Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Future Property Risk Estimation For Wildfire In Louisiana, USA

Background: Wildfire is an important but understudied natural hazard. As with other natural hazards, wildfire research is all too often conducted at too broad a spatial scale to identify local or regional patterns. This study addresses these gaps by examining the current and future wildfire property risk at the census-block level in Louisiana, a U.S. state with relatively dense population and substantial vulnerability to loss from this hazard, despite its wet climate. Here wildfire risk is defined as the product of exposure and vulnerability to the hazard, where exposure is a function of the historical and anticipated future wildfire frequency and extent, and the latter is a function of population, structure and content property value, damage probability, and percent of property damaged. Results: Historical (1992−2015) average annual statewide property loss due to wildfire was $5,556,389 (2010$), with the greatest risk to wildfire in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. Based on existing climate and environmental model output, this research projects that wildfire will increase by 25 percent by 2050 in Louisiana from current values. When combined with projections of population and property value, it is determined that the geographic distribution of risk by 2050 will remain similar to that today – with highest risk in southwestern inland Louisiana and east-central Louisiana. However, the magnitude of risk will increase across the state, especially in those areas. Projected annual loss will be $11,167,496 by 2050 (2010$) due to population growth, intensification of development at the wildland-urban interface, and climate change. The wildfire-induced property damage is notable because it is projected to increase by 101 percent. These values do not include crop, forestry, or indirect losses (e.g., cost of evacuation and missed time at work), which are likely to be substantial. Conclusions: The results suggest that increased efforts are needed to contain wildfires, to reduce the future risk. Otherwise, wildfire managers, environmental planners, actuaries, community leaders, and individual property owners in Louisiana will need to anticipate and budget for additional efforts to mitigate the economic (and presumably other) impacts associated with a substantial and increasing hazard that often goes underestimated.

Evaluation of the Risk Induced by Soil Erosion on Land Use. Case Study: Guruslău Depression

Changes in land use, increasing of agricultural areas to the detriment of wooded ones, and poor management of agricultural land, along with the impact of current changes in the climate (reflected in the increase of the climate aggression index) makes soil erosion one of the main risks associated with improper land use, with a direct impact on its productivity and an indirect impact on human beings. The aim of this study is to assess the risk induced by surface soil erosion on land use, using as our main method of investigation the development of two models of integrated spatial analysis of the territory: a derived model of the universal soil loss equation (USLE) and a qualitative model that integrates the result of soil erosion assessment with the database representing the land use. This was carried out in order to highlight the impact on the territory. The spatial analysis models were developed on a structure of vector spatial databases, through which the soil type, soil texture, climate aggression coefficient, and land use were mapped, and alphanumeric databases, representing the market cost of land, in EUROs, that highlight the quality of cultivated land (in terms of productive economic potential). The induced risk estimation is based on a qualitative rating of soil erosion vulnerability on a scale from 1 to 5 (1-low vulnerability; 5-high vulnerability) and of the reduction of the economic value of the land (according to the vulnerability rating). The implemented methodology highlights the quantitative risk, with a maximum value of about 46.000 EUROs, spatially identified on large surfaces on the outskirts of the Jibou municipality. It is mainly caused by the impact of soil erosion on large areas of orchards, which provide necessary products for human consumption. The present methodology can be implemented on similar areas and can be used as a model of good practices in risk assessment based on financial losses by local public authorities.