Scale variance analysis coupled with Moran's I scalogram to identify hierarchy and characteristic scale

Scale variance is highly sensitive to multi-scale patterns of variables, which is advantageous in identifying spatial hierarchy and characteristic scale(s). However, the significance of peak(s) in scale variance cannot be statistically tested, and different spatial patterns may be obtained when different zoning systems are used to calculate scale variance. To address these two problems, this study compared the scale levels with peaks in scale variance and the scale levels at which there were breaks in the nature of spatial autocorrelation as identified by shifts in Moran's I scalogram. The estimates for three simulated landscapes showed that accordance between scale levels identified employing the two methods can be used to evaluate the significance of peaks in scale variance and choose a more reasonable zoning system. The approach of scale variance analysis coupled with Moran's I scalogram was also applied to the Xilin River Basin of Inner Mongolia, China. The most vital characteristic scale (64 × 32 km) identified for the growing-season net ecosystem productivity (NEP) of the basin was validated by other spatial pattern analysis methods such as semi-variogram, Moran's I correlogram, and wavelet variance analyses, and the directionality of the chosen zoning systems was found to be similar to the orientation of actual dominant vegetation type patches. The results demonstrate that Moran's I scalogram can be used to improve the interpretation of the results of scale variance analysis and increase the reliability of scale variance analysis for landscapes having a repetitive patch pattern or gradient variation and that the proposed approach is suitable for identifying the hierarchy and the characteristic scales of patterns or processes. In summary, this study used a simple approach to solve two problems in scale variance analysis, thereby improving the methodology and enhancing the theoretical basis of multi-scale analysis.     

Landscapes of Risk: The Geography of Fatal Traffic Collisions in Indiana, 2003 to 2011

Potential death from traffic collisions is a common hazard faced by nearly everyone. This analysis uses geographical approaches to examine fatal collision risk in the state of Indiana from 2003 to 2011. The state averaged 757 fatal collisions annually. About 60 percent occurred in urban or suburban areas. Risk was highest in low-density exurban and rural areas and on highways, county roads, and interstates. County sheriffs and state police were more likely to face fatal collisions than municipal police. Significant black spots existed around the Illinois and Kentucky state lines. Fatal collisions were less clustered than nonfatal collisions. Fatal collisions exhibited positive spatial autocorrelation but the global Moran's I for counties was close to random. Approximately 10 percent of ninety-two counties had significant local spatial autocorrelation in fatal collision rates. Police and emergency response resource distributions should be considered in light of fatal collision black spots in the state.     

Testing local spatial autocorrelation using k-order neighbours

The analysis of local spatial autocorrelation for spatial attributes has been an important concern in geographical inquiry. In this paper, we propose a concept and algorithm of k-order neighbours based on Delaunay's triangulated irregular networks and redefine Getis and Ord's (1992) local spatial autocorrelation statistic as G_i(k) with weight coefficient w_ij(k) based on k-order neighbours for the study of local patterns in spatial attributes. To test the validity of these statistics, an experiment is performed using spatial data of the elderly population in Ichikawa City, Chiba Prefecture, Japan. The difference between the weight coefficients of the k-order neighbours and distance parameter to measure the spatial proximity of districts located in the city centre and near the city limits is found by Monte-Carlo simulation.     

A Review of “Hot,Flat and Crowded: Why We Need a Green Revolution—And How It Can Renew America”

Based on a county-level Chinese industry survey data set, this article aims to extend the agglomeration literature by applying and comparing selected combination indexes of geographical concentration that incorporate both traditional indexes of inequality and measures of spatial autocorrelation at the global level and by applying and comparing a new measure, the focal location quotient (FLQ), to the local Moran's I, a commonly used local indicator of spatial association, at the county level. At the global level, the results show that the combination indexes used are generally effective for comparing the extent of geographical concentration across industries, and they could serve as useful dependent variables in modeling agglomeration effects across industries. At the local level, specific spatial patterns of production concentrations are identified for textiles, machinery, food manufacturing, and the electronics and telecommunication industries. FLQ tends to generate more generalized patterns than does the local Moran statistic. Mapping the local statistics is useful in supplementing the global measures, and those maps tend to support the results of the global combination indexes.     

A GIS-based risk rating of forest insect outbreaks using aerial overview surveys and the local Moran's I statistic

The objective of this study is to provide an approach for assessing the short-term risk of mountain pine beetle Dendroctonus ponderosae Hopkins (Coleoptera: Scolytidae) attack over large forested areas based on the spatial-temporal behavior of beetle spread. This is accomplished by integrating GIS, aerial overview surveys, and local indicators of spatial association (LISA) in order to measure the spatial relationships of mountain pine beetle impacts from one year to the next. Specifically, we implement a LISA method called the bivariate local Moran's I_i to estimate the risk of mountain pine beetle attack across the pine distribution of British Columbia, Canada. The bivariate local Moran's I_i provides a means for classifying locations into separate qualitative risk categories that describe insect population dynamics from one year to the next, revealing where mountain pine beetle populations are most likely to increase, stay constant, or decline. The accuracy of the model's prediction of qualitative risk was higher in initial years and lower in later years of the study, ranging from 91% in 2002 to 72% in 2006. The risk rating can be continually updated by utilizing annual overview surveys, thus ensuring that risk prediction remains relatively high in the short-term. Such information can equip forest managers with the ability to allocate mitigation resources for responding to insect epidemics over very large areas.     

Incorporation of multi-scale spatial autocorrelation in soil moisture–landscape modeling

Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R², Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.     

A Review of “Turf Wars: Discourse,Diversity, and the Politics of Place”

The spatial correlation, or colocation, of two or more variables is a fundamental issue in geographical analysis but has received much less attention than the spatial correlation of values within a single variable, or autocorrelation. A recent paper by Leslie and Kronenfeld (2011) contributes to spatial correlation analysis in its development of a colocation statistic for categorical data that is interpreted in the same way as a location quotient, a frequently used measure in human geography and other branches of regional analysis. Geographically weighted colocation measures for categorical data are further developed in this article by generalizing Leslie and Kronenfeld's global measure as well as specifying a local counterpart for each global statistic using two different types of spatial filters: fixed and adaptive. These geographically weighted colocation quotients are applied to the spatial distribution of housing types to demonstrate their utility and interpretation.     

洞庭湖区生态承载力时空演化特征

利用探索性空间数据分析以及百分位数和变异系数,基于Arc GIS 和Geo DA软件支撑,对洞庭湖区生态承载力的空间格局特征、空间关联特征和相对差异演变特征进行分析。结果表明：① 空间格局上,洞庭湖区生态承载力从西南向东北大体上呈现“W”型（较高—低—高—低—较高）空间格局,倒“U”型的县域差异特征显著。② 空间关联上,洞庭湖区生态承载力的分布呈正的全局空间自相关,出现相似县域之间的空间集聚,全局Moran’s Ⅰ值相差不大,空间聚集程度比较稳定,趋于围绕某一偏低集聚度出现小范围内波动;局部空间自相关的LISA 集聚类型出现HH型、HL型和LH型3 种类型,依次集中分布在洞庭湖区的中部、西南部和东北部,局部空间格局保持相对稳定,但是聚类特征显现出稍微变化的迹象。③ 相对差异演变上,2001-2010 年洞庭湖区生态承载力相对差异演变整体上呈现一定的差异性。2001-2003 年相对差异较小,趋于稳定;2005 年前后相对差异较大;相比2001-2003 年,2005 年以来相对差异明显扩大且这种较大的空间相对差异随着时间的推移呈现小幅波动趋势。     

A spatial approach to select pilot counties for programs to correct the biased sex ratio at birth in Shandong province,China

The highly skewed sex ratio at birth (SRB) in China has stimulated numerous studies. However, the geographic distribution of SRB is seldom investigated, particularly at the county level. The need for an understanding at this level has increased since the Chinese government initiated its ‘Care for Girls’ campaign to improve the survival rate of females. This campaign has been initiated in a set of pilot counties. In this article we assess the effectiveness of the set of pilot counties in Shandong province and propose two alternate configurations. To do this, we first assess the spatial distribution of the SRB values by county in Shandong, expressed as a z-score (zSRB) after correcting for the biologically expected SRB value and population size of zero-aged children. A local Moran's I_i analysis of the zSRB values indicates a significant high–high cluster in the southwest of the province. The I_i , zSRB and female deficit (the difference of the observed from biologically expected number of zero-aged females) were then used to define two alternate configurations for the pilot counties. A comparison of the current and alternate configurations against a Monte Carlo randomisation analysis shows that the current configuration is significantly different from a random selection (p < 0.05) for the two criteria of maximising the aggregate female deficit and maximising the zSRB. Although this is a good result, both alternate configurations were more significant (p < 0.001), and therefore represent potentially better configurations for the campaign given the criteria used. The spatial analysis approach developed here could be used to improve the effectiveness of the Care-for-Girls campaign in Shandong province, and elsewhere in China.     

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B_cont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. B_cont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (?0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.     

Measuring Global Spatial Autocorrelation with Data Reliability Information

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation. Key Words: American Community Survey, Geary ratio, Moran’s I, permutation test, spatial Bhattacharyya coefficient.     

Exploring population spatial concentrations in Northern Ireland by community background and other characteristics: an application of geographically weighted spatial statistics

Information on how populations are spatially concentrated by different characteristics is a key means of guiding government policies in a variety of contexts, in addition to being of substantial academic interest. In particular, to reduce inequalities between groups, it is necessary to understand the characteristics of these groups in terms of their composition and their geographical structure. This article explores the degree to which the population of Northern Ireland is spatially concentrated by a range of characteristics. There is a long history of interest in residential segregation by religion in Northern Ireland; this article assesses population concentration not only by community background (‘religion or religion brought up in’) but also by housing tenure, employment and other socioeconomic and demographic characteristics. The spatial structure of geographical variables can be captured by a range of spatial statistics including Moran's I. Such approaches utilise information on connections between observations or the distances between them. While such approaches are conceptually an improvement on standard aspatial statistics, a logical further step is to compute statistics on a local basis on the grounds that most real-world properties are not spatially homogenous and, therefore, global measures may mask much variation. In population geography, which provides the substantive focus for this article, there are still relatively few studies that assess in depth the application of geographically weighted statistics for exploring population characteristics individually and for exploring relations between variables. This article demonstrates the value of such approaches by using a variety of geographically weighted statistical measures to explore outputs from the 2001 Census of Population of Northern Ireland. A key objective is to assess the degree to which the population is spatially divided, as judged by the selected variables. In other words, do people cluster more strongly with others who share their community background or others who have a similar socioeconomic status in some respect? The analysis demonstrates how geographically weighted statistics can be used to explore the degree to which single socioeconomic and demographic variables and relations between such variables differ at different spatial scales and at different geographical locations. For example, the results show that there are regions comprising neighbouring areas with large proportions of people from the same community background, but with variable unemployment levels, while in other areas the first case holds true but unemployment levels are consistently low. The analysis supports the contention that geographical variations in population characteristics are the norm, and these cannot be captured without using local methods. An additional methodological contribution relates to the treatment of counts expressed as percentages.     

Analysing telemetry data with a GIS-based vector structure

Abstract

This paper proposes a unique method for plotting field bearing locations, of the kind typically taken by wildlife biologists on free ranging species, directly on a computer-compatible habitat map. We show how to use a GIS data base to identify differential habitat use directly from the polygon formed by each set of bearings. A geometric algorithm is developed to interpret the bearings accurately. The technique avoids the most difficult errors associated with using point locations, namely those due to animal movement, and distance from receiver to transmitter, and is especially useful for habitat preference studies.     

Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain)

Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.     

Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS

Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system (GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period (May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters. Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient of determination (r ² ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater augmentation techniques need to be implemented to protect depleting groundwater resources.     

Fitzgerald: A Return to the Neighborhood and Its Contemporary Structural and Geographical Contexts

William Bunge's Fitzgerald: Portrait of a Revolution, initially published in 1971, is an enthralling verbal and visual account of the historical and geographical development of a one-square-mile neighborhood in Detroit. The original analysis of the Fitzgerald neighborhood was based on intensive field-based research conducted in a theoretical context of race and racism. The research reported here maintains that context but updates Fitzgerald's account of the neighborhood's built environment through a spatial analysis that uses parcel-by-parcel data generated in Google Earth and Google Street View instead of data collected in the field. Current spatial patterns of deterioration in the built environment are similar to those described in Fitzgerald, but positive sites are also apparent and often colocated with negative ones.     

Observations of dugongs at Aldabra Atoll,western Indian Ocean: lagoon habitat mapping and spatial analysis of sighting records

Until recently, it was thought that dugongs (Dugong dugon) were extinct in the Seychelles. However, a collection of sightings at Aldabra Atoll, a World Heritage Site in the Seychelles, has renewed interest in dugong distribution in the western Indian Ocean. This article consolidates the records of dugong sightings held in the Aldabra Research Station library and explores their spatial patterning. The locations of sightings (2001–2009) are plotted onto a high-resolution benthic habitat map of the Aldabra lagoon created by classifying a QuickBird satellite remote-sensing image in January 2009. A spatial cluster detection procedure is applied to point records of sightings to reveal a statistically significant cluster of sightings in the north-west of the lagoon, at Bras Monsieur Clairemont, suggesting a mutual co-existence of dugongs and seagrass beds. A habitat suitability model combines the point data set of dugong sightings within the continuous benthic habitat map and identifies the central western area as containing the most suitable habitat for dugong inside the Aldabra lagoon.     

Potential habitat of Acropora spp. on Florida reefs

Elkhorn and staghorn corals (Acropora palmata, Acropora cervicornis) were listed as threatened species under the Endangered Species Act in 2005. The decline of these species beginning in the late 1970s is unprecedented given the vital role they historically played as major builders of western Atlantic and Caribbean coral reefs. The goal of this study was to create potential-habitat maps for A. palmata and A. cervicornis that would show areas in which these species currently exist, as well as areas that would be suitable for their (re)establishment, using a database of reported in situ observations and existing mapped data. Using the mapped coral reef and hardbottom classifications throughout the Florida reef tract, potential-habitat maps were generated using buffers that incorporated 95% and 99% of reported observations of colonies of Acropora spp. The potential-habitat maps were produced based on benthic substrates throughout the Florida reef tract using GIS software. Locations of 99% of A. palmata observations and 84% of A. cervicornis observations coincided with previously mapped coral reef or hardbottom habitat. These results indicate that potential habitat for A. palmata is currently well defined and that potential habitat for A. cervicornis is more variable and has a wider range than that for A. palmata. This study provides a novel method of combining datasets at various geographic spatial scales and may be used to inform the current NOAA critical habitat map. One of the most important differences between the current NOAA critical habitat map and the new potential-habitat map is observed in the southeast Florida region, where A. cervicornis appears to be thriving outside of mapped reef areas and at latitudes considered marginal for hermatypic corals. Thus, the potential habitat extends further north than the previous critical habitat - and encompasses additional habitat for A. cervicornis and potentially for A. palmata.     

Do foreign subsidiaries innovate and perform better in a cluster? A spatial analysis of Japanese subsidiaries in the US

The interrelationship between clustering, innovation, and performance of Japanese subsidiaries in the US is examined in this paper. First, we apply Kulldorff's spatial scan statistic to identify process innovation clustering among Japanese subsidiaries. The scale at which clustering occurs is regional rather than local, and cluster location captures the geographical environment of home-transplants from Japan. Second, we apply spatial autoregressive models to test the relationship between cluster location and innovation among Japanese subsidiaries. The results indicate that cluster location strengthens the relationship between subsidiary innovation and firm performance. Negative spatial autocorrelation shows that clustering contributes to innovation by facilitating proximate learning between less innovative laggards, and, leading innovative Japanese subsidiaries.