Spatial Interaction Models Research Articles

How does pasture size alter plant–herbivore interactions among grazing cattle?

AbstractGrassland management impacts ecosystem functioning and is critical for livestock production and biodiversity conservation. Decreasing the stocking rate usually benefits biodiversity but also decreases production per unit area. Here, we assessed the combined effects of cattle herd and pasture size on sward structure, diet quality and daily grazing time while maintaining a constant stocking rate. The experiment was conducted over four successive years in two blocks of a species‐rich upland grassland that was continuously grazed for 5 months between May and October. Each block consisted of one 3‐ha pasture that was grazed by a group of three heifers, one 9‐ha pasture grazed by nine heifers, and one 27‐ha pasture grazed by 27 heifers. The grazed patches were discriminated using a mathematical model that for each point gave a probability of being grazed based on sward height and spatial dependence. Heifers created fewer patches and had less control over sward height in the smallest (3‐ha) plots, while no difference was found between the 9‐ and 27‐ha plots. The small heifer groups also had the shortest daily grazing time. These observations point out a risk that animal impacts on sward structure could be partly missed in grazing experiments conducted with very small groups. Estimated diet quality was unaffected by pasture size. Patch stability was frequent between two successive years but we observed a decrease in interannual patch stability as time intervals increase. Such observations are useful for calibrating spatial interaction models combining foraging behaviour and vegetation dynamics rules and enhance their predictive ability.

Predictive limitations of spatial interaction models: a non-Gaussian analysis

We present a method to compare spatial interaction models against data based on well known statistical measures that are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the US Census 2000. We find that the radiation model performs significantly worse than an appropriately chosen simple gravity model. Various conclusions are made regarding the development and use of spatial interaction models, including: that spatial interaction models fit badly to data in an absolute sense, that therefore the risk of over-fitting is small and adding additional fitted parameters improves the predictive power of models, and that appropriate choices of input data can improve model fit.

Spatial-temporal Evolution and Determinants of the Belt and Road Initiative: A Maximum Entropy Gravity Model Approach

The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative (BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy (MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.

Modeling Commuting Flows via Fixed Components

The basic, reasonable hypothesis underlying this paper is that many individuals are comfortable with their current combination of job and residential location and have no intention of changing job location or moving from where they live. This causes autocorrelation in the time series of commuting flows and provides a rationale for introducing fixed components in the trip distribution model. The fixed components are assumed to be constant in time and separated from the observed trip distribution. We next consider the residuals and fit the model by a constrained entropy-maximization procedure. Based on commuting data from Stockholm County in Sweden, the fixed component spatial interaction model is demonstrated to lead to a substantial improvement in goodness of fit compared with conventional spatial interaction models. The identification of fixed components leads to significantly lower estimates of the distance deterrence parameter, and fixed components are further argued to be potentially useful in a prediction perspective. The distance deterrence parameter in the fixed component model reflects the spatial interaction of workers who are actually considering changes in their residential and/or job location. We also compare alternative measures of spatial separation and discuss differences in commuting behaviour by gender.

Potential and Pitfalls of Big Transport Data for Spatial Interaction Models of Urban Mobility

Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and use shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling, it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this article, the usefulness of a recently available big transport data set—the New York City taxi trip data—is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting data set. In addition, the high temporal resolution of the taxi data provides an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high-frequency temporal dynamics of diverse urban activities.

Measuring Network Configuration of the Yangtze River Middle Reaches Urban Agglomeration: Based on Modified Radiation Model

The objective of this study is to develop a framework for re-examining and re-defining the classical concepts of spatial interaction and reorganization in the urban system. We introduce a modified radiation model for spatial interactions, coupled with migration big data, transport accessibility algorithm, and city competitiveness assessment for efficient distribution of the inter-city flow through the network. The Yangtze River Middle Reaches (YRMR) urban agglomeration (UA) is chosen as the case study region to systematically identify and measure its spatial configuration and to gain insights for other UAs’ sustainable development in China. The results are also compared with those computed by the classical gravity model to systematically discuss the applicability of spatial interaction laws and models, and related practical policies for regional sustainable development are discussed based on the findings as well. The conclusions are highlighted below: 1) Combining with the ‘city network paradigm’ and ‘central place theory’ can better express the spatial configurations of city systems in the context of ‘space of flows’; 2) The results validate the potentialities of a multi-analysis framework to assess the spatial configurations of city network based on the improved radiation model and network analysis tools; 3) The applications of spatial interaction models should be considered according to the specific geographical entity and its spatial scale.

Geographical or relational: What drives technology-specific R&D collaboration networks?

R&D collaboration networks enable rapid access to global sources of knowledge, especially in strongly knowledge-based and technology-driven industries. However, technological idiosyncrasies require a refined picture, in particular, when explaining the interplay between geographical and relational effects driving the constitution and dynamics of R&D collaboration networks. We employ a spatial interaction modelling approach to estimate how geographical separation and network structural effects influence technology-specific R&D collaborations between European regions. The results underline both the significance of geographical barriers and network structural effects and confirm that specific network connectivity is able to compensate for geographical barriers—throughout all technologies investigated, although the effects differ in magnitude. However, when two regions are dissimilar in their network centrality, the potential to reduce negative geographical effects is relatively lower.

BiFlowLISA: Measuring spatial association for bivariate flow data

Spatial flow data are often used to represent spatial interaction phenomena such as daily commuting trips, human or animal migrations, and the exchanges of commodities, capital, or even information between regions. With the increasingly available large volume of flow data in fine spatiotemporal resolution, exploratory spatial data analysis (ESDA) has become more important than ever to gain understanding of the data and the story behind it. A major group of flow-related ESDA methods focus on measuring spatial associations, which proves useful in improving the prediction power and interpretability of spatial interaction model (SIM), as well as in identifying local clusters and outliers of flow events. This paper introduces a new spatial statistical method called BiFlowLISA—a local indicator of spatial association of bivariate flow data. BiFlowLISA evaluates the association between two types of flows in close proximity, in other words, how the value of type-I flows associate with the value of nearby type-II flows. We develop BiFlowLISA by extending the local bivariate Moran's I to the flow context. We also put forth its global version to measure the global patterns, and another variant of BiFlowLISA to measure both spatial and in-situ correlations at the same time. Several flow-specific issues are discussed and solved, including flow neighbor definition, OD matrix sparsity, and conditional permutation. We experiment with synthetic datasets to verify its functionality and to summarize its characteristics. A case study of taxi and ride-hailing services in New York City demonstrates its usefulness in the comparative analysis of the spatial patterns of two types of travel flows. More applications of BiFlowLISA await to be explored in the future.

The Push and Pull Factors Contributing Towards Asylum Migration from Developing Countries to Developed Countries Since 2000

AbstractIn the early 2000s, asylum seekers went primarily to destinations their countries had historical relationships, including former colonial ties, common languages and ethnic networks. Since the mid‐2000s, there has been a shift towards asylum seekers arriving in destinations without such relationships. In this study, we apply spatial interaction models to understand the factors contributing to recent asylum migration from developing countries to developed countries from 2001 to 2015. We find that the push factors of political terror, civil rights violation and poverty do not result in increased asylum migration to developed countries. Instead, they act to reduce the number of asylum seekers. We also find that interactions between a range of push factors are important for understanding the movements and that immigration policies can have considerable effects on the destination choices of asylum seekers.

Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It

This manuscript develops a theoretical spatial interaction model using the entropy approach to relax the assumption of the deterministic utility function. The spatial healthcare accessibility improves as the demand for healthcare increases or the opportunity cost of traveling to and from healthcare providers decreases. The empirical application used different spatial econometric techniques and multilevel modeling to evaluate the spatial distribution of existing hospitals in Texas and their social and economic correlates. To control for spatial autocorrelation, spatial autoregressive regression models were estimated, and geographically weighted regression models examined potential spatial non-stationarity. The multilevel modeling controlled for spatial autocorrelation and also allowed local variation and spatial non-stationarity. The empirical analysis showed that healthcare accessibility was not stationary in Texas in 2015, with areas of poor accessibility in rural and peripheral areas in Texas, when using hospitals’ location and county data. The model of spatial interaction applied to healthcare accessibility can be used to evaluate policies aiming at the provision of health services, such as closures of hospitals and capacity increases.

Modeling human migration across spatial scales in Colombia.

Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.

Estimating road transport costs between and within European Union regions

Transport costs are a crucial element of applied spatial models such as spatial macro-economic models or interaction models for e.g. trade, migration or commuting. However, good estimates of these costs are not available at lower levels of spatial aggregation. In applied work, the distance between two regions is often approximated by the distance between the largest city in each region. For costs within regions, researchers often resort to crude ad-hoc approximations relating internal distance to the area of the region assuming a uniform internal distribution. This paper improves on this by considering averages of transport costs calculated between extremely large random samples of centroids which are drawn from a population grid. This allows calculating distances, travel times and transport costs both between and within regions, while taking into account the unequal distribution of population within the regions. The use of a detailed road network and many auxiliary datasets allows performing policy analysis. We assess the impact on transport cost of an increase in fuel prices, and find that it has a relatively high effect on transport costs in Eastern Europe. We evaluate transport infrastructure investment of the European Cohesion Policy program 2014–2020. The largest decreases in transport costs are found in targeted regions in Eastern Europe while the effect is much smaller in targeted regions in Southern Europe, suggesting decreasing returns to further transport infrastructure in these regions. We find significant inter-regional spillovers to regions directly bordering the regions targeted by the policy in Eastern Europe, such as in Germany and Austria, but also Finland and Northern Italy. The positive spillovers to EU regions in Western Europe are quite small.

The influence of distance and quality on utilisation of birthing services at health facilities in Eastern Region, Ghana

ObjectivesSkilled birth attendance is the single most important intervention to reduce maternal mortality. However, studies have not used routinely collected health service birth data at named health facilities to understand...

A new method using medians to calibrate single-parameter spatial interaction models

I present a method for calibrating the impedance parameter of a gravity spatial interaction model using only the median travel time as a measure of observed traveler behavior. Complete information about the spatial structure of origins, destinations, and travel times between origins and destinations is also required. Using Monte Carlo simulation techniques on stylized cities, I attempt to recover true (a priori known) impedance values with this method for a range of impedance values for both negative exponential and power impedance functions. The results are compared with estimates obtained by other fast methods. The proposed method proves to provide a fairly accurate estimate of the impedance parameter, with a mean percent error typically below 20% and often below 10% for common impedance values. The proposed method is an improvement over existing calibration methods in several respects. First, it allows for the estimation of the impedance parameter directly without lengthy iterative calculations. Second, because it only requires median travel times, it can be calibrated with smaller samples (n~200), allowing the construction of gravity models for specific modes and/or travel purposes. And third, the method does not require expensive travel demand software and so can be implemented more widely in practice.

An Agent-Based Model for Simulating Inter-Settlement Trade in Past Societies

Social and computational archaeology focuses largely on the study of past societies and the evolution of human behaviour. At the same time, agent-based models (ABMs) allow the efficient modeling of human agency, and the quantitative representation and exploration of specific properties and patterns in archaeological information. In this work we put forward a novel agent-based trading model, for simulating the exchange and distribution of resources across settlements in past societies. The model is part of a broader ABM populated with autonomous, utility-seeking agents corresponding to households; with the ability to employ any spatial interaction model of choice. As such, it allows the study of the settlements’ trading ability and power, given their geo-location and their position within the trading network, and the structural properties of the network itself. As a case study we use the Minoan society during the Bronze Age, in the wider area of Knossos on the island of Crete, Greece. We instantiate two well-known spatial interaction sub-models, XTENT and Gravity, and conduct a systematic evaluation of the dynamic trading network that is formed over time. Our simulations assess the sustainability of the artificial Minoan society in terms of population size, number and distribution of agent communities, with respect to the available archaeological data and spatial interaction model employed; and, further, evaluate the resulting trading network’s structure (centrality, clustering, etc.) and how it affects inter-settlement organization, providing in the process insights and support for archaeological hypotheses on the settlement organization in place at the time. Our results show that when the trading network is modeled using Gravity, which focuses on the settlements' importance rather than proximity to each other, settlement numbers’ evolution patterns emerge that are similar to the ones that exist in the archaeological record. It can also be inferred by our simulations that a rather dense trading network, without a strict settlement hierarchy, could have emerged during the Late Minoan period, after the Theran volcanic eruption, a well documented historic catastrophic event. Moreover, it appears that the trading network's structure and interaction patterns are reversed after the Theran eruption, when compared to those in effect in earlier periods.

A Multiscale Flow-Focused Geographically Weighted Regression Modelling Approach and Its Application for Transport Flows on Expressways

Scale is a fundamental geographical concept and its role in different geographical contexts has been widely documented. The increasing availability of transport mobility data, in the form of big datasets, enables further incorporation of spatial dependencies and non-stationarity into spatial interaction modeling of transport flows. In this paper a newly developed multiscale flow-focused geographically weighted regression (MFGWR) approach has been applied, in addition to global and local Moran I indices of flow data, to model multiscale socio-economic determinants of regional transport flows between counties across the Jiangsu Province in China. The results have confirmed the power of local Moran I of flow data for identifying urban agglomerations and the effectiveness of MFGWR in exploring multiscale processes of spatial interactions. A comparison between MFGWR and flow-focused geographically weighted regression (FGWR) showed that the MFGWR approach can better interpret the heterogeneous processes of spatial interaction.

Place-Based Factors and the Performance of Farm-Level Entrepreneurship: A Spatial Interaction Model of Agritourism in the U.S.

We apply farm-level data to a two-stage model to explore how three different theories of comparative advantage influence the propensity of a farm or ranch to adopt an agritourism enterprise and the level of economic activity tied to that enterprise. Findings suggest that a county’s entrepreneurial spirit and scenic byways increase the propensity to adopt agritourism, but natural endowments and agglomeration are the primary drivers of agritourism economic activity. Results should assist policy makers as well as rural economic development researchers in leveraging community strengths to increase economic activity in the agritourism industry and its surrounding rural economies.

The contribution of Sir Alan Wilson to spatial interaction and transport modelling

ABSTRACTIn a series of papers in the mid-1960s and early 70s, building on his key 1967 paper, Alan Wilson made a series of fundamental contributions to the specification and application of land-use transport models. In this paper the basis of his entropy-maximizing approach to spatial distribution models is outlined with a demonstration that the methods have applications very much beyond its transport roots. The entropy maximization method laid the foundations for the development of a range of multinomial logit share models. He expanded the core transport ideas to the whole range of comprehensive models, initially building on, and extending, Lowry's iconic Pittsburgh model. The factors that have sustained the longevity of Wilson's models are explored and articulated.

Alan Wilson – contributions to research on population and migration

ABSTRACTThis article reviews Alan Wilson's research on population and migration in the 1970s and the 2010s, which supplements his principal contribution - mathematical modelling of urban and regional systems. In the 1970s, drawing on input-output models of economies and working with Philip Rees, Wilson established the accounting basis for Andrei Rogers' multi-regional projection model, adding international migration. Innovative methods were developed to complete demographic accounts, where there were data gaps. In the 2010s, working with Adam Dennett, Wilson systematized methods for estimating migration flows between regions in Europe, employing his family of spatial interaction models. The key aim of both research strands was to ensure that no information was ignored to ensure consistency in population and migration models. The influence of Wilson's contributions to research on population and migration is traced through a survey of subsequent research.

Exploring the development of Malaysian seaports as a hub for tourism activities

Purpose The purpose of this paper is to explore the prospects of Malaysian seaports as hubs for seaport tourism. This symbiosis nexus between seaports and tourism needs to be explored to provide a luxury economic growth. Combinations of these two segments are expected to explore a new market in Malaysian tourism industry. Design/methodology/approach A thorough case study consists of five major seaports in Malaysia including Penang Port, Port Klang, Port of Tanjung Pelepas, Kuantan Port, Bintulu Port and Kota Kinabalu; these seaports have been selected to reveal their opportunities for the prospect of Malaysian seaport tourism via spatial interaction model. Findings Four main components including cruise activities, support from intra-region and inter-region economic corridors and the seaport regionalisation can be integrated to reveal the capacity of Malaysian seaport to be hub for seaport tourism. Originality/value This paper incorporates tourism sector as one of the streams in the fifth-generation seaports. Seaports and tourism are two economic generators in Malaysia and infusion of these components is expected to enhance the economic prospect, diversify the function of seaports and reduce the over-dependence on conventional tourism activities.