Empirically, the physical spatial arrangement of places provides us with a clue about the likelihood for crime opportunities based on the principles of crime prevention through environmental design (CPTED). Although we know that the quality of the urban built environment influences people’s behavior, its measurement as a variable is not an easy task. In this study, we present and develop a set of urban built environment indicators (UBEIs) based on two datasets: building footprints and road networks at the neighborhood level in the city of Praia, Cape Verde. We selected the four most relevant UBEIs to create a single urban built environment indicator (CUBEI), and then, explored their relationships with five types of crime (i.e., burglary, robbery, mugging, residential robbery, and crimes involving weapons) using correlation and regression analysis. Our results showed a consistent and statistically significant relationship between different types of crimes with both the UBEIs and CUBEI, suggesting that a poor urban built environment is associated with an increase of all types of crimes investigated in this study. Thus, to minimize crime incidents, urban planners should rehabilitate or design neighborhoods from the earlier stage, considering the principles of CPTED and broken window theory (BWT).

Urban expansion is the outcome of intensive human activity within a certain natural environment and may cause ecological and environmental problems, especially on small islands where land is a scarce resource. Praia is the capital city of Cape Verde, located on such an island. Understanding urban expansion will provide good knowledge for urban planning and policy making in balancing urban economic development and natural resource protection. According to available data, the urban expansion in Praia between 1969 and 2015 is observed in four phases (1969–1993, 1993–2003, 2003–2010, and 2010–2015). In order to integrate various data sources, this study applies an available method to coordinate and calibrate map data with different scales and forms into a consistent dataset and then introduces some improvements in the delineation of urban areas. With this data, the driving forces in each phase are explored using regression analysis, by which the main urban expansion processes are presented. We found a decrease in annual growth rate (AGR) of urban expansion after the year 2003 and a parallel stabilization of urban utilization density (UD) and land consumption per capita (LCR). This study also indicates that population is not always the persistent driving factor for urban expansion and the majority of horizontal expansion has occurred in zones with less infrastructure.

This article examines the driving forces of food security in the areas of the Nam Theun2 Hydropower Project (NT2) in Khamuan, Laos. A questionnaire survey was conducted to collect data from 100 NT2 resettlement households based on the random sampling technique. A linear regression technique was used to identify the influence of household food insecurity. The result showed that household size, food price, drought, shock, household income per month, number of laborers, gender of the household head, and farmland areas are important factors for household food insecurity. Policies should focus on irrigation that will permit yearlong cultivation. This will in turn become the stimulus for a concatenation of events in the process of development. People will resettle to practice agriculture while also expanding non-agricultural employment. Businesses in skills training, fish processing, textile, services, and crafts will be created, boosting household income. With inevitable population expansion, education in family planning will also be necessary to control population in relation to available resources.

Vector-borne diseases, primarily those transmitted by mosquitoes, are a serious public health problem. Some, such as dengue, put half of the world’s population at risk. Combating these diseases requires multifaceted strategies, with vector surveillance and control playing key roles. Robust and predictive surveillance systems for vector-borne diseases, based on risk stratification, enable the implementation of appropriate interventions across time and space. Here, we present a collection of entomological, demographic, water and sanitation, and climatic data from Praia (Cabo Verde), a hotspot for mosquito-borne diseases. These data were collected from June to November 2022, at 40 sentinel points scattered across the urban area of Praia. They constitute a valuable source of information for developing predictive scenarios of arbovirus outbreak risk using statistical models applied to spatial and non-spatial indicators. These data demonstrate the utility of GBIF in transforming large volumes of occurrence data into valuable information for arbovirus surveillance and vector control.