with the number of illegal dumping generations. GWR shows a better statistical value than OLS, where the significance of the adjusted R-square increased from 0.24 to 0.61. This study may help reduce the number of illegal waste dumping practices, especially in a metro city context.

Comparison of R and GeoDa Software in Case of Stunting Using Spatial Error Model

Gorontalo city is the capital of Gorontalo province which has a high incidence of stunting. This high incidence rate needs to get attention because stunting can further become one of the indicators of the low quality of human resources in Gorontalo. One method that can be used to analyze the factors that cause stunting is the spatial regression method, namely Spatial Error Model (SEM). SEM model can analyze used R and GeoDa software. The purpose of this study is to find out the factors that affect stunting in Gorontalo City and compare the results of the Spatial Error Model analysis based on the results of R and GeoDa software. The results showed that there are two variables that have a significant effect on stunting incidence, namely the variable number of Complete Basic Immunization (IDL) and the amount of proper sanitation. The R and GeoDa software comparison results showed there were several similar outputs i.e. LM test output, parameter estimation and R-square value, while the different outputs were Moran's I test output, Breusch-Pagan test, and AIC value. Although Moran's I test output and Breusch-Pagan’s test are different, but they produce the same conclusion. The AIC value produced by GeoDa is smaller than R software.

The Cop In Your Neighbor's Doorbell: Amazon Ring and the Spread of Participatory Mass Surveillance

Consumer surveillance products such as 'smart' doorbell cameras are an already-pervasive phenomenon in the U.S. These devices are marketed as personal and community security tools that allow users to answer their front door remotely, record "suspicious activity" captured by their cameras, and share reports with neighbors. The widespread use of doorbell cameras specifically, however, has created an opaque, wide-reaching surveillance network used by thousands of law enforcement agencies nationwide. The full breadth of this network and how users operate on such platforms is largely unknown. Amazon Ring, one of the largest manufacturers of smart doorbells, offers a companion social networking app to their physical doorbells called Ring Neighbors that allows camera owners to share video and text posts with other camera owners that live nearby. In this paper, we use data collected from public posts on Neighbors to create the first comprehensive map and analysis of smart doorbell camera use across the continental U.S. We use spatial regression methods to estimate the county-level predictors of Neighbors app usage nationally. We then use Los Angeles, one of the most active areas of Ring usage in the country, as a case study to investigate how different neighborhoods in a racially heterogeneous city use a platform like Ring. Using a structured topic analysis and experimental survey design, we show that users actively frame video subjects as criminal and suspicious, that the race of a neighborhood has a significant impact on posting rates, and provide some evidence that Neighbors may be used as a racial gatekeeping tool, particularly by white neighborhoods that border non-white areas in Los Angeles.

Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets

Abstract Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we evaluated the use of a spatial regression method to estimate the uncertainty in precipitation and temperature fields of existing deterministic gridded meteorological datasets. Taking the widely used North American Land Data Assimilation System 2 (NLDAS-2) precipitation and temperature dataset as an example, we used the deterministic NLDAS-2 values to generate ensemble estimates for daily precipitation, mean temperature, and the diurnal temperature range. Our method is a form of ensemble dressing. Nine variations were tested to assess the impacts of sampling density on the estimates of the mean and uncertainty, and one strategy was selected to generate 100 ensemble members at 1/8° and daily resolution for the period 1979–2019, termed as the Ensemble Dressing of NLDAS-2 (EDN2). Compared with an independent station-based ensemble dataset, the ensemble dressing method produces reasonable uncertainty patterns for precipitation and underestimates uncertainty for temperature. For precipitation, the uncertainty increases with the increase in daily accumulation. For temperature, the uncertainty is relatively small in the warm season and large in the cold season. This ensemble dressing method is applicable to other deterministic gridded meteorological datasets. The generated spatiotemporally varying uncertainty information could support applications such as land surface and hydrologic modeling, data assimilation, and forecasting, especially where application models are tied to a specific meteorological dataset.

Sustainable Land Use Model In Garang Watershed

Land cover change is a phenomenon that often occurs along with population growth and economic activity. This phenomenon not only occurs in urban areas but also penetrates into suburban areas, which incidentally have an important role in the water flow system. The same condition occurs in the Garang Watershed, Semarang City, where changes in land cover have begun to penetrate the water catchment area. Land cover change in the Garang watershed system will certainly have consequences for potential water-related disasters, and it will get worse due to climate change. Climate change in many places has triggered a water-related disaster or hydrometeorological disaster. Land cover change and climate change are conditions that cannot be avoided but can be managed so that they do not have a negative impact on the community. To minimize the adverse impacts of land cover changes and climate change is to provide land use directions that can reduce the threat of hydrometeorological disasters so that the sustainability of the ecosystem in the Garang watershed can be maintained. One approach that can be used in the framework of sustainable land use modeling is the spatial regression method. The results of the analysis show that the role of vegetation and water reservoirs can significantly reduce the potential hazard of hydrometeorological disasters.