Selecting conservation reserves using species-covering models: Adapting the ARC/INFO GIS

Conflicts between human development of the landscape and conservation of biodiversiry will continue to grow. Given this reality, there have been a number of attempts to model the optimal selection of conservation reserve sites such that maximum biodiversity protection can be attained within a limited budget for land acquisition. Here we adapt the Location-Allocation module of ARC/INFO to solve the problem of representing, or covering, as many species as possible in a fixed number of selected reserve sites. Resident ARC/INFO solution routines are applied to an innovative 'logical' network that converts the problem of optimal reserve selection into a problem of optimal facility placement, which the Location-Allocation module can recognize and solve. Use of this unique logical network structure as input to ARC/INFO's internal solvers makes possible, compared to previous methods, a much tighter integration of spatial optimization tools with mapping and database tools, all of which are internal to the GIS and accessed via a menu-driven interface. The main advantage is that users of public domain data (such as the US Gap Analysis data) can conduct their own explorations of possible reserve systems without having to acquire and master optimization packages and reformat model output data for GIS display and post-analysis of solutions. Our sample application uses species data from south-western California. We also present a second major form of species-covering model grounded in the same logical network. This enhanced modcl accommodates weighting of species by their conservation importance, thus allowing reserve systems to be designed around the protection of the most threatened or vulnerable biota.     

A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

Topology-based proximities in spatial systems

This paper introduces several topology-based measures that characterise proximity relationships between regions in a spatial system. These measures are derived from a relative adjacency operator that is computed from the dual graph of a spatial system. The operator is flexible as the respective importance of neighbouring and outlying regions can be parameterised. Given a reference region in a spatial system, we also show how the relative adjacency supports the analysis of the relative distribution of other regions, and how these regions are clustered with respect to that reference region. Extensions of the relative adjacency integrate additional spatial and thematic criteria. The properties of the relative adjacency are illustrated by means of reference examples and a case study.The authors thank the referees for their constructive comments and suggestions that significantly improved the quality of the paper.     

Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area,China

Urban sprawl has led to environmental problems and large losses of arable land in China. In this study, we monitor and model urban sprawl by means of a combination of remote sensing, geographical information system and spatial statistics. We use time-series data to explore the potential socio-economic driving forces behind urban sprawl, and spatial models in different scenarios to explore the spatio-temporal interactions. The methodology is applied to the city of Wuhan, China, for the period from 1990 to 2013. The results reveal that the built-up land has expanded and has dispersed in urban clusters. Population growth, and economic and transportation development are still the main causes of urban sprawl; however, when they have developed to certain levels, the area affected by construction in urban areas (Jian Cheng Qu (JCQ)) and the area of cultivated land (ACL) tend to be stable. Spatial regression models are shown to be superior to the traditional models. The interaction among districts with the same administrative status is stronger than if one of those neighbors is in the city center and the other in the suburban area. The expansion of urban built-up land is driven by the socio-economic development at the same period, and greatly influenced by its spatio-temporal neighbors. We conclude that the integration of remote sensing, a geographical information system, and spatial statistics offers an excellent opportunity to explore the spatio-temporal variation and interactions among the districts in the sprawling metropolitan areas. Relevant regulations to control the urban sprawl process are suggested accordingly.     

Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows

Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration) and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes how to implement the methodology utilized to analyze a particular empirical flows dataset.

Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales.     

Delaunay三角网支持下的空间场表达

不规则三角网TIN本质上属于基于场的空间数据模型,然而在GIS领域,通常将其局限于DTM数字地形模型的表达中,没有象规则栅格模型那样在平面实体及实体关系表达中发挥足够的作用。针对这一局限性,仿照规则格网的栅格数据模型,应用DELAUNAY三角网工具,建立一种面向平面空间场表达的形式化数据模型。应用三角形的3种基元:顶点、边、三角形面表达空间点、线、面目标,定义该模型上的3种操作:扩充、收缩与骨架化,并进一步推广到序惯操作与条件操作。在几个应用实例基础上,分析该模型在空间邻近关系表达上可发挥重要作用。     

Modeling network autocorrelation within migration flows by eigenvector spatial filtering

Although the assumption of independence among interaction flows frequently is engaged in spatial interaction modeling, in many circumstances it leads to misspecified models and incorrect inferences. An informed approach is to explicitly incorporate an assumed relationship structure among the interaction flows, and to explicitly model the network autocorrelation. This paper illustrates such an approach in the context of U.S. interstate migration flows. Behavioral assumptions, similar to those of the intervening opportunities or the competing destinations concepts, exemplify how to specify network flows that are related to particular origin–destination combinations. The stepwise incorporation of eigenvectors, which are extracted from a network link matrix, captures the network autocorrelation in a Poisson regression model specification context. Spatial autocorrelation in Poisson regression is measured by the test statistic of Jacqmin-Gadda et al. (Stat Med 16(11):1283–1297, 1997). Results show that estimated regression parameters in the spatial filtering interaction model become more intuitively interpretable.

Predicting road system speeds using spatial structure variables and network characteristics

Spatial regression is applied to GPS floating car measurements to build a predictive model of road system speed as a function of link type, time period, and spatial structure. The models correct for correlated spatial errors and autocorrelation of speeds. Correlation neighborhoods are based on either Euclidean or network distance. Econometric and statistical methods are used to choose the best model form and statistical neighborhood. Models of different types have different coefficient estimates and fit quality, which might affect inferences. Speed predictions are validated against a holdout sample to illustrate the usefulness of spatial regression in road system speed monitoring.      

基于元模式的空间关联规则挖掘方法

空间关联规则可以从空间数据库中发现空间对象之间的隐含的、不明确的空间关系。空间数据库中空间关联规则挖掘研究是空间数据挖掘和知识发现(SDMKD)的主要内容,其主要问题是多层,多关系的规则发现问题。本文把元模式与空间关联规则挖掘相结合介绍了挖掘地理信息系统(GIS)中空间数据库关联规则的常用步骤方法。     

基于Oracle Spatial和SVG的空间信息发布研究

针对目前Web GIS空间数据的发布现状．根据Oracle Spatial和SVG各自的特性，给出了一种应用Spatial和SVG进行Web GIS空间信息发布的方法，设计并实现了一个空间信息发布组件。     

Hilbert曲线层级演进关系

网格编码代数是当前全球空间网格研究中的难点,也是推动全球空间网格从数据组织管理框架发展成为新一代空间分析模型的理论基础。Hilbert曲线是设计全球空间网格单元编码的重要工具。本文以Hilbert曲线序列码作为八叉树立体网格单元的唯一编码,由Hilbert曲线数学性质推导得到网格单元Hilbert码的层级演进关系表,进而设计了用于网格编码代数运算的若干操作算子,为构建全球空间网格分析理论与方法提供参考。     

Mapping local regression for spatial object-pairs

Local regression methods such as geographically weighted regression (GWR) can provide specific information about individual locations (or places) in spatial analysis that is useful for mapping nonstationary covariate relationships. However, the distance-based weighting schemes used in GWR are only adaptable for spatial objects that are point or area features. In particular, spatial object-pairs pose a challenge for local analysis because they have a linear dimensionality rather than a point dimensionality. This paper proposes using an alternative local regression model – quantile regression (QR) – for investigating the stationarity of regression parameters with respect to these linear features as well as facilitating the visualization of the results. An empirical example of a gravity model analysis of trade patterns within Europe is used to illustrate the utility of the proposed method.     

Spatial autocorrelation of West Nile virus vector mosquito abundance in a seasonally wet suburban environment

The objective of this study is to quantify and model spatial dependence in mosquito vector populations and develop predictions for unsampled locations using geostatistics. Mosquito control program trap sites are often located too far apart to detect spatial dependence but the results show that integration of spatial data over time for Cx. pipiens-restuans and according to meteorological conditions for Ae. vexans enables spatial analysis of sparse sample data. This study shows that mosquito abundance is spatially correlated and that spatial dependence differs between Cx. pipiens-restuans and Ae. vexans mosquitoes.      

WebGIS中的空间数据库模型的研究

目前由于Internet带宽的限制,WebGIS对空间数据的访问是WebGIS发展的瓶颈。因此为满足WebGIS对空间数据的统一管理和运行速度这两个方面的要求,该文实践了一种基于扩展改进的空间数据库模型,并提出了一种新的空间数据索引模型,通过一系列的实验验证了这种模型满足WebGIS需求,最后将该模型在WebGIS中得到了原型实现。     

Deconstructing Galbraith: A Geostatistical Analysis of Cartographic Intent

Abstract

This study uses cartometric techniques to reveal cartographic design considerations operative in Frank H. Galbraith’s 1897 railway mail service training maps. Galbraith’s maps make extensive use of humorous cartouches intended to serve as mnemonic devices for learning railway mail service routes. Poisson and quadrat point pattern analyses are performed on all eight maps in the series to determine whether the mimetic symbols are clustered along the railway mail routes or dispersed throughout the mapped spaces to achieve better graphic balance. Evidence overwhelmingly indicates that the placement of the mnemonic symbols was primarily driven by design considerations. The study therefore proves that it is possible to use cartometric techniques to deconstruct the map document to determine the cartographic design principles operative during the map's construction.     

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.     

Automatic interpretation of digital maps

In the past, the availability and/or the acquisition of spatial data were often the main problems of the realization of spatial applications. Meanwhile this situation has changed: on one hand, comprehensive spatial datasets already exist and on the other hand, new sensor technologies have the ability to capture fast and with high quality large amounts of spatial data. More and more responsible for the increasing accessibility of spatial data are also collaborative mapping techniques which enable users to create maps by themselves and to make them available in the internet. However, the potential of this diversity of spatial data can only hardly be utilized. Especially maps in the internet are represented very often only with graphical elements and no explicit information about the map’s scale, extension and content is available. Nevertheless, humans are able to extract this information and to interpret maps. For example, it is possible for a human to distinguish between rural and industrial areas only by looking at the objects’ geometries. Furthermore, a human can easily identify and group map objects that belong together. Also the type, scale and extension of a map can be identified under certain conditions only by looking at the objects’ geometries. All these examples can be subsumed under the term “map interpretation”. In this paper it is discussed how map interpretation can be automated and how automatic map interpretation can be used in order to support other processes. The different kinds of automatic map interpretation are discussed and two approaches are shown in detail.     

The development of an intervening opportunities model with spatial dominance effects

In this paper an intervening opportunities model with spatial dominance is developed. The usual assumption in spatial theory is that decision makers are influenced not just by the size of a destination or distance but by these two factors in combination, that is, spatial dominance. Decision-makers will have more knowledge about, and clearly perceive destinations that exert the greatest amount of spatial dominance on their origins, just as they would primate cities. Thus destinations are ranked in terms of the spatial dominance calculated for each destination. Empirical verification of the model utilizes state-to-state migration flow data for the US. Calibration results compare favourably with the traditional intervening opportunities model and the production constrained gravity model. Received: 7 October 1999/Accepted: 15 June 2000     

空间信息网格的若干问题分析

网格的出现为当代的空间信息科学带来了发展机遇,也对空间信息科学提出了新的挑战。将网格技术引入到空间信息的处理和应用中来,成为测绘工作者必须面对的重要课题和任务。本文系统分析了空间信息网格的概念、开展空间信息网格研究的必要性以及空间信息网格的特性,同时提出了面向服务的空间信息网格体系模型。