Spatial Data Model Research Articles

Humanistic health of the physical living environment: The equity of property inheritance in China.

Equity in the inheritance culture of family property is an important component of humanistic health in modern living environments. The inheritance of property under Chinese traditional family culture is the material basis for the continuation of family and clan. This study demonstrates the equity component embedded in traditional family inheritance culture and further studies of the healthy human settlements environment. Based on the theory of "equal share for all sons" in ancient China and the "equity" and "justice" that are of modern significance, this paper analyzes the family division culture of individual traditional housing and the corresponding impact indices of family division equity. Taking Renhe Village, a typical local residential building of the middle and late Qing Dynasty as the research object, this study built a spatial syntax data model and 3D simulation technology for the simulation analysis of space and climate. The results show that Renhe Village meets the requirements of the equity evaluation system of housing property rights distribution in terms of the natural unit indicator (quantity, lighting, ventilation) and the overall spatial indicator (privacy, centrality, convenience). In other words, equity does not mean an absolute average share, but an equity culture formed after six evaluation indices under the subdivision of two indicators are balanced. Based on the above, an equity system model of housing property rights distribution was established, and the weight of the ancients' attention to the housing distribution standard was explored. It is further found that the ancients attached more importance to light among the natural unit indicators, and attached the most importance to centrality in the overall spatial indicators. These findings provide new ideas for understanding the equity of property inheritance under Chinese traditional family culture. They also provide quantifiable criteria for the distribution of modern rural housing and social security housing, and ultimately provide a reference for the humanistic public health of the modern living environment.

A spatial origin‐destination approach for the analysis of local tourism demand in Italy

AbstractThe article assesses the competitiveness of tourist destinations, while accounting for spatial features of tourism and information on both the origin and the destination of tourists. Using a dynamic spatial panel data model with common factors within the origin‐destination framework, it explores unilateral inbound tourism flows in 110 Italian regions from 23 European origin countries over the period 2004–2017. The estimation and test results confirm the need to consider this advanced spatial econometric model. In an empirical application measuring tourism resilience of regions based on this model, the results demonstrate the importance of promoting coordinated actions between neighbouring destinations to support the attractiveness of local clusters.

Compact representations of spatial hierarchical structures with support for topological queries

Among different spatial data models, the topological model for spatial regions explicitly represents common boundaries. This model pursues the efficiency of topology-related queries and the elimination of data redundancy. This paper proposes several space-efficient data structures to support access to the topological representation of two-dimensional regions that are organized in a multi-granular or hierarchical structure, such as the political and administrative partition of a country. In the context of these hierarchies, we focus on queries that search for inclusion, disjointness, and adjacency between regions. The proposed structures build upon compact planar graph embeddings, which show to have a good trade-off between space and time.

The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

ABSTRACT This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours’ infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease.

Space–time analyses of sediment composition reveals synchronized dynamics at all intertidal flats in the Dutch Wadden Sea

Intertidal mudflat systems are shaped by geological processes and an interplay of hydrodynamics, sediment availability and ecological processes. All around the world these systems are affected by relative sea level rise (RSLR), changing climate and by human activities such as sediment nourishments, dredging, hydrological engineering and bottom trawling. These kinds of perturbations cause changes in morphology and sediment composition which may cause shifts in the composition, spatial distribution and productivity of benthic communities. We analysed the spatial and temporal variability of the sediment grain size of more than 900 km2 intertidal flats in the Dutch Wadden Sea in the period 2009–2019. The large scale coverage was achieved by yearly grid sampling at more than 4000 stations. Spatial panel data models were used to analyse changes in median grain size and mud content between years and to estimate the effects of resuspension due to wind and the accumulation of silt during summer. We show that between years the sediments of the intertidal flats changed synchronously throughout the study area and that the flats became coarser during the period 2011–2015 and muddier again between 2015 and 2019. The system wide changes and the absence of clear local deviations leads to the hypothesis that a large scale factor like changing hydrodynamic regime (e.g. due to RSLR), variability in the composition of suspended sediment in the North Sea or changing microphytobenthic productivity were causally involved in the coarsening of intertidal flats. Our data and analysis provides a base for further scientific enquiry but longer time series on higher temporal resolution of both sediment data and the physical and ecological environment are required. Models simulating the environment may provide further insight into possible development of sediment composition of the intertidal flats of the Wadden Sea.

The spillover impacts of urbanization and energy usage on CO2 emissions: A regional analysis in the United States

This study endeavors to identify how urbanization and energy factors (use, prices, and policies) have influenced CO2 emission patterns per capita among the 48 U.S. states and the District of Columbia from 2000 to 2015. To examine interconnections between state-level emissions, three different spatial panel data models are estimated: the spatial autoregressive model, spatial error model, and the spatial Durbin model models. This study makes contributions to the literature by applying a spatial panel data approach and including policies on urbanization and renewable standards. Urbanization is found to have a statistically significant, negative direct effect on emissions, yet a negative spillover effect such that a 1.0% increase in state i's urbanization level leads to a 0.30% decrease in its own state's emissions, but a 0.012% increase in an adjacent state j's emissions. Although state-level emissions are increased by higher energy use and coal consumption, both energy prices and renewable portfolio standards decrease emissions both within their own and adjacent states.

Impact of high-speed railway construction on spatial patterns of regional economic development along the route: A case study of the Shanghai–Kunming high-speed railway

The Shanghai–Kunming high-speed railway spans the eastern, central, and western regions of China and is an important "horizontal" in the "eight vertical and eight horizontal" railway network of China. This paper estimates the direct impact of opening a high-speed railway on the spatial patterns of regional economic development along the route by integrating GIS technology, spatial panel data models, and the entropy method. The results show that the high-speed railway can significantly increase the economic aggregate and urbanization rates along the route and promote the upgrading of industrial structures, but it has a limited effect on the economic growth rate, showing a weak trend. In terms of spatial patterns, the high-speed railway significantly improves regional accessibility, the strength of economic ties, and economic potential along the route. The regional economy is characterized by weak spatial agglomeration and presents a trend of balanced development. In general, although the economic effect of HSR for the economically lagged regions is smaller in the short term than that in the economically and less economically developed regions, the high-speed railway has the greatest impact on regional economic development and spatial patterns in economically backward areas, thus the economically backward regions have a greater posterior advantage, which is conducive to balancing regional economies and narrowing regional differences.

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

Convergence of food consumption across Ukrainian regions: approach using spatial panel data models

Purpose. The article studies the convergence between the regions of Ukraine in terms of the basic foodstuff consumption per capita during 2000–2019, taking into account the effects of spatial interaction across regions. 
 Methodology / approach. The convergence analysis between regions of Ukraine is based on the concept of β-convergence which can be tested using spatial econometric models namely spatial autoregressive models and spatial error models. The need for considering spatial interaction can be explained by the fact that regions are characterized by constant interaction with each other. Therefore, region should not be considered as isolated objects in space in empirical research with usage of panel data. Ignoring the spatial interaction between regions and using standard evaluation procedures can reduce the reliability and validity of the obtained results to some extent.
 Results. The results of our calculation confirm the process of β-convergence of average per capita consumption of all food groups, which means that food consumption in regions with an initial low level of consumption is growing faster than in regions with high initial levels of consumption. Also, as part of the use of spatial econometric models the convergence process was determined to be influenced by spatial interaction between regions while the influence of neighbouring regions has a positive effect on food consumption in particular region.
 Originality / scientific novelty. The article further develops the main ideas of modeling interregional differentiation based on convergence theory and for the first time, spatial econometric models were used to estimate β-convergence of Ukrainian regions by the levels of consumption of basic foodstuffs. 
 Practical value / implications. The approach proposed by the authors and the obtained results can be used both by state authorities on agrarian policy and food issues, and by enterprises of the agricultural sector in the analysis and forecasting of trends in the consumption of basic foodstuffs at the regional level; when planning the production, processing and delivery of agricultural products, when planning state or regional trade policy in the field of food. At the same time, the inclusion of spatial effects in the model of evaluating convergence will allow policymakers to take into account the geographical features of the convergence process and, accordingly, make more informed decisions to reduce the differentiation of regions of Ukraine by the levels of consumption of basic foodstuffs.

Identifying latent group structures in spatial dynamic panels

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group membership are unknown. To identify the latent group structures, we first adopt the GMM to obtain the preliminary unconstrained estimates of the slope coefficients. Then we apply the sequential binary segmentation algorithm (SBSA) of Wang and Su (2021) to these estimates and obtain the clusters. A BIC-type information criterion is proposed to choose the number of latent groups consistently. The asymptotic analysis shows that this method can identify the true group structure consistently, and the post-classification estimators enjoy the oracle property. Monte Carlo simulations demonstrate that our method has good finite sample performance. Finally, we apply our approach to the US housing market and identify two latent Metropolitan Statistical Areas groups.

Spatio-Temporal Analysis of Game Harvests in Sweden

The benefits and costs of wildlife are contingent on the spatial overlap of animal populations with economic and recreational human activities. By using a production function approach with dynamic spatial panel data models, we analyze the effects of human hunting and carnivore predation pressure on the value of ungulate game harvests. The results show evidence of dynamic spatial dependence in the harvests of roe deer and wild boar, but not in those of moose, which is likely explained by the presence of harvesting quotas for the latter. Results suggest the impact of lynx on roe deer harvesting values is reduced by 75% when spatial effects are taken into account. The spatial analysis confirms that policymakers’ aim to reduce wild boar populations through increased hunting has been successful, an effect that was only visible when considering spatial effects.

Statistical Deep Learning for Spatial and Spatiotemporal Data

Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity.

Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.

Bayesian clustering of spatial functional data with application to a human mobility study during COVID-19

The coronavirus (COVID-19) global pandemic has made a significant impact on people’s social activities. Cell phone mobility data provide unique and rich information on studying this impact. The motivating dataset of this study is the daily leaving-home index data at Harris County in Texas provided by SafeGraph. To study changes in daily leaving-home index and how they relate to public policy and sociodemographic variables, we propose a new Bayesian wavelet model for modeling and clustering spatial functional data, where domain partitioning is achieved by operating on the spanning trees. The resulting clusters can have arbitrary shapes and are spatially contiguous in the input domain. An efficient tailored reversible jump Markov chain Monte Carlo algorithm is proposed to implement the model. The method is applied to the spatial functional data of the daily percentages of people who left home. We focus on the time period covering both lockdown and phased reopening in Texas during the COVID-19 pandemic and study the changing behaviors of those functional curves. By linking the clustering results with the sociodemographic information, we identify several covariates of census blocks that have a noticeable impact on the clustering patterns of people’s mobility behaviors.

The driving influence of multidimensional urbanization on green total factor productivity in China: evidence from spatiotemporal analysis.

China is experiencing a swift transformation from a rural to an urban society. This societal transition is the result of a country's ambition toward economic development. Rapid urbanization impacts on human wellbeing, environment, and infrastructure. Unlike many cases of urban extension, China's urbanization has led to increase in environmental issues, rather than to contain, and posed serious challenges to achieve sustainable development. To date, nevertheless, there is a little empirical enquiry on the spatiotemporal effects of multidimensional urbanization on green total factor productivity. Taking this into account, this article constructs a spatial panel data model based on 283 Chinese cities during 2006-2019. The results showed that the three types of urbanization (i.e., population, land, and economic) have positive effect on green total factor productivity (GTFP). Population urbanization has indirect positive effect on GTFP. Local land urbanization inhibits while surrounding land urbanization promotes GTFP. Economic urbanization has positive direct effect on GTFP. Considering regional heterogeneity, population and economic urbanization have positive effect on GTFP in eastern region. In central region, land urbanization significantly promotes GTFP, while economic urbanization has significant negative effect on GTFP. In the western region, the three types of urbanization are not conducive to GTFP. Considering sized heterogeneity, population and economic types of urbanization have significant negative effect on GTFP in small- and medium-sized cities. In large cities, the three types of urbanization have significant positive effects on GTFP. Considering temporal heterogeneity, the three types of urbanization significantly promote GTFP before 2014, but economic urbanization deteriorates GTFP after 2014. Our results recommend loosening Hukou system, improving land use efficiency, and developing technology and knowledge-intensive industries.

An individual tree-based model for estimating regional and temporal carbon storage of Abies chensiensis forest ecosystem in the Qinling Mountains, China

Accurate estimation of carbon storage of forest ecosystem has significant implication for mitigating global warming. In order to get accurate estimation of carbon storage of forest ecosystem at the regional scale and temporal dynamics in the Qinling Mountains, this study established a dynamic estimation model (FCM model) for forest carbon storage at tree stand scale according to the data of forest growth and degradation characteristics of Aibes chensiensis, and constructed a regional and temporal estimation model (geo-FCM model) based on FCM model and Aibes chensiensis spatial distribution data from 3S technology. The results showed that carbon density of Aibes chensiensis forest at tree age of 80 years from FCM model were 245.56 t ha−1, which was similar to the measured carbon densities from field investigation. The simulation process of the FCM model could well reflect the growth characteristic of Aibes chensiensis. The simulated result of geo-FCM model showed carbon storage of Abies chensiensis forest in study region were 5.23 × 105 t in year 2018 with the average carbon density of 222.26 t ha−1 and increased to 5.60 × 105 t in year 2028 along with tree growth. Its carbon storage increased 7.11% compared to the state of year 2018. The constructed model provided a robust method to estimate forest carbon storage of different tree species at different age stages at the regional scale, which can provide an important reference for forest management and selection of plantation tree species.

A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes

Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge we propose a method based on statistics and machine learning for the spatial prediction of extreme wildfire frequencies and sizes. This method is tailored to handle large datasets, including missing observations. Our approach relies on a four-stage high-dimensional bivariate sparse spatial model for zero-inflated data, which is developed using stochastic partial differential equations(SPDE). In Stage 1, the observations are categorized in zero/nonzero categories and are modeled using a two-layered hierarchical Bayesian sparse spatial model to estimate the probabilities of these two categories. In Stage 2, before modeling the positive observations using spatially-varying coefficients, smoothed parameter surfaces are obtained from empirical estimates using fixed rank kriging. This approximate Bayesian method inference was employed to avoid the high computational burden of large spatial data modeling using spatially-varying coefficients. In Stage 3, the standardized log-transformed positive observations from the second stage are further modeled using a sparse bivariate spatial Gaussian process. The Gaussian distribution assumption for wildfire counts developed in the third stage is computationally effective but erroneous. Thus in Stage 4, the predicted values are rectified using Random Forests. The posterior inference is drawn for Stages 1 and 3 using Markov chain Monte Carlo (MCMC) sampling. A cross-validation scheme is then created for the artificially generated gaps, and the EVA 2021 prediction scores of the proposed model are compared to those obtained using certain natural competitors.

New Approaches to Modelling Wilderness Quality in Iceland

Much of Europe’s remaining wilderness areas are found in Iceland, yet few are formally protected despite ongoing threats from renewable energy exploitation and 4 × 4 usage. Robust and repeatable approaches are required to map wilderness landscape qualities in support of developing policy on designations that meet international standards. We present an approach to mapping wilderness that is based on internationally recognised methods and customised to suit the unique nature of Icelandic landscapes. We use spatially explicit models of wilderness attributes that measure human impact from vehicular access, land use and visible human features rather than relying on proxy measures such as buffer zones. Seventeen wilderness areas are identified across the Central Highlands and surrounding areas, totalling some 28,470 km2. These are compared to existing mapping projects. The character of these areas is described using additional spatial data models on openness, ruggedness and accessibility from settlements, together with information on mobile phone coverage and grazing patterns. This is the most detailed mapping of wilderness in Iceland to date and an important step towards the formal definition of boundaries of wilderness areas meeting IUCN Category 1b and Wild Europe Working Definition in Iceland.

A REVIEW OF 3D SPATIAL DATA MODELS FOR SURFACE AND SUBSURFACE INTEGRATION OF SPATIAL OBJECTS

Abstract. Three dimensional (3D) spatial data models have been known as a geographic information system platform for representing the dimensionality of spatial objects with respect to the real world. They can be used to mimmick the real-world objects above the ground, on the ground and underground. A 3D spatial data model that can integrate surface and subsurface elements will have a significant impact on engineering, spatial and urban planning, and the built environment. Such an impact includes good infrastructure development, proper planning, result-oriented installation of utilities, location-specific excavation of existing utilities, and cost-effective economy resulting from minimal damage of infrastructures. This article explores and documents the state-of-the-art with respect to 3D spatial data models specifically for the integration of surface and subsurface objects based on a thorough review of the literature in academic articles, technical reports, and web-based materials. To better understand the scope, this paper reviews the different 3D spatial modelling approach for surface spatial objects, subsurface spatial objects, and with keen focus on surface and subsurface spatial objects integration. The Open Geospatial Consortium (OGC) has played an important role in data integration and has a core space in 3D spatial data model evolution. One of the recent data models for such task is CityJSON due to its simplicity in data storage, visualization, manipulation, and data update compared to CityGML. The parameters influencing the choice of models can be attributed to the availability of data structure, applicability, exchange format and relevance. Based on the range of 3D spatial data model reviewed, a unified model is expedient for integration of surface and subsurface spatial objects.

Use of spatial panel-data models to investigate factors related to incidence of end-stage renal disease: a nationwide longitudinal study in Taiwan

BackgroundThe assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD).MethodsA longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient’s residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors.ResultsIn total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most “social environmental factors,” except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate.ConclusionFuture comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.