Locked up? The development and internal migration nexus in Colombia

Although a sizable number of studies have been exploring the migration development nexus in international settings, there is still a reduced number on internal contexts in recent years. This research aims to estimate the causal effect of origin economic conditions on internal population migration using a time series of the Colombian states between 2012 and 2019. This analysis provides a macro perspective of associations and causation between population dynamics and development in the current changes observed using spatial interaction models. Likewise, it analyses the current portray of internal migration in Colombia (defined by five-years and one-year flows). The evidence shows that the migration hump depends on the scale at which it is analyzed. At an aggregated scale, initial economic conditions are negatively associated with migration until a threshold where this relationship is reversed. The opposite is observed in the rural migrants subsample.

Gravitational Analysis in Regional Science and Spatial Economics: A Vector Gradient Approach to Trade

For decades, gravitational analysis has been a key instrument in analyzing spatial flows. Time and again, it has prompted new and challenging research questions. This paper provides a concise overview of the foundation, the conceptualization and empirical relevance of gravitational principles in regional science and spatial economics. Attention is also given to general “social physics” interpretations of gravity in spatial interaction models and to the impact of intangible distance frictions. The main emphasis in the study is placed on the significance of spatial impedance functions and gravity potential analysis. In particular, the paper focuses on cross-border trade and has three main goals: (i) to address the robustness of distance friction parameters related to trade borders, employing, inter alia, quantitative results from meta-analyses on trade models in spatial economics; (ii) to present a promising methodology based on gravity potential and the related gravitational gradient models that include directional intensities of flows; (iii) to test the validity of the latter approach on the basis of a vector gradient analysis of export patterns of the Netherlands. The paper argues that—despite the space-reducing impact of the modern digital technologies—gravitational principles still have an uncontested relevance in an analysis of spatial flows in regional science.

The spatial structure debate in spatial interaction modeling: 50 years on

Spatial interaction and spatial structure are foundational geographical abstractions, though there is often variation in how they are conceptualized and deployed in quantitative models. In particular, the last five decades have produced an exceptional diversity regarding the role of spatial structure within spatial interaction models. This is explored by outlining the initiation and development of the notion of spatial structure within spatial interaction modeling and critically reviewing four methodological approaches that emerged from ongoing debate about the topic. The outcome is a comprehensive coverage of the past and a sketch of one potential path forward for advancing this long-standing inquiry.

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.

The spatial structure debate in spatial interaction modeling: 50 years on

Spatial interaction and spatial structure are foundational geographical abstractions, though there is often variation in how they are conceptualized and deployed in quantitative models. In particular, the last five decades have produced an exceptional diversity regarding the role of spatial structure within spatial interaction models. This is explored by outlining the initiation and development of the notion of spatial structure within spatial interaction modeling and critically reviewing four methodological approaches that emerged from ongoing debate about the topic. The outcome is a comprehensive coverage of the past and a sketch of one potential path forward for advancing this longstanding inquiry.

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 utilize 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 paper, the usefulness of a recently available big transport dataset - the New York City (NYC) 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 dataset. In addition, the high temporal resolution of the taxi data provide 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.