Individual Movement - Sequence Analysis Method (IM-SAM): characterizing spatio-temporal patterns of animal habitat use across landscapes

ABSTRACT

We present methodological advances to a recently developed framework to study sequential habitat use by animals using a visually-explicit and tree-based Sequence Analysis Method (SAM), derived from molecular biology and more recently used in time geography. Habitat use sequences are expressed as annotations obtained by intersecting GPS movement trajectories with environmental layers. Here, we develop IM-SAM, where we use the individual reference area of use as the reference spatial context. To assess IM-SAM’s applicability, we investigated the sequential use of open and closed habitats across multiple European roe deer populations ranging in landscapes with contrasting structure. Starting from simulated sequences based on a mechanistic movement model, we found that different sequential patterns of habitat use were distinguished as separate, robust clusters, with less variable cluster size when habitats were present in equal proportions within the individual reference area of use. Application on real roe deer sequences showed that our approach effectively captured variation in spatio-temporal patterns of sequential habitat use, and provided evidence for important behavioral processes, such as day-night habitat alternation. By characterizing sequential habitat use patterns of animals, we may better evaluate the temporal trade-offs in animal habitat use and how they are affected by changes in landscapes.     

A data model for moving regions of fixed shape in databases

Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.     

面向对象的时空数据模型及其实现技术

在基于时间片的连续快照模型基础上,基于OpenGIS定义的几何类型扩充了时态类型,构建面向对象的时空数据模型,定义其抽象数据类型及相关操作;并采用对象关系数据库PostgreSQL作为实现平台,扩展其存储和查询能力。通过实例验证了上述模型及实现方法的有效性和实用性。     

The GRASS GIS temporal framework

The availability of continental and global-scale spatio-temporal geographical data sets and the requirement to efficiently process, analyse and manage them led to the development of the temporally enabled Geographic Resources Analysis Support System (GRASS GIS). We present the temporal framework that extends GRASS GIS with spatio-temporal capabilities. The framework provides comprehensive functionality to implement a full-featured temporal geographic information system (GIS) based on a combined field and object-based approach. A significantly improved snapshot approach is used to manage spatial fields of raster, three-dimensional raster and vector type in time. The resulting timestamped spatial fields are organised in spatio-temporal fields referred to as space-time data sets. Both types of fields are handled as objects in our framework. The spatio-temporal extent of the objects and related metadata is stored in relational databases, thus providing additional functionalities to perform SQL-based analysis. We present our combined field and object-based approach in detail and show the management, analysis and processing of spatio-temporal data sets with complex spatio-temporal topologies. A key feature is the hierarchical processing of spatio-temporal data ranging from topological analysis of spatio-temporal fields over boolean operations on spatio-temporal extents, to single pixel, voxel and vector feature access. The linear scalability of our approach is demonstrated by handling up to 1,000,000 raster layers in a single space-time data set. We provide several code examples to show the capabilities of the GRASS GIS Temporal Framework and present the spatio-temporal intersection of trajectory data which demonstrates the object-based ability of our framework.     

Toward space-time buffering for spatiotemporal proximity analysis of movement data

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.     

Solutions for 4D cadastre – with a case study on utility networks

The increasing complexity and flexibility of modern land use requires that cadastres need to manage information on the third and temporal (fourth) dimension. This article considers the registration of legal space of utility networks in cadastre in this 3D + time (=4D) context. A requirement analysis in three countries that have methods to register utility networks complying with their legal, organizational and technical structure (Turkey, the Netherlands and Queensland, Australia) is the basis for three alternatives for 4D cadastre to register utility networks. The three alternatives are analysed with respect to legal, organizational and technical cadastral requirements. This article presents a case study and a prototype from the Netherlands. In this country by law utilities are considered to be real estate objects with obligatory registration of ownership and geometry. This study shows that the 3D space and separate temporal attributes approach (state-based model) is a very promising solution to maintain temporal changes of utility networks and that this approach is to be preferred above the current practice, where the 3D and temporal aspects are not considered when registering a network.     

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.     

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.     

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.     

基于多粒度时空对象数据模型的高速公路智能监控系统建模

多粒度时空对象数据模型更注重现实世界复杂、动态现象的表达,能更好地应用于智能设施管理和智能决策支持系统。该文基于多粒度时空对象数据模型及其建模理论,对高速公路智能监控系统中实体对象的特征和关系进行概念建模,设计数据存储方案,开发高速公路智能监控多粒度时空对象建模原型系统,并验证数据模型的实用性。结果表明:多粒度时空对象数据模型可以很好地支撑高速公路智能监控系统中动态智能实体及实体间复杂关系的表达、管理和分析,实现对路情的智能分析与决策。     

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACT

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.     

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.     

56 YEARS OF SOLIFLUCTION MEASUREMENTS IN THE ABISKO MOUNTAINS, NORTHERN SWEDEN – ANALYSIS OF TEMPORAL AND SPATIAL VARIATIONS OF SLOW SOIL SURFACE MOVEMENT

Solifluction movement rates from 1952 to 2008 for the Abisko region, northern Sweden, have been compiled and analysed through correlation tests and multiple regression. The temporal analysis is based on two datasets ( Lobe11 & gridAB and Line B ) from Kärkevagge. The dataset Lobe11 & gridAB show a strong correlation between movement rates and mean annual air temperature (MAAT) and MAAT is also identified as one of the significant contributing parameters in the multiple regression model. No significant correlations were found for the Line B dataset. The spatial analysis indicates generally higher movement rates in the western part of the region and at lower altitudes mainly between 700 and 900 m a.s.l., but the spatial variability is high. To reduce the influence of the temporal variation the data for the correlation tests of the spatial variations were divided into two parts: 1957 to 1980 and 1981 to 2008. The correlation analysis of the dataset 1957 to 1980 shows a significant negative correlation between annual average movement rates and permafrost probability and altitude. The dataset 1981 to 2008 shows a positive correlation between movement rates and wetness index. It is concluded that movement rates may increase with higher MAAT in the western part of the region (Kärkevagge), the spatial variability of movement rates within the region is very high and that altitude (and/or permafrost) together with wetness index are the main controls on the regional spatial variation. The study highlights the limitations in establishing statistical relationships between movement rates and climate using data from different field empirical studies.     

Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

Remote-sensing-based drought monitoring methods provide fast and useful information for a sustainable management strategy of drought impact over a region. Common pixel-based monitoring methods are limited in the analysis of the dynamics of this impact at regional scale. For instance, these hardly allow us to quantify the movement of drought in space and time and to compare drought with rainfall deficits without losing the variability of these events within a region. This study proposed an object-based approach that allowed us to visualize and quantify the spatio-temporal movement of drought impact on vegetation, called vegetative drought, in a region. The GIS software Dynomap was used to extract and track objects. Measures of distance and angle were used for determining the speed and direction of vegetative drought and rainfall deficit objects, calculated from the National Oceanic and Atmospheric Administration's (NOAA's) normalized difference vegetation index and rainfall estimates data. The methods were applied to the two rainy seasons during the drought year 1999 in East Africa. Results showed that vegetative drought objects moved into the southwestern direction at an average angle of??138.5° during the first season and??144.5° during the second season. The speed of objects varied between 38 km dekad^?1 and 185 km dekad^?1 during the first season and between 33 km dekad^?1 and 144 km dekad^?1 during the second season, reflecting the rate of spread between dekads. Vegetative drought objects close to rainfall deficit objects showed similar trajectories and sometimes regions overlapped. This indicated that the two events are related. We conclude that a spatiotemporal relationship existed between the two types of events and that this could be quantified.     

Towards a process-based biogeography of reindeer—scaling space,time, and organizational levels of space use

Due to increasing anthropogenic habitat alteration, fragmentation, and loss, the analysis of how, when, and why animals select particular habitats has become a central issue in ecology and biogeography. Animals adapt to spatiotemporal variability in resources either by tracking these resources or by plasticity in behavior to cover their needs, leading to three important implications: movement, temporal variation, and individual trade-offs leading to intra-species variability, all of which are directly linked to the use of space by animals. Based on GPS-tracking of domesticated reindeer in southern Norway, the authors addressed these issues by analyzing movement patterns and space use simultaneously across different temporal and organizational scales. Emerging information about the space use by reindeer was found explicitly linked to scaling relations, reflecting the matter of scale as having important functional implications rather than solely being a technical question of extent and resolution. Adding movement into the analysis of space use further advances the necessary functional perspective on space use. The authors conclude that scaling not only space but also time and the organizational level, in combination with accounting for different behavioral states, brings biogeography closer to a process-based view, rather than solely pattern-based view.     

Integrative representation and inference of qualitative locations about points,lines, and polygons

Qualitative knowledge representation of spatial locations and relations is popular in many text-based media, for example, postings on social networks, news reports, and encyclopedia, as representing qualitative spatial locations is indispensable to infer spatial knowledge from them. However, an integrative model capable of handling direction-based locations of various spatial objects is missing. This study presents an integrative representation and inference framework about direction-based qualitative locations for points, lines, and polygons. In the framework, direction partitions of different types of reference objects are first unified to create a partition consisting of cells, segments, and corners. They serve as a frame of reference to locate spatial objects (e.g., points, lines, and polygons). Qualitative relations are then defined to relate spatial objects to the elements in a cell partition, and to form the model of qualitative locations. Last, based on the integrative representation, location-based reasoning mechanism is presented to derive topological relations between objects from their locations, such as point–point, line–line, point–line, point–polygon, line–polygon, and polygon–polygon relations. The presented model can locate any type of spatial objects in a frame of reference composed of points, lines, and polygons, and derive topological relations between any pairs of objects from the locations in a unified method.     

空间运动对象与时空数据类型研究

当前时空数据模型多以描述空间实体的离散变化为主。该文中对空间运动对象在抽象层次的无限连续空间、离散层次的有限离散空间上的数据类型进行分析和定义，将其分别划分为时间类型、空间类型和时态类型来研究，并提出支持空间运动对象的表示方法和操作方式。该方法既能表示空间实体的连续运动，也能表示其离散变化，为空间运动对象时空数据模型的建立奠定了基础。     

一种适用于产权地籍管理的时空数据模型

该文提出了一种可适用城镇产权地籍管理的时空数据模型及一整套完善解决地籍变更和地籍历史档案管理的方案。论述了该模型的基本思想、空间对象的存储结构描述、时空数据的组织方式以及宗地的变更策略等，并通过实际的应用论证了该模型的有效性和实用性。     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.