Locating day-labor employment: toward a geographic understanding of day-labor hiring site locations in the San Diego metropolitan area

Day-labor hiring sites are common features of the contemporary North American landscape. These are public and semi-public spaces where mostly male residents congregate daily in hopes of being hired for short-term work. Research on contemporary day-labor markets in the United States to date tends to be policy-oriented, intended to reduce the injustices that are a common part of life as a day laborer. Unfortunately, very little is understood about the spatial organization of day-labor markets. Drawing on more than five years of mixed-methods research in the San Diego Metropolitan Area (SDMA), this paper takes two important steps toward a spatial understanding of day-labor hiring sites. First, it demonstrates that informal hiring sites are established in locations that maximize laborers’ chances of finding employment. Second, it establishes a geo-spatial typology of hiring sites for the SDMA that can be used to better tailor day-labor support efforts and policy to site-level context.     

Neighborhood Factors Associated with Same-Sex Households in U.S. Cities

Quality of life cannot be fully described by the availability of regional amenities. Instead, it also depends on the quality of neighborhoods and housing. In connection with discussions on knowledge workers, not much systematic research has been done on the characteristics of neighborhoods characterized by high proportions of same-sex households. By analyzing census tracts within select counties across the United States using Poisson regressions, this article investigates what factors are related to the number of same-sex households.     

The Discursive Regime of the "American Dream" and the New Suburban Frontier: The Case of Kendall County,Illinois

This study examines the current role of ideology in maintaining and reproducing consumer demand for residential suburban spaces in the outer ring of American metropolitan regions. It highlights the contradiction between the promises that are made through the ideology and discourse of the American Dream and the inherent limits of those promises within the realities of a highly dynamic capitalist society. It is argued that this powerful discursive formation has evolved through recent neoliberal times, and is manifest in the suburban landscape in new ways. It must continue to produce simulated images that signify mythologized narratives of the past that cater to sentiments of hope and a better life in the future. At the same time, it works through existing fears and anxieties in order to secure the progressive accumulation of capital in the suburban built environment. Evidence will be provided from the ongoing suburbanization of Kendall County, Illinois at the southwestern edge of metropolitan Chicago.     

An Algorithm for Localised Contour Removal over Steep Terrain

Abstract

Isolines have proved to be a highly effective way of conveying the shape of a surface (most commonly in the form of height contours to convey geographical landscape). Selecting the right contour interval is a compromise between showing sufficient detail in flat regions, whilst avoiding excessive crowding of lines in steep and morphologically complex areas. The traditional way of avoiding coalescence and confusion across steep regions has been to manually remove short sections of intermediate contours, while retaining index contours. Incorporating humans in automated environments is not viable. This research reports on the design, implementation and evaluation of an automated solution to this problem involving the automatic identification of coalescing lines, and removal of line segments to ensure clarity in the interpretation of contour information. Evaluation was made by subjective comparison with Ordnance Survey products. The results were found to be very close to the quality associated with manual techniques.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

Elements of viral cartography

ABSTRACT

Making and sharing maps is easier than ever, and social media platforms make it possible for maps to rapidly attain widespread visibility and engagement. Such maps can be considered examples of viral cartography – maps that reach rapid popularity via social media dissemination. In this research we propose a framework for evaluating the design and social dissemination characteristics of viral maps. We apply this framework in two case studies using maps that reached wide audiences on Twitter. We then analyze collections of maps derived from and inspired by viral maps using image analysis and machine learning to characterize their design elements. Based on our initial work to conceptualize and analyze virality in cartography, we propose a set of new research challenges to better understand viral mapmaking and leverage its social affordances.     

风机节能技术应用与经济性对比

主要针对300MW级循环流化床锅炉一、二次离心风机的几种调速节能方式进行比较,针对电厂普遍采用的变频器和液力耦合器调速的经济性进行了探讨、对比。     

滨州市县域经济发展中土地利用的转变思路探讨

近几年,县域经济作为经济发展的重要基础越来越受到各级政府高度重视。滨州市以其特有的土地资源优势,为县域经济发展提供重要支撑。针对滨州市县域经济发展现状,从土地资源的比较优势人手,提出了在现阶段发展县域经济土地利用的新思路。     

基于变形信息分解的大坝变形趋势判断及预测研究

在分离大坝变形数据信息的基础上,利用重标极差法实现大坝变形趋势判断,然后利用优化极限学习机及混沌理论实现大坝变形预测。重标极差分析表明,大坝变形始终具有正向持续性,但其程度具有减弱趋势。在变形预测过程中,模型参数的递进优化不仅能提高预测精度,还能有效提高其稳定性,预测模型的相对误差均值均小于2%,验证了本文预测思路的有效性。大坝变形趋势判断及预测结果一致性较好,均认为大坝变形仍会进一步增加,但增加幅度相对较小,趋向于稳定发展。     

基于聚类算法的钻孔瞬变电磁视电阻率立体成像方法

煤矿井下地球物理水害超前探测要求探测点20 m范围内不得有积水和金属物体,传统电磁法超前探测技术已不能满足要求,钻孔瞬变电磁法通过将收发装置送入掘进工作面前方的钻孔中进行探测,既远离了巷道中的各种干扰,又提高了隐蔽致灾水体的探测精度。为解决该方法对钻孔径向异常体的准确定位解释难题,通过三维正演总结了其水平分量异常响应特征,提出了异常体象限确定准则,研究了根据水平分量幅值和异常象限综合求取异常体工具面角的计算方法。将由垂直分量计算得到的每一个视电阻率视为独立异常体,基于K-means聚类算法对相应的水平分量异常曲线特征值进行二分类,实现了全数据集的视电阻率象限自动划分,结合异常工具面角算法研究得出钻孔瞬变电磁视电阻率立体成像方法。最后计算了三维数值模型的立体成像结果,对钻孔径向的小规模低阻异常体取得了良好效果。结果表明:基于K-means聚类算法的钻孔瞬变电磁视电阻率立体成像方法是地球物理与机器学习的有机结合,该方法能够为井下掘进工作面隐伏水害超前探测精细解释提供技术支撑。