Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).     

GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.     

Predictive model for areas with illegal landfills using logistic regression

The existence of illegal landfills is an environmental problem in most countries. However, research on this issue is scarce and limited by the availability and quality of data on the subject. Thus, most illegal landfill studies have only been conducted in a partial manner, focusing on geographical aspects or the causes of these landfills (lack of environmental awareness, inadequate waste management systems, and the role of local government). This research analyses a sample of 120 possible areas with illegal landfills in Andalusia using logistic regression in order to obtain a predictive model for the occurrence of these landfills, including both types of variables (geographical and behavioural) jointly. The results confirm that the variables that most influence the occurrence of illegal landfills are spatial (“Industrial Land”, “Plains” and “Rural Land”); whilst the variables that most reduce the likelihood of illegal landfills are those related to certain characteristics of the municipal waste management system and environmental awareness, such as “Availability of Recycling Facilities”, “Punitive Policies”, “Supervision” and “Awareness-raising Campaigns”. The model obtained shows that variables of very different nature and magnitude interact in the occurrence of illegal landfills, each of which contributes a series of features characteristic of its scale. It is advisable, therefore, to perform an analysis using a multi-scale approach in order to gain an overall understanding of the phenomenon.     

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.     

消防战勤保障物资储备点布局与选址问题研究

为科学规划消防应急救援物资储备点布局,进一步提高消防部队处置突发事件的能力,从当前我国消防战勤保障物资储备点的建设现状入手,研究了消防战勤保障物资的分类。在此基础上明确了物资储备点的布点原则和选址方法,建立了储备点选址的数学模型,并利用计算机进行求解,得到若干个备选地址;然后在综合考量各种影响因素的前提下,通过灾害发生区域相对集中的规律进行适当的调整,进而筛选出最佳选址。最后讨论了部局级应急装备物资储备库的布局现状及建议。结果表明:储备库的选址应在科学合理的物资储备点选址基础上,与部局、总队的应急装备物资储备库相互依托、互相补充,从而形成一套符合我国消防力量建设实际的消防战勤保障物资储备体系。     

What motivates farmers to participate in sustainable agriculture? Evidence and policy implications

Sustained growth in agricultural productivity has become an ideal model and a political goal of agricultural development in China. Local participation is essential for the implementation of sustainable agriculture strategy. We conducted a case study in Hailun County, an important grain production base in China, to find ways to motivate farmers to participate in sustainable agriculture. We obtained data from semi-structured interviews of 98 households using participatory rural appraisal methods. Logistic regression models were used to explore factors that underlie farmers' attitudes towards agricultural production and their environmental awareness. Results indicated that low agricultural economic efficiency could result in less enthusiasm for agriculture among local farmers. This is a potential risk for China's food security and sustainable agricultural development. The backward development of the rural economy limited improvement of farmers' environmental awareness. Moreover, poor rural labour quality implied a lack of local participation in sustainable agriculture. Suggestions for sustainable agricultural development include establishment of a reasonable market and agricultural subsidy mechanisms, basic and vocational education for young adults, effective agricultural instruction and environmental education agencies and two-way communication mechanisms.     

气体采集袋中一氧化碳浓度与时间的关系

在不同的时间段，测定存放于普通球胆及特制的锡箔袋内的一氧化碳标准气的浓度，来春浓度值与贮存时间的关系。通过对３种一氧经碳浓度的测定，证实普通球胆采集的一氧化碳样品，应在当天进行分析；用锡箔气袋，则可保存７－１０ｄ。’     

Relation of Lake Acidification and Recovery to Fish,Common Loon and Common Merganser Occurrence in Algoma Lakes

Models are needed that predict both spatial and temporal improvements to ecosystems following reductions of acidifying emissions that produce `acid rain'. Logistic regression models were developed for the occurrence of fish and two fish-eatingbirds, common loons (Gavia immer) and common mergansers(Mergus merganser), using monitoring data collected onlakes across Ontario. These models were applied in the Algomaregion, including the Turkey Lakes Watershed (TLW). Using theWaterfowl Acidification Response Modeling System (WARMS), severalSO₂ emission reduction scenarios were simulated, i.e. thosecontributing to measured 1982–1986 sulphate deposition levels, 1994levels (corresponding to full implementation of Canadian SO₂emission reductions as stipulated in the 1991 Canada/U.S. AirQuality Agreement), 2010 levels (1994 plus full U.S. reductions),and both a 50% and a 75% further reduction beyond 2010 levels. Some habitat improvements in Algoma were predicted under the 2010scenario for all biota, but substantial increases in habitatquality, especially for mergansers, would occur only under further reductions. The TLW showed little change in chemistry orbiota, while lakes near the Montreal River were predicted toimprove substantially.     

Migration effects in a stochastic multipopulation model for African bee population dynamics

The rate of northern migration of the Africanized honey bee (AHB) in the United States has recently slowed dramatically. This paper investigates the impact of migration on the equilibrium size distributions of a particular stochastic multipopulation model, namely a coupled logistic power law model. The bivariate equilibrium size distribution of the model is derived and illustrated with parameter values used to describe AHB population dynamics. In the model, the difference between the equilibrium sizes of the two populations is a measure of the effect of migration. The distribution of this difference may be approximated by a normal distribution. The mean and variance parameters for the normal are predicted accurately by a second-order regression model based on the migration rate and the maximum size of the first population. The methodology is general, and should be useful in studying the migration effect in many other applications with one-way migration.