Towards an integrated science of movement: converging research on animal movement ecology and human mobility science

There is long-standing scientific interest in understanding purposeful movement by animals and humans. Traditionally, collecting data on individual moving entities was difficult and time-consuming, limiting scientific progress. The growth of location-aware and other geospatial technologies for capturing, managing and analyzing moving objects data are shattering these limitations, leading to revolutions in animal movement ecology and human mobility science. Despite parallel transitions towards massive individual-level data collected automatically via sensors, there is little scientific cross-fertilization across the animal and human divide. There are potential synergies from converging these separate domains towards an integrated science of movement. This paper discusses the data-driven revolutions in the animal movement ecology and human mobility science, their contrasting worldviews and, as examples of complementarity, transdisciplinary questions that span both fields. We also identify research challenges that should be met to develop an integrated science of movement trajectories.     

Incorporating behavior into animal movement modeling: a constrained agent-based model for estimating visit probabilities in space-time prisms

ABSTRACT

Animal movement is a dynamic spatio-temporal process. While trajectory data reflect the instantaneous animal position in space and time, other factors influence movement decisions between these observed positions. While some methods incorporate environmental (habitat) context into their understanding of the animal movement process, it is often captured in terms of simple parameters or weights influencing model results; primary behavioral data are not used directly to inform these models. Here, a new space-time constrained agent-based model is introduced, capable of producing ordered, behaviorally informed animal potential paths between observed space-time anchors. Potential paths generated by this approach incorporate both observed animal behavior and classical space-time constraints, and are used to construct associated visit probability distributions. Additionally, the notion of a behavioral space-time path is introduced, a variant of the space-time path based on the results of behaviorally aware animal movement simulation. The results of this approach demonstrate a means to better understand the varied movement opportunities within space-time prisms from an animal behavior perspective. From a spatial ecology perspective, not only is the environmental context considered, but the animal’s choice of transition and movement magnitude between contexts is modeled. This approach provides insight into the complex sequence of behaviorally informed actions driving animal movement decision-making.     

CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel

ABSTRACT

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features present akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that identifies these critical barriers and corridors. We have built this approach into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).     

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.     

Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships

ABSTRACT

Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.     

GIS goes nano: Vegetation studies in Victoria Land, Antarctica

Abstract: Vegetation in Antarctica consists mainly of algae, moss and lichen and is interesting to research because of the isolation and extreme growing conditions. An understanding of this vegetation is important for both the management of tourism in Antarctica, and because it provides a potential barometer of global climate and environmental change. This paper demonstrates two applications of GIS to mapping vegetation in Victoria Land, Antarctica. The first application computes the changes that have occurred to the vegetation within a 120 m by 28 m plot between 1962 and 2004. The second application maps and analyses the growth of a lichen specimen (Buellia frigida) during the same period. These applications demonstrate that GIS is a multiscale technology that can be used to detect detailed change in vegetation growth.     

Simulating Movement-Related Resource Dynamics to Improve Species Distribution Models: A Case Study with Oilbirds in Northern South America

A better understanding of the current and future distributions of organisms is a critical facet of biodiversity conservation, and species distribution models (SDMs) are an important framework for achieving this. Despite the potential of SDMs to address an array of biogeography questions, they are subject to a number of conceptual and methodological uncertainties, such as the role of animal movement processes in determining geographic ranges. Movement processes have only recently been incorporated in SDMs, predominantly conceptualized as broad-scale movement processes (e.g., dispersal), while finer scale ambulatory movements of mobile animals (e.g., foraging) have been omitted. This research addresses this gap by developing a model that simulates the dynamic relationship between movement and biotic resources (e.g., food sources) for oilbirds (Steatornis caripensis) in Venezuela. This simulation represented the sustainability of an oilbird’s neighborhood, based on the connectivity, accessibility, and viability of its biotic resources. These dynamic variables improved the accuracy and ecological realism of the SDM projection compared to other commonly applied SDM scenarios. Integration of a Lagrangian (individual-level) form of movement in SDM with step-selection functions to parameterize biased-correlated random walks provides a new empirical framework for applying geographic context to simulation.     

Deferred decentralized movement pattern mining for geosensor networks

This article presents an algorithm for decentralized (in-network) data mining of the movement pattern flock among mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to ‘graze’ over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge.     

An analysis on movement patterns between zones using smart card data in subway networks

Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by using an agglomerative clustering method. In addition, evaluation measures of MZPs are suggested in terms of coverage and accuracy. The effectiveness of the proposed approach is finally demonstrated through a real-world data set obtained from smart cards on a subway network in Seoul, Korea.     

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity

ABSTRACT

The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method.     

Mining candidate causal relationships in movement patterns

In many applications, the environmental context for and drivers of movement patterns are just as important as the patterns themselves. This article adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between movement patterns and their environmental context. In addition to data about movement and its dynamic environmental context, our approach requires as input definitions of the states and events of interest. The technique outputs causal and causal-like relationships of potential interest, along with associated measures of support and confidence. As a validation of our approach, the analysis is applied to real data about fish movement in the Murray River in Australia. The results demonstrate that the technique is capable of identifying statistically significant patterns of movement indicative of causal and causal-like relationships.     

基于RS和GIS的区域人类活动强度空间模拟

人类活动强度的定量化和空间化是研究人类活动对生态环境影响的重点和难点,传统的统计方法难以实现空间定量化,而忽略社会经济统计数据的空间模拟方法获得的人类活动强度也存在缺陷。文中基于遥感、交通、地形和统计数据,探讨了综合应用统计、遥感和GIS方法模拟区域人类活动强度的方法,并应用该方法对衡阳盆地人类活动强度进行了模拟。定性分析结果表明,该方法能够较准确地获取区域人类活动强度空间分布。     

Movement similarity assessment using symbolic representation of trajectories

This article describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters (MPs) such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular MP. Each segment is assigned to a movement parameter class (MPC), representing the behavior of the MP. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance called normalized weighted edit distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques and that compare our NWED measure to a related method.     

From A to B,randomly: a point-to-point random trajectory generator for animal movement

For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines the concepts of random walks, space-time prisms, and the Brownian bridge movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-)continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges.     

定性GIS在时空间行为研究中的应用

定性GIS已经成为地理学混合研究方法的重要组成部分,通过扎根理论并与可视化结合、GIS和民族志整合等途径,对时空间行为研究产生了深远的影响。其通过文本或非文本质性材料与GIS地理特征的整合,形成了时空行为研究中独特的地理环境系统与空间机制分析,一个重要的前沿领域就是地理叙事在时间地理学研究中的应用。时间地理学是研究人类时空行为的重要视角,但由于对个体主观性及行为的社会文化因素的相对忽视等原因,受到了一定的质疑。近年来以GIS为代表的地理空间技术的定性分析与表达,为时间地理学融入质性分析提供了方法论基础。关美宝创建的基于GIS的地理叙事方法,通过借鉴叙事分析的一般方法尤其是三维叙事空间分析,结合时间地理学概念框架,开发了基于GIS平台的计算机辅助叙事分析组件(3D-VQGIS),实现了在GIS内可视化的、循环交互的叙事分析,为定性GIS发展中GIS与叙事分析方法的融合做出了贡献。将地理叙事方法用于时空行为研究,可以为时间地理学方法创造一个更便于理解与解释研究对象日常行为的地理环境,为其融入行为与主体的质性分析提供从数据整合、数据分析到解释与展示的一整套研究方法与具体操作工具。     

Geographic information systems and health: An educational resource

Abstract

The use of geographic information systems to analyze spatial dimensions of health care and disease ecology is becoming a realistic prospect for investigators in the social sciences. This review of the literature, bringing together a diverse collection of professional and academic journals, can be grouped into four categories: potential, cautionary, preliminary, and application. Enough references have been collected and reviewed to provide instructors with material for a classroom unit about 1) using GIS in a medical geography class: 2) medical applications in a GIS class; or 3) using GIS in classes which have a health and disease component.     

干旱区沙质草地植被覆盖变化模拟: Ⅰ.模型

 在干旱、半干旱地区,影响沙质草地植被覆盖的因素主要包括土壤水分、植被生长和风沙运动。根据这三方面的已有研究成果,建立了植被覆盖变化概念模型和数学模型。模型包括栅格水流模型、径流模型、地下水补给模型、土地生产潜力模型等。结合GIS技术,该模型可用于模拟干旱区沙质草地的土壤水分补给量变化、流沙面积变化和植被覆盖变化的空间分布。     

A spatial analytical perspective on geographical information systems

Abstract

The field of geographical information systems (GIS) is reviewed from the viewpoint of spatial analysis which is the key component of the familiar four-part model of input, storage, analysis and output Input is constrained by the limits of manual methods and problems of ambiguity in scanning. The potential for developments in output is seen to be limited to the query mode of GIS operation, and to depend on abandoning the cartographic model. Discussion of storage methods is organized around the raster versus vector debate and the need to represent two spatial dimensions in one. A taxonomy of GIS spatial analysis operations is presented together with a generic data model. Prospects for implementation are discussed and seen to depend on appropriate scales of organization in national and international academic research.     

An event-based conceptual model for context-aware movement analysis

Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach.