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The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

World's human dynamics can be parameterized with metrics that explain the current model of economic growth and its sustainability. Changes in the world's human dynamics are crucial for understanding the current state of the world, which is faced with increasing challenges related to globalization. In this paper, we propose to analyze the shifting locations of centers of gravity of four basic global indicators (these are Gross Domestic Product, carbon dioxide emissions, population, and urban population) for the period 1960–2016. The spatial locations of the respective centers of gravity (one per year) draw some traces that explain, at least partially, relevant changes on different world's human dynamics at a global level. These traces and dynamics are further discussed. In addition, these traces are fundamental for predicting upcoming trends for the next few years. Results shown here may help political leaders and policymakers for solving upcoming and future global challenges related to the current economic system and its impact on the environment.

© 2013 IEEE. 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 vary 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 paper, 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 the 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.