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Nowadays, around half of the global population lives in urban areas. This rate is expected to increase up to two-thirds by the year 2050. Most studies analyze urban dynamics in wide geographic ranges, focusing mainly on cities. According to them, the global population is spatially distributed (and polarized) in two extremes: large urban agglomerations and rural deserts. However, this remark is excessively general and imprecise. For this reason, it remains essential to analyze these dynamics at other spatial scales. A close-up look in thinly populated regions shows how urban dynamics are also noticeable. In this paper, we analyze spatiotemporal patterns of population distribution in a predominantly rural area by applying a local-scale approach. These patterns are represented by using spatial networks with nodes representing the human settlements and links showing hierarchies between nodes. This case study is conducted in a small municipality located in northwestern Spain. It is a predominantly rural area with a very particular spatial pattern of population distribution.
José Balsa-Barreiro; Alfredo J. Morales; Rubén C. Lois-González. Mapping Population Dynamics at Local Scales Using Spatial Networks. Complexity 2021, 2021, 1 -14.
AMA StyleJosé Balsa-Barreiro, Alfredo J. Morales, Rubén C. Lois-González. Mapping Population Dynamics at Local Scales Using Spatial Networks. Complexity. 2021; 2021 ():1-14.
Chicago/Turabian StyleJosé Balsa-Barreiro; Alfredo J. Morales; Rubén C. Lois-González. 2021. "Mapping Population Dynamics at Local Scales Using Spatial Networks." Complexity 2021, no. : 1-14.
A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups, and definition of similar behaviors. Naturalistic Driving methods provide a solution by allowing the collection of exhaustive datasets in quantitative and qualitative terms. However, exploiting and analyzing these datasets is complex and resource-intensive. Moreover, most of the previous studies, have constrained the great potential of naturalistic driving datasets to very specific situations, events, and/or road sections. In this paper, we propose a novel methodology for extracting driving patterns from naturalistic driving data, even from small population samples. We use Geographic Information Systems (GIS), so we can evaluate drivers’ behavior and reactions to certain events or road sections, and compare across situations using different spatial scales. To that end, we analyze some kinematic parameters such as speeds, acceleration, braking, and other forces that define a driving attitude. Our method favors an adequate mapping of complete datasets enabling us to achieve a comprehensive perspective of driving performance.
José Balsa-Barreiro; Pedro M. Valero-Mora; Mónica Menéndez; Rashid Mehmood. Extraction of Naturalistic Driving Patterns with Geographic Information Systems. Mobile Networks and Applications 2020, 1 -17.
AMA StyleJosé Balsa-Barreiro, Pedro M. Valero-Mora, Mónica Menéndez, Rashid Mehmood. Extraction of Naturalistic Driving Patterns with Geographic Information Systems. Mobile Networks and Applications. 2020; ():1-17.
Chicago/Turabian StyleJosé Balsa-Barreiro; Pedro M. Valero-Mora; Mónica Menéndez; Rashid Mehmood. 2020. "Extraction of Naturalistic Driving Patterns with Geographic Information Systems." Mobile Networks and Applications , no. : 1-17.
In the age of hyperconnectivity, we are undergoing an explosive increase in the interdependence of the political, commercial, financial, and social spheres. The recent rise of deglobalization movements across the world highlights the local negative externalities of poorly designed networked structures at the global scale: high social complexity derived from immigration shocks, elevated risk of contagion in financial downturns, as well as increasing inequality and social polarization. While global interdependencies on networks enable opportunities for cultural and economic growth, they also establish channels for unresolved conflicts and design errors to propagate across social systems. We analyze failure propagation on networks as a function of density and centralization of inter-dependencies. We show that the risk of failure in both overly distributed and centralized systems behave similarly when the number of connections exceeds a system-dependent threshold number. The scale of interdependencies matters and must be considered for the design of policies targeted at increasing or decreasing the connectivity of social systems.
José Balsa-Barreiro; Aymeric Vié; Alfredo J. Morales; Manuel Cebrián. Deglobalization in a hyper-connected world. Palgrave Communications 2020, 6, 1 -4.
AMA StyleJosé Balsa-Barreiro, Aymeric Vié, Alfredo J. Morales, Manuel Cebrián. Deglobalization in a hyper-connected world. Palgrave Communications. 2020; 6 (1):1-4.
Chicago/Turabian StyleJosé Balsa-Barreiro; Aymeric Vié; Alfredo J. Morales; Manuel Cebrián. 2020. "Deglobalization in a hyper-connected world." Palgrave Communications 6, no. 1: 1-4.
Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.
José Balsa-Barreiro; Pedro M. Valero-Mora; José L. Berné-Valero; Fco-Alberto Varela-García. GIS Mapping of Driving Behavior Based on Naturalistic Driving Data. ISPRS International Journal of Geo-Information 2019, 8, 226 .
AMA StyleJosé Balsa-Barreiro, Pedro M. Valero-Mora, José L. Berné-Valero, Fco-Alberto Varela-García. GIS Mapping of Driving Behavior Based on Naturalistic Driving Data. ISPRS International Journal of Geo-Information. 2019; 8 (5):226.
Chicago/Turabian StyleJosé Balsa-Barreiro; Pedro M. Valero-Mora; José L. Berné-Valero; Fco-Alberto Varela-García. 2019. "GIS Mapping of Driving Behavior Based on Naturalistic Driving Data." ISPRS International Journal of Geo-Information 8, no. 5: 226.
Human societies have radically changed from the second half of the 20th century. Urban areas are increasingly concentrating more people and economic activities. Connections between market economies in different regions have increased exponentially the flow of people and goods at a global level. These movements are spatially organized through a hierarchical transportation network that connects different areas. The quality and coverage of such network varies greatly across regions. Territorial cohesion and accessibility within a region could be roughly evaluated through the existing level of connectivity between urban nodes. This can be easily done by estimating travel times between different points in the territory, which would show relevant differences based on both the territory itself and the existing infrastructure. Unfortunately, this information is not typically shown in traditional maps. In this article, we propose a novel methodology for assessing the degree of territorial accessibility within and across urban networks, by using time-distorted maps. To this end, we consider multiple scenarios related to different public transport modes and times of day. The study area corresponds to a Spanish region, where we set up a relatively extended network by considering its most relevant cities and towns. Final maps can clearly illustrate the deficiencies in transport infrastructure and/or connections from a spatial perspective. These maps can be excellent tools for supporting technicians, politicians, public managers, and other stakeholders in the decision-making process. INDEX TERMS(Time varying) Accessibility, Geographic Information Systems, Territorial cohesion, Time distorted maps, Transport geography, Travel-time maps.
Jose Balsa-Barreiro; Lukas Ambuuhl; Monica Menendez; Alex ‘Sandy’ Pentland. Mapping Time-Varying Accessibility and Territorial Cohesion With Time-Distorted Maps. IEEE Access 2019, 7, 41702 -41714.
AMA StyleJose Balsa-Barreiro, Lukas Ambuuhl, Monica Menendez, Alex ‘Sandy’ Pentland. Mapping Time-Varying Accessibility and Territorial Cohesion With Time-Distorted Maps. IEEE Access. 2019; 7 (99):41702-41714.
Chicago/Turabian StyleJose Balsa-Barreiro; Lukas Ambuuhl; Monica Menendez; Alex ‘Sandy’ Pentland. 2019. "Mapping Time-Varying Accessibility and Territorial Cohesion With Time-Distorted Maps." IEEE Access 7, no. 99: 41702-41714.
José Balsa-Barreiro. Airborne light detection and ranging (LiDAR) point density analysis. Scientific Research and Essays 2012, 7, 1 .
AMA StyleJosé Balsa-Barreiro. Airborne light detection and ranging (LiDAR) point density analysis. Scientific Research and Essays. 2012; 7 (33):1.
Chicago/Turabian StyleJosé Balsa-Barreiro. 2012. "Airborne light detection and ranging (LiDAR) point density analysis." Scientific Research and Essays 7, no. 33: 1.