Spatial Interaction Models Research Articles

SIMVI reveals intrinsic and spatial-induced states in spatial omics data.

Spatial omics technologies enable the analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to capture spatial regulations for further biological discoveries. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. We derive theoretical support for SIMVI in disentangling intrinsic and spatial-induced variations. By this disentanglement, SIMVI enables estimation of spatial effects (SE) at a single-cell resolution, and opens up various opportunities for novel downstream analyses. To demonstrate the potential of SIMVI, we applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex, Slide-seqv2 mouse hippocampus, Slide-tags human tonsil, spatial multiome human melanoma, cohort-level CosMx melanoma). In all tested datasets, SIMVI effectively disentangles variations and infers accurate spatial effects compared with alternative methods. Moreover, on these datasets, SIMVI uniquely uncovers complex spatial regulations and dynamics of biological significance. In the human tonsil data, SIMVI illuminates the cyclical spatial dynamics of germinal center B cells during maturation. Applying SIMVI to both RNA and ATAC modalities of the multiome melanoma data reveals potential tumor epigenetic reprogramming states. Application of SIMVI on our newly-collected cohort-level CosMx melanoma dataset uncovers space-and-outcome-dependent macrophage states and the underlying cellular communication machinery in the tumor microenvironments.

REGIONAL FORCES AND URBAN FORMS: GROWTH DIRECTION

The possibility of influence that the regional system may have on the urban forms that cities tend to assume is discussed. The difference in scale involved in this vision, which ranges from the regional scale, passes through the municipal scale, and given this situation that presents the way in which urban form, city and region are approached today, it appears that the Studies lack greater connection and assessment of influences between different scales and impacts. This research is carried out through the proposal of a methodological guide, and the instrument developed to obtain results was the collection of data and construction of variables of interest, which can provide a useful tool to study the influence of spatial effects on aggregation of spatial interaction models and how much they can contribute in different ways to substantial achievements in spatial econometric studies, in addition to suggesting statistical tools that infer the value, intensity and probable hierarchy of average flows at certain levels of aggregation of real and estimated available variables.

An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow related research, fostering interdisciplinary perspectives and modes of thinking.

MCI모형을 이용한 중국 옌타이시 대형소매점 매출예측 및 상권영향분석

Recent research on GIS analysis, spatial interaction models, and site selection for large retail stores is actively underway in China both domestically and internationally. However, most studies tend to rely on secondary data provided by government agencies, neglecting research based on actual consumer survey data. Therefore, this study aimed to improve methods for analyzing commercial areas of large-scale retail stores in China by proposing the use of consumer data to enhance existing spatial interaction models. Specifically, by conducting surveys among consumers who actually visit large-scale retail stores, basic data such as store visit rates and visitor travel distances were collected to construct the MCI model. Through this approach, the study estimated the visit probability and market share of large-scale retail stores, and analyzed the impact of market environmental changes on commercial areas. Firstly, based on address data from 279 Chinese consumers, the study area was divided into 44 regions. These regions were combined with information on consumer shopping patterns and store details to construct a matrix of interactions between regions and large-scale retail stores. Secondly, important parameters in the MCI model, such as store attractiveness index and distance sensitivity, were estimated through OLS regression analysis. Findings revealed that the store type (regional large-scale retail, urban large-scale retail) did not significantly impact on consumers’ store preferences. Instead, customer visits exhibited an inverse relationship with distance and a direct relationship with store size. These estimated results were consistent with the theoretical perspectives of spatial interaction models. Thirdly, to examine how changes in the retail environment may affect retail businesses within commercial areas, the study focused on changes in sales and market share of surrounding retail businesses when a specific store expanded its area or entered a new region. The results showed that both expanding store area and entering new regions had negative impacts on other existing stores.

A conjugate method for simulating the dynamics of stochastic urban spatial network models

Urban networks are integral components of urban systems, contributing to their functioning and shaping the overall dynamics of urban areas. They are characterized by their complexity, interdependence, and dynamic nature. The construction, analysis and understanding of urban network models is therefore essential to address complex urban challenges, fostering sustainable development, and improving the overall quality of life in systems like cities and regions. In this work, we present and analyze the properties of a stochastic spatial-interaction model of urban structures. In addition, we devise a suitable time-stepping integrator that allows analyzing the evolution of this stochastic system at large times intervals, providing information of the dynamical behavior of the system in its equilibrium state. Numerical simulation studies are carried out to illustrate the practical effectiveness of the proposed approach.

Modeling tuberculosis transmission flow in China, 2010–2012

BackgroundChina has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China.Methods29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.ResultsTuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows.ConclusionsTuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Chorological abductive inferring: case studies of tracing spatial dissemination of COVID-19

Abstract COVID-19 did not disappear in the third year (2022) of the global pandemic. On the contrary, the number of infected people several times exceeded the highs of previous years, but the greater morbidity was not accompanied by a relatively comparable number of deaths. Some studies showed that the SARS-CoV-2 virus impact, e.g. in CEE EU countries, characterizes the seasonal intensity as temperatures fall or rise in relative humidity. All researchers agree that the number of COVID-19-infected people is only an estimate based on the volume of tests performed and that the true numbers are usually much higher. The implementation of spatial interaction modeling could potentially aid in the control of the COVID-19 pandemic due to the inherently spatial nature of its diffusion. The gravity models used in this investigation to simulate the regional spread of the COVID-19 epidemic are based methodologically on previous empirical studies. The proposed methodology uses techniques for modeling spatial interactions due to the epidemics described above, which are a direct result of the number of contacts between individuals. The COVID-19 pandemic can be studied regionally using spatial diffusion methods as well as population potential models (spatial interaction models) and visualized using geographic information system software. Empirical verification and geovisualizations are based on available recent population and pandemic statistics that are possible to acquire from national health services. Methodologically, this type of modeling and simulation aimed at reconstructing a factual situation can be defined as abductive chorological inferring.

Examining Food Access Equity by Integrating Grocery Store Pricing into Spatial Accessibility Measures

Accessibility to food stores offering nutritious and affordable options is critical to ensure urban residents lead healthy lives. However, the uneven distribution of diverse food options in U.S. cities has raised concerns about marginalized communities’ ability to obtain affordable and nutritious food. Transportation-related issues, like car-oriented urban form and sprawl, have created food deserts in cities, making it hard for marginalized communities to access affordable and healthy food. This lack of access has recently drawn the attention of urban planners to address food access-related equity issues. Spatial interaction models can help measure food access by examining how grocery store supply, demand, and proximity affect individuals’ access to necessary food. However, these measures do not always consider the pricing of food items, which can significantly affect food access for low-income individuals and families. We use a highly disaggregated measure tailored to measuring inequitable food accessibility by factoring grocery store pricing into various types of supply and demand. Then, using regional socio-demographic thresholds, we identify target population groups and perform a series of equity analyses using our food accessibility measure. Our results show that walking is more inequitable than driving to food stores and that African American and Hispanic minorities, the elderly, and carless households are more burdened than the reference groups (whites). Using our framework, we identify food deserts and provide spatial and empirical insights for policymakers and planners to address food insecurity and promote equitable access to healthy food.

Modelling and Forecasting Interregional Migration for Multiregional Population Projections

In this paper we focus on modelling and forecasting gross interregional migration in a way that can be embedded within multiregional population projections. We revisit, and apply, a family of spatial interaction models first formulated during the 1970s. The classic gravity model—in which migration is positively related to the populations of sending and receiving areas, but inversely related to various types of spatial friction associated with migrating between them—is a special case that is nested within this family of models. We investigate which member of the family of models gives the best fit when modelling five-year migration flows between the 66 Territorial Authorities (TAs) of Aotearoa New Zealand, using 2013 and 2018 census data. We find that predicting migration between two TAs can be improved by taking into account, firstly, an index of the ‘draw’ from all other TAs when modelling out-migration of any TA and, secondly, an index of the ‘competitiveness’ of a TA vis-à-vis all other TAs when modelling in-migration of any TA. We highlight the properties of the statistically-preferred model by simulating the impact on internal migration of an exogenous increase in Auckland’s population. In this model, such a population change affects not only migration flows from and to Auckland, but also migration between other TAs. The usefulness of this approach for population projections is assessed by forecasting the 2013–18 migration matrix by means of 2013 census data only. In this specific case, the model outperforms the classic gravity model in terms of forecasting gross migration, but not net migration.

Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

Addressing the fixed effects in gravity model based on higher-order origin-destination pairs

The gravity model is a classical spatial interaction model for estimating geographical flows between regions, such as trade and migration. A key to enhancing the model performance is addressing the fixed effects (FE) to capture unobserved group-based characteristics. Traditionally, this is done by grouping flows sharing the same origin or destination. However, it ignores the fact that FE of flows are directional and spatially heterogeneous, e.g., for the same origin (destination), the FE vary among destinations (origins). In this study, we propose a new method that addresses FE of flows based on higher-order origin-destination (OD) pairs. Specifically, we estimate FE by grouping flows that share the same OD dyad at an upper geographic level, e.g., all county-level migration flows from California to Florida are grouped to estimate their FE. We conducted two case studies using U.S. migration flows and for-hire vehicle trips in New York City. The results prove that our method can enhance the model performance. More importantly, our results demonstrate that the FE based on higher-order OD pairs can reveal the directionality, reciprocity, regionality, and temporal regularity of geographical flows. This insight helps us achieve a deeper understanding of the space-time mechanisms underlying spatial interaction phenomena.

Border effects for domestic travel in China during COVID-19 pandemic

While numerous studies have evaluated the role of international borders, few efforts have been made to assess the influence of domestic borders on travel during COVID-19. In China, the nearly three-year travel control, starting in early 2020, has caused considerable fluctuations in public perceptions and sentiments. Domestic travel behaviors were disrupted, and in some regions, even reshaped. This study evaluates border effects for domestic travel in China from 2019 to 2021. Utilizing short-term intercity travel flow data obtained from Baidu Map, we configured and performed a series of modified gravity models to reveal provincial border effects at three levels of detail. Several major conclusions can be drawn from the results: First, there is a consistent border effect on domestic travel within China, both pre-pandemic and during the pandemic. Second, border effects are directional, as they vary for inbound and outbound travel and depend on the origin and destination. Third, border effects exhibit clear spatial patterns that are associated with location and travel distance. Fourth, the impacts of COVID-19 on border effects are predominantly temporary; however, in some instances, they may be long-term. The results highlight the necessity of considering borders when estimating travel flows using spatial interaction models. This study also sheds light on related policymaking, as restricting or loosening travel controls at borders can have profound impacts on economic activities and disease transmission.

The Mesolevel Economy in Nineteenth-Century England and Wales: Applying Input-Output Accounting and Spatial Interaction Modelling to the Historical Study

This paper is offered as a contribution to understanding historical trade. The method of input-output modelling is utilised to evaluate the evolution of cities (and their associated regions) in England and Wales between 1851 and 1911. The construction of input-output accounts for each city region is based on “heroic” data assumptions, which enable the construction of a demonstration model illustrating a new iterative approach to historical analysis. In its current application, the model enables estimates to be made of mesolevel trade between cities, which enhances our analysis of urban evolution in this period.

Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent.

Network-entropy-based morphological polycentricity in 1851-1881 England and Wales

A system of cities is morphologically polycentric when its cities are similarly sized and evenly spaced across its territory. In this paper, we adapt established spatial interaction models and entropy-based measures of heterogeneity of weighted networks to the problem of measuring morphological polycentricity. We study the evolution of the morphological polycentricity of the system of English and Welsh towns from 1851 to 1881, a period characterised by rapid urbanisation and expansion of railways. Our approach enables us to account for morphological aspects of the system that are often neglected by existing measures of morphological polycentricity, such as the evolution of transport infrastructure and its impact on travel distances. We show that the trend towards a greater concentration of the population in fewer urban centres in England and Wales was accompanied by a more diverse network of connections and potential intercity interactions.

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

A novel 3D train–bridge interaction model for monorail system considering nonlinear wheel-track slipping behavior

Variable speed operation of the train cause easily the wheel-track slipping phenomenon, inducing strong nonlinear dynamic behavior of the suspended monorail train and bridge system (SMTBS), especially under an insufficient wheel-track friction coefficient. To investigate the coupled vibration features of the SMTBS under variable speed conditions, a novel 3D train–bridge interaction model for the monorail system considering nonlinear wheel-track slipping behavior is developed. Firstly, based on the D’Alembert principle, the vibration equations of the vehicle subsystem are derived by adequately considering the nonlinear interactive behavior among the vehicle components. Then, a high-efficiency modeling method for the large-scale bridge subsystem is proposed based on the component mode synthesis (CMS) method. The vehicle and bridge subsystems are coupled with a spatial wheel-track interaction model considering the nonlinear wheel-track sliding behavior. Furtherly, by a comprehensive comparison with the field test data, the effectiveness of the proposed method is verified, as well as the reasonable modal truncation frequencies of the bridge subsystem are determined. On this basis, the dynamics performances of the SMTBS are evaluated under different initial braking speeds and wheel-track interfacial adhesion conditions; besides, the nonlinear wheel-track slipping characteristics and their influences on the vehicle–bridge interaction are also revealed. The analysis results indicate that the proposed model is reliable for investigating the time-varying dynamic features of SMTBS under variable train speeds. Both the axle load transfer phenomenon and longitudinal slip of the driving tire would be easy to appear under the braking condition, which would significantly increase the longitudinal vehicle–bridge dynamic responses. To ensure a good vehicle–bridge dynamics performance, it is suggested that the wheel-track interfacial friction coefficient is larger than 0.35.

Exploring Multiscale Spatial Interactions: Multiscale Geographically Weighted Negative Binomial Regression

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns.

Exploring the distance decay in port hinterlands under port regionalization using truck GPS data

Ports are pivotal in the socioeconomic development of regions, countries, and cities as they serve as gateways for the transportation of goods between sea and land. Analyses of port hinterlands can enable port authorities and shippers to develop competitive strategies while assisting local authorities in identifying areas where inland transportation systems require improvement. In general, physical distance is a robust proxy for transportation costs. However, the role of distance in the context of port regionalization remains under debate, despite the spatial interaction model, with distance decay being commonly used to analyze the port–hinterland relationship. Doubt concerning the explanatory power of distance may act as an obstacle for port managers and local authorities in assessing the market share of ports, as well as in constructing planning agendas related to facility locations. This study examines the distance decay of port hinterlands using more than 0.18 billion points of heavy-duty truck GPS trajectory data from the Guangdong–Hong Kong–Macau Greater Bay Area. We develop an algorithm to compute heavy-duty truck flows and actual travel distance between the port and identified facility areas, and we apply the weighted least squares method to evaluate the explanatory power of six distance-decay models. Our findings reveal that the actual travel distance is a significant reduction factor in the port–hinterland relationship, while it follows the exponential model instead of a power function. This paper presents new evidence to reshape the role of distance in delimiting port hinterlands, providing port managers and local authorities with novel insights into the structure of port hinterlands in the context of port regionalization.

A Predictive Model for Cropland Transformation at the Regional Level: A Case Study of the Belgorod Oblast, European Russia

The problem of choosing the type of land use is now more relevant than ever. Against the backdrop of the growth of urbanized territories, the challenge is to preserve cropland, maintain the quality of soil resources, and find a balance between competing land uses. Forecasting and modeling changes in the area of cropland is a sought-after area of research against the backdrop of a growing shortage of fertile land and a threat to food security. In this study, on the example of one of the agriculturally most developed administrative regions of Russia (Belgorod Oblast), an approach to statistical modeling of agricultural land areas over the past 30 years is shown. Two approaches were used: statistical modeling of the dynamics of the total area of the study region’s cropland depending on the balance of other types of land and spatial interaction modeling of cropland in a key area. For the study region, administrative districts with positive and negative cropland dynamics were identified; the main types of land were revealed, due to which cropland is withdrawn, and a regression balance model was developed. It was revealed that the implementation of the planned regional programs to expand the development and conservation of meadow lands will reduce cropland by 3.07% or 83.2 thousand ha. On the example of one of the administrative districts with high rates of urbanization, the probability of cropland transformation into other types of land was estimated and a predictive spatial model of land use was developed. According to the forecast, about 6.2 thousand ha of cropland will turn into residential development land, and 2/3 of their area will be concentrated within 6 km from the borders of the regional capital city (Belgorod). The presented approach to forecasting the area of cropland and the threats of its reduction due to the need to replace other types of land is relevant for all agricultural regions and countries with developing urbanization processes.