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.

Exploring the Patterns of Resident Resettlement in Rural and Suburban Areas and Their Influence on the Passenger Trip Generation

The region is a complex multi-element and heterogeneous dynamic system. Regional public transport systems are characterized by heterogeneity and incompleteness of information about the processes that take place in them. The factors determining the demand for public passenger transport services, the structure of suburban population transit and the economic consequences of changing this structure have not yet been sufficiently investigated. There is no quantitative assessment of the control factors’ influence on the results of the passenger transport system functioning in suburban traffic, which complicates the effective management in this field.In order to determine the mutual influence of objects with unclear functional connection, one of the methods of computational geometry is considered, which allows quantifying and obtaining tabulated functions of various components and parameters of settlements' interaction and centers of gravity. The numerical characteristics obtained by the described method are further used in the modeling of the processes of efficient functioning and collaboration of urban passenger transport with other external transport types, in particular at interchanges.As a result of the proposed operations, we obtain a tabulated function of the investigated parameter for the area of spatial interaction of infrastructure objects and visualize the calculated parameters in the form of graphical dependencies or in the form of a three-dimensional surface. Thus, using the method of computational geometry, with constructing a Voronoi diagram and executing the Delaunay triangulation, it is possible to obtain tabulated characteristic parameters for inhomogeneous zones of mutual influence where infrastructure objects with unclear functional connection are located.

Tourists’ Behavioral Characteristics Regarding Island-Based Tourism Destinations through the Perspective of Spatial Constraints: A Case Study of Yangma Island in China

The factors affecting tourist behavior are complex and diverse, but research on its effect from a purely spatial perspective is still limited. The aim of this paper is to explore the dichotomous interaction between tourists and islands: the behavioral patterns of tourists in island destinations and the mechanisms by which island spaces constrain tourist behavior. This study uses fine-grained global positioning system (GPS) tracking data actively authorized and released by tourists. We empirically studied tourist behavior from a spatial constraint perspective and discovered the following: island space has a strong influence and constraining effect on tourist behavior; different spatial attributes have different constraining effects on tourist behavior; and people with different identities interact with different attributes of space to produce different spatial properties, resulting in a ‘harmony-contradiction’ model of spatial interaction. These findings are of great value in expanding the perspective of spatial constraints and exploring the interactions between people and land. They are also of great practical significance in promoting spatial planning synergy, facilitating the construction of high-quality island-based tourism destinations, and building a harmonious relationship between people and land.

Provincial division of economic zones based on the improved urban gravity model: A case study of Hunan Province, China

Regional collaboration and the division of economic zones have been widely discussed for sustainable development. This paper aims at building a framework for analyzing the provincial division of economic zone (DEZ) via considering spatial interaction among regions based on the improved gravity model and clustering approaches. The proposed framework of province DEZ is applied in the case study of Hunan Province, China. The results reveal that Chenzhou city in the south of Hunan Province is always excluded from the DEZ due to its larger external gravity from other cities in neighboring provinces. It also shows that the city components of economic zones (EZs) have experienced a fluctuation at a higher degree from 2002 to 2006 to 2009 while it reached to a stable phase in 2013. Furthermore, cross-provincial regional integration and the highway construction have a significant impact on the change of city components of EZ. The findings are of great potential in regional planning that should be incorporated to the toolkit of regional policy and sustainable development for local governments.

The unequal commute: Comparing commuting patterns across income and racial worker subgroups

The spatial dimension of the journey-to-work has important implications for land use and development policymaking and has been widely studied. One thrust of this research is concerned with the disaggregation of workers into subgroups for understanding disparities in commute. Most of these studies, however, were limited to the disaggregation by single socioeconomic class. Hence, this research aims to examine commuting disparities across commuter subgroups stratified by two socioeconomic variables—income and race—using a visual analytics approach. By applying the doubly constrained spatial interaction model to the 2014 Longitudinal Employer-Household Dynamics data, this research first synthesizes commuting flows for Downtown Houston workers across income-race subgroups at the tract level in Harris County, Texas, USA. It then uses bivariate choropleth mapping to visualize the spatial distributions of major Downtown Houston commuter neighborhoods by income-race classes, and significant commuting disparities are identified across income-race subgroups. The results highlight the importance of considering income and race simultaneously for commuting research. The visualization could help policymakers clearly identify the unequal commute across worker subgroups and inform policymaking.

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

Safety Management of Civil Engineering Construction Based on Artificial Intelligence and Machine Vision Technology

With the development of social economy and the progress of science and technology, the process of urbanization continues to deepen and the progress of the construction industry changes with each passing day. People are paying more and more attention to the safety of civil engineering. Accidents frequently occurred during the construction process, which exposed the lack of attention to safety issues during the construction process, the lack of effective supervision of safe construction, and the lack of safety awareness of construction personnel, which had an impact on the construction of civil engineering. In this article, a solution to the safety management system for construction personnel based on artificial intelligence machine vision technology is proposed for the study of the safety management of civil engineering structures. First of all, through the research and analysis of the problems in the safety management of construction workers, the types of safety accidents of construction workers are summarized and the effect of machine understanding of the construction scene is realized through target detection and description of the spatial interaction between the two. A real-time detection and early warning platform is built, and early warnings are automatically sent out when a predefined dangerous scene occurs, so as to achieve the purpose of preventing construction accidents. The experimental results in this paper show that, in the use of artificial intelligence machine vision technology to manage the safety of civil engineering construction, the safety management of workers in the construction is realized, and the results show that the level of construction safety management has increased to 97.4%, ensuring the quality of civil engineering construction and safety.

Organocatalytic Discrimination of Non-Directing Aryl and Heteroaryl Groups: Enantioselective Synthesis of Bioactive Indole-Containing Triarylmethanes

Despite the enormous developments of asymmetric catalysis, the basis for asymmetric induction is largely limited to spatial interaction between substrate and catalyst. Consequently, asymmetric discrimination between two sterically similar groups remains a challenge. This is particularly formidable for enantiodifferentiation between aryl and heteroaryl groups without a directing group or electronic manipulation. Here we address this challenge by a robust organocatalytic system leading to excellent enantioselection between aryl and heteroaryl groups. With the versatile 2-indole imine methide as platform, an excellent combination of a superb chiral phosphoric acid and the optimal hydride source provided efficient access to a range of highly enantioenriched indole-containing triarylmethanes. Control experiments and kinetic studies provided important insights into the mechanism. DFT calculations also indicated that, while hydrogen bonding is important for activation, the key interaction for discrimination of the two aryl groups is mainly π-π stacking. Preliminary biological studies also demonstrated the great potential of these triarylmethanes for anticancer and antiviral drug development.