Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil.

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

The potential of volunteered geographic information to investigate peri-urbanization in the conservation zone of Mexico City.

Due to the availability of Web 2.0 technologies, volunteered geographic information (VGI) is on the rise. This new type of data is available on many topics and on different scales. Thus, it has become interesting for research. This article deals with the collective potential of VGI and remote sensing to detect peri-urbanization in the conservation zone of Mexico City. On the one hand, remote sensing identifies horizontal urban expansion, and on the other hand, VGI of ecological complaints provides data about informal settlements. This enables the combination of top-down approaches (remote sensing) and bottom-up approaches (ecological complaints). Within the analysis, we identify areas of high urbanization as well as complaint densities and bring them together in a multi-scale analysis using Geographic Information Systems (GIS). Furthermore, we investigate the influence of settlement patterns and main roads on the peri-urbanization process in Mexico City using OpenStreetMap. Peri-urbanization is detected especially in the transition zone between the urban and rural (conservation) area and near main roads as well as settlements.

Human and remote sensing data to investigate the frontiers of urbanization in the south of Mexico City.

The data presented here were originally collected for the article "Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing" (Rodriguez et al. 2017) [4]. They were divided into three databases (remote sensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remote sensing database was prepared as a result of a semi-automatic classification, dividing the land cover data into urban and non-urban classes. The second data set details an alternative view of the phenomena of urbanization by concentrating on illegal settlements in the conservation zone. It was based on voluntary complaints about environmental and land use offences filed at the Procuraduria Ambiental y del Ordenamiento Territorial del Distrito Federal (PAOT), which is a governmental entity responsible for reviewing and processing grievances on five basic topics: illegal land use, deterioration of green areas, waste, noise/vibrations, and animals. Anyone can file a PAOT complaint by phone, electronically, or in person. The complaint ends with a resolution, act of conciliation, or recommendation for action by other actors, such as the police or health office. The third data about unemployment was extracted from Mexico׳s National Census 2010 database available via public access.