A parallel algorithm for coverage optimization on multi-core architectures

Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.     

Accuracy assessment of NLCD 2011 percent impervious cover for selected USA metropolitan areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m² NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m² land cover data developed as part of the United States Environmental Protection Agency’s EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km²); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC – reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.     

Changes in the residential segregation of immigrants in Sweden from 1990 to 2012: Using a multi-scalar segregation measure that accounts for the modifiable areal unit problem

In this paper, the levels of residential segregation of immigrants in Sweden during the years 1990, 1997, 2005, and 2012 are calculated. This paper applies a novel method for calculating segregation that is multi-scalar and addresses the modifiable areal unit problem (MAUP). The level of segregation is evaluated for each populated location by identifying the population that includes the k-nearest neighbours. The share of immigrants in this assessment population is then compared to the share in the reference population that comprises the K-nearest neighbours. One of the strengths of this method is the possibility to modify the reference population, thus making it possible to measure the difference in the results due to the size of the reference population. This study demonstrates that the results can considerably differ depending on which reference population is used. Furthermore, this study indicates that using different reference areas can produce completely different trends over time, such as decreasing or increasing segregation. The results demonstrate a general increase in segregation between 1990 and 1997, followed by a more complex pattern from 1997 to 2012. The segregation values are presented for all populated locations in Sweden, and population-weighted means are calculated for the whole of Sweden, in addition to the Stockholm, Malmö, and Gothenburg metropolitan areas.     

The modifiable areal unit problem in hedonic house-price models

When geographically aggregated data are included in hedonic models, the resulting coefficients are biased by the spatial scale and spatial configuration of variable measurement. We explore the effects of this modifiable areal unit problem (MAUP) within the context of hedonic price models with an individual-level dependent variable. Specifically, we developed standard and spatial hedonic regression models in order to examine the effects of the MAUP on model fit and coefficient estimates. Our empirical analysis documents several significant scale and zoning effects in the hedonic modeling framework. First, neighborhood characteristics are clearly important in efforts to improve model fit—and they are more significant contributors in the standard model than in the spatial hedonic model. For aggregation scale, the model fit change of the standard model is relatively large, whereas the change is more modest for spatial models. The patterns of change in model fit for standard and spatial hedonic models clearly diverge from one another, implying the existence of a scale level showing a maximum functional range of the submarket on which scale dependencies are expected to have an impact. Regarding the zoning effect, the model fits for both standard and spatial hedonic models vary according to the submarket systems.     

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective1

Measuring the level of segregation often encounters two methodological issues: measures are sensitive to changes in the geographical scale of the data and the effectiveness of the measure in reflecting spatial segregation. Several spatial measures have been suggested to measure spatial segregation, but whether they are more or less sensitive to changes in spatial scale has not been investigated, while some spatial measures are relatively scale-insensitive. Using the 1990 Census data of 30 selected U.S. metropolitan areas, this paper demonstrates that these spatial measures, similar to the aspatial measure, report higher levels of segregation when smaller areal units are used in the analysis. Some spatial measures are even more sensitive to scale changes than aspatial measures. Certain patterns of the scale sensitivity were identified, but no general rules can be formulated. A preliminary explanation of the scale effect on spatial segregation measures is offered.     

The Zone Definition Problem in Survey Research: An Empirical Example from New York State*

This paper examines problems of zonal definition in the context of a recent empirical project on the geography of inter-firm linkages in New York State. It is argued that the results of a spatially structured survey of private companies can change significantly depending on the manner in which the study regions of the analysis are defined. Variations in the composition of study regions are a result of either changes in spatial scale, or spatial zoning at any one scale. This point is illustrated with data from 472 New York State manufacturing firms aggregated into four different zonal systems. The results of the analysis suggest that misleading interpretations of spatial data can emerge, even when logical boundaries are selected from the outset.     

Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem

ABSTRACT

Socioeconomic and health analysts commonly rely on areally aggregated data, in part because government regulations on confidentiality prohibit data release at the individual level. Analytical results from areally aggregated data, however, are sensitive to the modifiable areal unit problem (MAUP). Levels of aggregation as well as the arbitrary and modifiable sizes, shapes, and arrangements of zones affect the validity and reliability of findings from analyses of areally aggregated data. MAUP, long acknowledged, remains unresolved. We present an exploratory spatial data analytical approach (ESDA) to understand the scalar effects of MAUP. To characterize relationships between data aggregation structures and spatial scales, we develop a method for statistically and visually exploring the local indicators of spatial association (LISA) exhibited between a variable and itself across varying levels of aggregation. We demonstrate our approach by analyzing the across-scale relationships of aggregated 2010 median income for the State of Pennsylvania and 2005–2009 cancer diagnosis rates for the State of New York between county–tract, tract–block group, and county–block group level US census designated enumeration units. This method for understanding the relationship between MAUP and spatial scale provides guidance to researchers in selecting the most appropriate scales to aggregate, analyze, and represent data for problem-specific analyses.     

利用时空密度聚类的高速公路交通事故黑点路段鉴别

研究高速公路交通事故黑点路段的时空分布规律和关联因素,一直是交通领域的关注重点。本文针对事故统计的交通事故黑点路段鉴别方法存在地理学中的可塑面积单元(MAUP)问题,提出一种基于时空密度聚类的高速公路交通事故黑点路段鉴别方法。该方法改进了传统的DBSCAN空间聚类算法,引入一种顾及时间周期性和事故严重程度的事故时空邻近计算方法,通过密度连接规则自适应鉴别各种时空尺度的交通事故黑点路段。以2012—2016年湖南省的高速公路交通事故为例进行试验,结果表明,本文方法可有效克服不同划分单元的可塑面积单元问题,自适应鉴别不同长度的黑点路段,同时可进一步挖掘黑点路段上交通事故时空聚集模式。     

Assessing environmental inequalities in the city of Santiago (Chile) with a hierarchical multiscale approach

Environmental inequalities are a common characteristic of urban areas. Environmental inequality is the unequal spatial distribution of environmental risks and goods among social groups. As environmental inequalities are inherently a spatial matter the choice of scale is essential for correctly understanding inequality issues and for designing proper and effective mitigation policies. However, the potential effects of scale of analysis on inequalities results have largely been underestimated in the assessment of environmental inequalities, leading to contradictory results from different studies. In this study we assess the patterns of environmental inequalities and associated scale issues in the city of Santiago (Chile) using a hierarchical multiscale approach. Our approach focuses on the analysis of spatial relationships between three environmental (i.e., surface temperature, air pollution, vegetation cover) and two socio-demographic variables (i.e., household wealth, population density) on multiple grain sizes and extents. We used census data, remote sensing data, and air pollution monitoring stations to generate raster layers at five grain sizes and five nested extents. We tested for inequalities through Pearson correlation analysis resulting in a total of 1530 assessed relationships. Our results show that environmental inequalities are a prevalent phenomenon in the city of Santiago, but the details of these inequalities are highly scale dependent. Changing the grain size and extent of analysis do not only affect the strength of relationships between socio-demographic and environmental variables, but also the spatial distribution of environmental inequalities across the urban landscape. Therefore, due to the scale-dependence of assessment results, researchers and decision-makers should be extremely careful when interpreting their findings and translating them into policy making. If the scale dependency of environmental inequalities is not taken into account, policy interventions may be largely ineffective because the scale at which interventions are designed may not match the scale at which inequalities are generated.     

Assessing housing growth when census boundaries change

The US Census provides the primary source of spatially explicit social data, but changing block boundaries complicate analyses of housing growth over time. We compared procedures for reconciling housing density data between 1990 and 2000 census block boundaries in order to assess the sensitivity of analytical methods to estimates of housing growth in Oregon. Estimates of housing growth varied substantially and were sensitive to the method of interpolation. With no processing and areal‐weighted interpolation, more than 35% of the landscape changed; 75–80% of this change was due to decline in housing density. This decline was implausible, however, because housing structures generally persist over time. Based on aggregated boundaries, 11% of the landscape changed, but only 4% experienced a decline in housing density. Nevertheless, the housing density change map was almost twice as coarse spatially as the 2000 housing density data. We also applied a dasymetric approach to redistribute 1990 housing data into 2000 census boundaries under the assumption that the distribution of housing in 2000 reflected the same distribution as in 1990. The dasymetric approach resulted in conservative change estimates at a fine resolution. All methods involved some type of trade‐off (e.g. analytical difficulty, data resolution, magnitude or bias in direction of change). However, our dasymetric procedure is a novel approach for assessing housing growth over changing census boundaries that may be particularly useful because it accounts for the uniquely persistent nature of housing over time.