Ecosystem services assessment and compensation costs for installing seaweed farms

In a global context of promotion and expansion of blue growth initiatives, the development of activities such as aquaculture calls for the assessment of the potential impacts on biodiversity at different levels and associated services. This paper presents an assessment of the potential impact of the installation of seaweed farms on ecosystem services and the induced compensation costs. Biophysical and socioeconomic indicators have been developed for helping decision makers to select the most suitable locations. The approach considers a multi-criteria approach based on Geographical Information Systems (GIS) and Habitat Equivalency Analysis (HEA). The former is used to obtain biophysical ecosystem services and socioeconomic indicators and the latter to evaluate the costs required to compensate the loss of cultural and provisioning ecosystem services. A case-study in the Normand-Breton (Saint Malo) Gulf, France, illustrates this method through the analysis of hypothetical locations of seaweed farms. Results highlight the differences between alternative locations regarding biophysical constraints (in terms of distance and depth), potential risks of conflicts with existing uses, impacts on habitats and the ecosystem services delivered, and compensation costs. This case-study illustrates the flexibility of this approach which can be further adapted to include other indicators in order to deliver integrated information to coastal planners.     

Assessing potential spatial and temporal conflicts in Washington's marine waters

This paper uses spatial, temporal, and use-intensity data for 27 major marine uses in Washington waters to feature a method for assessing potential use conflicts in a variety of scenarios. The study represents a first step towards quantifying potential conflicts within Washington’s Marine Spatial Planning (MSP) study area by using a cumulative analysis to highlight high- and low-use intensity areas and the novel Marine Potential Conflict Index (MPCI), which incorporates space, time, and intensity of use, to quantify pairwise potential conflicts between uses. About 10% of pairs of uses do not overlap spatially and are likely compatible with one another. Temporally, the number of uses peaks in July and August and falls during January and February. Additionally, the MPCI identified three important use types with a substantial degree of potential conflict: commercial fishing, tribal fishing, and shipping. External data were used in three cases to ground-truth the analysis, as a limited test of its utility in managing any potential conflict. This article assesses the extent of Washington’s existing marine uses along spatial, temporal, and intensity axes, and more broadly provides a straightforward way to examine potential conflicts between marine uses.     

南海某导管架海洋平台倒塌分析

针对在役老龄导管架平台进行倒塌计算分析,确定极限承载力进而评估老龄导管架的安全裕度。采用非线性有限元方法,考虑平台的波流载荷及桩-土的非线性相互作用,利用SACS软件建立导管架整体三维有限元计算分析模型,并用逐步加载的方式,对南海某导管架平台进行了全过程非线性倒塌分析。计算分析表明,该导管架平台极限强度很高,具有较大的安全裕度;导管架倒塌过程呈逐步破坏形式,先是撑杆屈服,造成局部结构破坏,然后是钢桩发生屈服,降低结构承载力,最后节点逐步失效,造成结构倒塌。揭示了导管架平台结构失效倒塌的机理,给出了倒塌分析的可行方法和步骤。     

Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties.     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each containing a unique quasi-stationary pattern of land cover classes. To achieve this goal, we use a recently developed method which utilizes machine vision techniques. First, the entire National Land Cover Dataset (NLCD) is partitioned into a grid of square-size blocks of cells, called motifels. The size of a motifel defines the spatial scale of a local landscape. The land cover classes of cells within a motifel form a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen–Shannon divergence as a dissimilarity function between patterns we group the motifels into several LPTs. The grouping procedure consists of two phases. First, the grid of motifels is partitioned spatially using a region-growing segmentation algorithm. Then, the resulting segments of this grid, each represented by its medoid, are clustered using a hierarchical algorithm with Ward’s linkage. The broad-extent maps of progressively more generalized LPTs resulting from this procedure are shown and discussed. Our delineated LPTs agree well with the perceptual patterns seen in the NLCD map.     

Estimating spatial efficiency using cyber search,GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County

ABSTRACT

The efficiency of public investments and services has been of interest to geographic researchers for several decades. While in the private sector inefficiency often leads to higher prices, loss of competitiveness, and loss of business, in the public sector inefficiency in service provision does not necessarily lead to immediate changes. In many cases, it is not an entirely easy task to analyze a particular service as appropriate data may be difficult to obtain and hidden in detailed budgets. In this paper, we develop an integrative approach that uses cyber search, Geographic Information System (GIS), and spatial optimization to estimate the spatial efficiency of fire protection services in Los Angeles (LA) County. We develop a cyber-search process to identify current deployment patterns of fire stations across the major urban region of LA County. We compare the results of our search to existing databases. Using spatial optimization, we estimate the level of deployment that is needed to meet desired coverage levels based upon the location of an ideal fire station pattern, and then compare this ideal level of deployment to the existing system as a means of estimating spatial efficiency. GIS is adopted throughout the paper to simulate the demand locations, to conduct location-based spatial analysis, to visualize fire station data, and to map model simulation results. Finally, we show that the existing system in LA County has considerable room for improvement. The methodology presented in this paper is both novel and groundbreaking, and the automated assessments are readily transferable to other counties and jurisdictions.     

考虑地球曲率影响的重力中间层校正计算及其精度分析

介绍一种考虑地球曲率影响的“球冠模型” 重力中间层校正算法,并通过不同算例对比该算法与以往“圆柱体模型”算法之间的差值。结果表明,其差值的数量级最大可达0.4 mGal。     

Visualising 30 Years of Population Density Change in Australia’s Major Capital Cities

ABSTRACT

This study uses a novel spatial approach to compare population density change across cities and over time. It examines spatio-temporal change in Australia’s five most populated capital cities from 1981 to 2011, and documents the established and emerging patterns of population distribution. The settlement patterns of Australian cities have changed substantially in the last 30 years. From the doughnut cities of the 1980s, programs of consolidation, renewal and densification have changed and concentrated population in our cities. Australian cities in the 1980s were characterised by sparsely populated, low density centres with growth concentrated to the suburban fringes. ‘Smart Growth’ and the ‘New Urbanism’ movements in the 1990s advocated higher dwelling density living and the inner cities re-emerged, inner areas were redeveloped, and the population distribution shifted towards increased inner city population densities. Policies aimed at re-populating the inner city dominated and the resultant changes are now visible in Australia’s five most populated capital cities. While this pattern has been reported in a number of studies, questions remain regarding the extent of these changes and how to analyse and visualise them across urban space. This paper reports on a spatial method which addresses the limitations of changing statistical boundaries to identify the changing patterns in Australian cities over time and space.     

“To Understand the Place”: Geographical Knowledge and Diplomatic Practice

Diplomats produce a great deal of geographical knowledge: By reporting on distant places to their governments, they create particular understandings of political space. Yet these professionals rarely link their work to geography: What a geographer might call a geographical sensibility is labeled cultural knowledge by a diplomat. This article clarifies the relationship between geographical knowledge and diplomatic practice. Empirically, it draws from nearly 100 interviews with foreign policy professionals to offer a more “peopled” or quasi-ethnographic account of diplomacy than is usually available in scholarly literature. Conceptually, the article contributes to our understanding of how geographical knowledge is created inside diplomatic and bureaucratic institutions.