A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s location and scale parameters than when the scale parameter is taken to be constant over the region.
Shared e-scooter systems were first introduced in 2017 and have since been spreading around the world as a sustainable mode of transport. The success of this mode is also due to new urban mobility strategies and plans, such as the European Sustainable and Smart Mobility Strategy, which relies on non-pollutant modes. To display the range of effects that can be achieved in urban mobility through the proper implementation of shared e-scooter systems, a systematic literature review and a case study were performed. It was found that this shared system can help cities with environmental issues, such as reducing air pollution, reducing inequality in access to transport, promoting money-saving, and improving mobility resilience. During the Covid-19 pandemic, shared e-scooters became a great asset in many cities worldwide, because they promote social distancing and help cities not to rely only on private cars to replace public transport rides, especially for short-distance trips. In the case study of Braga, it was found that the city still relies on shared e-scooter modes as a mobility option after the pandemic, also promoting special fares for people to start using the service.
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.
At present, conflicts between urban development and the climate environment are becoming increasingly apparent under rapid urbanization in China. Revealing the dynamic mechanism and controlling factors of the urban outdoor thermal environment is the necessary theoretical preparation for regulating and improving the urban climate environment. Taking Hangzhou as an example and based on the local climate zones classification system, we investigated the effects of land cover composition and structure on temperature variability at the local scale. The measurement campaign was conducted within four local climate zones (LCZ 2, 4, 5, and LCZ 9) during 7 days in the summer of 2018. The results showed that the temperature difference within the respective LCZ was always below 1.1 °C and the mean temperature difference between LCZs caused by different surface physical properties was as high as 1.6 °C at night. Among four LCZs, LCZ 2 was always the hottest, and LCZ 9 was the coolest at night. In particular, the percentage of pervious surface was the most important land cover feature in explaining the air temperature difference. For both daytime and nighttime, increasing the percentage of pervious surface as well as decreasing the percentage of impervious surface and the percentage of building surface could lower the local temperature, with the strongest influence radius range from 120 m to 150 m. Besides, the temperature increased with the SVF increased at day and opposite at night.
Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.
The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.
Spatial Statistics and Disease Mapping Paper Session at the 2016 American Association of Geographers Annual Meeting.https://github.com/maps-apps-n/geography-thesis/blob/master/TBIncidenceConfPaper.pdf tl;dr A multi-variate multi-level regression analysis proposes primary schools as major hotspot sources of TB incidence in Bolivia per geographical coverage .
Physical Geography poster session of the American Association of Geographers, 2018. https://github.com/maps-apps-n/flgroundwater/blob/master/pdfposter.pdf tl;dr 1. An increase in non-medical barium in locations in proximity to oil wells along Brevard County and the Gold Coast. 2. A cluster of enterococcus bacteria along with an increase in salinity in Gulf County.
Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.