Maximum entropy methods are used to infer the true trip-distribution matrix in cases where parts of the data are suppressed due to privacy concerns. Large proportions of the suppressed data are found to be inferred correctly when the marginal totals in the trip-distribution are known. Entropy-based approaches are further found to outperform a strategy of ignoring suppressed information in cases with suppressed marginal totals and/or a higher cut-off value for suppressing cell information. We show that standard suppression rules induce systematic bias in estimated distance-deterrence parameters, and that entropy-based reconstruction restores macro-consistent origin–destination flows sufficient for correct inference. Our methods are demonstrated to reduce the systematic bias in estimates of the distance deterrence parameter to such small numbers that it is effectively zero, preventing potentially serious bias in estimates and predictions resulting from standard spatial interaction models. We also identify the conditions under which time-series data and inter-zonal distances most improve reconstruction accuracy.
This article analyzes job quality using a spatial model for men and women in the urban area of the city of Cali, Colombia. The results reveal positive spatial dependence among men and in the overall population, while no spatial effects are detected for women. In particular, work experience, completion of high school education, and commuting to work by motorcycle increase the probability of having a quality job in both the total population and among men. By contrast, being the head of the household, being a Venezuelan migrant, working in the construction sector, and the worsening of working conditions due to the COVID-19 pandemic negatively affect job quality in both groups of interest.
Several spatial centrality measures have been proposed to capture the differentiation of urban space. Accessibility measures appraise ease of access to other locations; intermediation measures describe a location’s control over network movement. Commonly used measures overlook attractors and the decay of interaction with distance, and preferential centrality and Freeman–Krafta centrality were introduced to incorporate, respectively, the agglomeration of activities and distance decay. This article compares five measures – closeness, betweenness, Freeman–Krafta centrality, and preferential centrality with and without its agglomeration term – in Porto Alegre, Brazil. The dependent variable is improved-property value per square metre of land, built from 2022 property transactions and the municipal unit registry. The comparison is made for 7,775 address records and, after aggregation, for 812 hexagonal cells of 500 m. Performance is defined once, as the coefficient on standardized predictors and the trend-only prediction error under spatially blocked cross-validation. In the headline specifications the three accessibility measures are more closely associated with the outcome than the two intermediation measures at both levels, and preferential centrality with agglomeration has the largest standardized coefficient and the lowest prediction error at both, although its advantage over the version without agglomeration is small and does not hold under every specification. Betweenness and Freeman–Krafta centrality are indistinguishable from zero at the address level and positive and significant at the 500 m cells. Built-area controls substantially attenuate the accessibility associations, highlighting the importance of building intensity and property composition.
This study examines how the architecture of global liner shipping connectivity changed between 2006 and 2026, using the UNCTAD Liner Shipping Bilateral Connectivity Index (LSBCI), a modelled economy-level index rather than a physical vessel-route network. We build 21 annual first-quarter networks from 655,370 directed bilateral records (327,685 unique economy pairs) covering up to 191 economies, and examine small-world structure, the strength distribution, inequality, a candidate structural break, and the association with seaborne freight tonnage, with nine robustness checks. The networks are small-world relative to an Erdős–Rényi benchmark in every year (σ = 2.43–2.66), while the connectivity-strength distribution is not consistent with a single power law (bootstrap p ≤ .025 in the three years tested; lognormal preferred, p < .001). Inequality in connectivity strength rose (Gini 0.162 to 0.201, p < .001), whereas the HHI and top-10 share show no significant trend, so the largest hubs did not become more dominant. A Quandt–Andrews search selects 2017, not 2015 or 2020, as the best-fitting break; this coincides with the formation of the Ocean Alliance and THE Alliance but does not identify their effect, and power is limited (n = 21). Connectivity strength is positively associated with seaborne freight tonnage (elasticity 0.54, p = .004), robust to Driscoll–Kraay standard errors and PPML, but the association weakens once lagged trade is included and no macroeconomic controls are available. By 2026 China ranks first in connectivity strength while the UK, Netherlands and US remain among the leading economies: a reordering, not a replacement, of hubs.
This study examines the structural connectivity and topological accessibility of an Asia-connected container shipping network, including Asian and non-Asian maritime locations. An undirected network comprising 1,138 maritime locations and 1,806 links is constructed and analysed through complex network measures. The network is low-density, locally clustered, and has a short average shortest-path length, indicating sparse overall connectivity alongside interconnected neighbourhoods. Centrality rankings reveal distinct network roles. Shanghai has the highest degree, Singapore the highest betweenness, and Nanjing the highest strength based on recorded sailing frequency. Non-Asian ports and passage nodes also occupy prominent intermediary positions, highlighting connections beyond Asia and the importance of distinguishing different location types. These findings identify connectivity patterns and potential dependence on key intermediary locations, but do not establish operational efficiency or resilience. The analysis provides a structural basis for connectivity planning and subsequent disruption assessments, while its short observation period limits conclusions about persistent network patterns.
