使用PostgreSQL/PostGIS和R构建GPS野生动物追踪数据管理系统的实战指南

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"该资源是一本关于使用PostgreSQL和PostGIS构建GPS野生动物追踪数据的空间数据库管理系统的实践指南。由Ferdinando Urbano和Francesca Cagnacci编辑，前言由Ran Nathan撰写。书中的内容涵盖了如何利用这些技术进行空间数据存储、管理和分析，特别适用于野生动物追踪数据的处理。" 《Spatial Database for GPS Wildlife Tracking Data》是指导读者如何利用PostgreSQL和PostGIS这两个强大的开源工具创建和管理空间数据库的专业书籍。PostgreSQL是一种功能全面的关系型数据库管理系统，支持复杂的数据类型和高级查询功能。PostGIS是PostgreSQL的一个扩展，它为PostgreSQL添加了地理空间和矢量数据处理能力，使得数据库能够处理地理坐标、地图和空间分析。本书主要关注于GPS野生动物追踪数据的管理，这对于生态学家和自然资源管理者来说是非常有价值的。GPS追踪数据提供了关于动物行为、迁徙模式和栖息地使用等信息，而有效地存储和分析这些数据是理解和保护野生动物的关键。通过使用PostgreSQL和PostGIS，可以创建一个高效、灵活且可扩展的系统来存储和查询大量的GPS轨迹数据。书中可能涵盖以下几个关键知识点： 1. **空间数据模型**：介绍如何将地理空间数据（如点、线、多边形）表示为数据库中的对象，并解释如何创建和管理这些对象。 2. **GIS基础**：提供地理信息系统的基本概念，包括坐标系统、投影转换和空间参考。 3. **PostgreSQL安装与配置**：详细说明如何安装和配置PostgreSQL服务器，以及如何创建数据库和用户。 4. **PostGIS集成**：讲解如何启用和使用PostGIS扩展，包括创建空间表、执行空间查询和操作空间函数。 5. **GPS数据导入与预处理**：介绍如何将GPS追踪数据导入到数据库中，以及如何清洗和整理数据，以确保其质量和一致性。 6. **空间查询与分析**：演示如何利用SQL查询语言和PostGIS的函数进行空间查询，如距离计算、缓冲区分析和空间关系检查。 7. **R语言集成**：由于R在统计分析和制图方面的能力，书中可能会讲述如何通过R与PostgreSQL/PostGIS交互，进行数据分析和可视化。 8. **数据安全与备份**：讨论数据库的安全措施，如权限控制和定期备份，以防止数据丢失。 9. **案例研究**：提供实际的野生动物追踪项目例子，展示如何应用上述技术和方法来解决实际问题。 10. **性能优化**：探讨如何调整数据库设置以提高查询速度和整体性能。这本书对于那些需要处理大量空间数据，尤其是涉及野生动物研究和保护的学者和专业人士来说，是一本非常实用的指南，可以帮助他们构建高效的数据管理平台，从而更好地理解和保护我们的自然世界。

The Long and Winding Road to Movement Ecology:

Let’s put Things in Order, First

Animal movement is a proximate response to local environmental conditions, such

as climate, chemophysical parameters, resources, and the presence of mates or

predators; in other words, the context in which animals are born, survive,

reproduce, and die. At the same time, movement is the result of complex

evolutionary mechanisms, uniquely blending gene expression into physiological

and behavioural responses. Animal movement has been described as the glue that

ties ecological processes together (Turchin 1998; Nathan et al. 2008) and as an

important mechanistic link between ecology and evolution (Cagnacci et al. 2010).

Moreover, the increasing concern for rapidly changing ecosystems, under climate

and land use change, brings with it an urgent and heightened interest in the

capacity of animals to respond to such changes. An exciting perspective offered by

animal movement is the possibility to understand this phenomenon at different

spatial scales and levels of organisation, from individuals, to populations over

landscapes and to the distribution range of species (Mueller and Fagan 2008). Such

stimulating theoretical framework and widely relevant applied perspective clearly

justify the ever-increasing interest in animal tracking studies, especially those

based on animal-borne data sets, i.e. those obtained through the deployment of

tracking units on individual animals. The innovation in Global Positioning System

(GPS) technology, combined with systems for remote data transfer, has

particularly favored GPS-based animal telemetry to become a standard in wildlife

tracking (Cagnacci et al. 2010).

The advancement of a movement ecology theoretical framework has been

paralleled by technological progress that allows ecologists to obtain a huge amount

and diversity of empirical animal movement data sets. However, this fast-growing

and expanding process has not been followed by an equally rapid development of

procedures to manage and integrate animal movement data sets, thus leaving a gap

between the acquisition of data and the overarching scientiﬁc questions they have

the potential to address (Urbano et al. 2010).

The ideal objective of any movement ecology study is rooted in relevant

ecological questions that can contribute to theory and inform conservation and

management actions. For example, a study on natal dispersal can help in identifying

barriers to gene ﬂow, or an analysis on environmental characteristics affecting

population performance can support decisions on protection areas and conservation

corridors. Whatever the question, the second necessary step should be the

evaluation of the appropriate methodology, and speciﬁcally deciding whether

individual marking with Global Navigation Satellite Systems (GNSS) devices such

as GPS (or other sensors) is the most effective and informative approach to pursuing

the ﬁnal goal (see discussion in Hebblewhite and Haydon 2010). If the answer is

yes, GPS units should be ideally deployed according to an a priori sampling design

(e.g. number, sex ratio and spacing of sampled individuals) and relevant

information on marked individuals should be collected (e.g. age and body mass),

xviii Introduction

as well as on the population of reference and the environmental context

(e.g. population density, presence of competitor species/predators, human activity

pressure). Collection of GPS locations of marked animals should ﬁnally ﬂow, and

can be complemented with environmental parameters (e.g. weather, habitat types

and vegetation indexes based on remote sensing).

The reason for this book comes at this stage of the scientiﬁc method: what to do

with these data? How to handle, manage, store and retrieve them, and how to

eventually feed them to analysis tools such as statistics packages or Geographic

Information Systems (GIS) and test scientiﬁc hypotheses? These operations, which

might be assumed to be secondary compared to the overarching goal of answering

scientiﬁc questions, can instead become overwhelming and hamper the efﬁciency

and consistency of the whole process. Animal ecologists, wildlife biologists and

managers, to whom this book is mainly addressed, are rarely exposed to the basics

of computer science in their training, and may select common tools such as

spreadsheets, or operate in a ‘ﬂat-ﬁles’ fashion. However, the quantity and

complexity of GPS and other bio-logged data require a proper software

architecture to be fully exploited and not wasted or, worse, misused. This book

is a guide to manage and process wildlife tracking data with an advanced software

platform based on a spatial database. It is neither a manual on database

programming nor on wildlife tracking; instead, it aims to ﬁll the gap between the

state-of-the-art knowledge on data management and its application to wildlife

tracking data. This problem-solving oriented guide focuses on how to extract the

most from GPS animal tracking data, while preventing error propagation and

optimizing the performance of analysis. Using databases to manage tracking data

implies a considerable initial effort for those who are not familiar with these tools;

however, the time spent learning will pay off in time saved for the management

and processing of the data, and in the quality of the analysis and ﬁnal results.

Moreover, once a consistent database structure is built to code the storing and

management of data, more familiar tools can be used as interfaces.

Another important advantage of using a structured and consistent software

platform for management of wildlife tracking data is the ever-increasing

importance of cooperative projects and data sharing. Deploying sensors on

animals can be expensive, both in terms of capture logistics and actual cost of

tracking devices, and implies some amount of stress for the marked animals. These

costs pose ﬁnancial and even moral incentives to maximize the effective use of

animal-borne data. Moreover, many large-scale questions, such as evaluating the

effects of climate and land use change on animal distributions, can be properly

addressed only by studying multiple populations, or by integrating data from

several species or time periods. Data requirements for such studies are virtually

impossible for any single research institution to meet, and can only be achieved by

cooperative research and data sharing (Whitlock 2011). Spatial databases are

essential tools to assure data standards and interoperability, along with a safe

multiple users operational environment.

In this manual, a sequential set of exercises guides readers step by step through

setting up a spatial database to manage GPS tracking data together with ancillary

Introduction xix

locations, animals and the environmental variables, and high performance of data

retrieval, which can become cumbersome when data sets are very large. Databases

are designed for being remotely accessed and used concurrently by multiple users,

assuring data integrity and security (i.e. ability to deﬁne a diversiﬁed access

policy). Last but not least, once the storing and management of data is coded in a

robust database, data can still be visualized and accessed with common tools such

as ofﬁce packages (e.g. spreadsheets, database front-ends) or GIS desktop.

Therefore, after the initial effort of designing the structure and populating the

database, most users should be able to easily access the data.

The proposed SQL code can be directly used to create an operational test

database and, at a later stage, as a template to create a more customized data

management system. R is used as the reference statistical environment. The

examples, illustrated in a MS Windows environment, can be easily adapted to the

commonly used operating systems, such as Mas OS X and Linux, which are all

supported by both PostgreSQL/PostGIS and R.

Chapter 1 is a review of the opportunities and challenges that are posed by GPS

tracking from a data management perspective, with a brief introduction to (spatial)

databases, which are proposed as the best available tool for wildlife tracking data

management. Chapter 2 guides readers through the initial creation of a (Post-

greSQL) database to store GPS data. Chapter 3 shows how to integrate ancillary

information on both animals and GPS devices. Chapter 4 presents a solution to

associating GPS positions to animals in an automated fashion. Chapter 5 explores

the features offered by PostGIS, the spatial extension of PostgreSQL, transforming

the coordinates received from the GPS sensors into ‘spatial objects’, i.e. points

embedding spatial characteristics. Chapter 6 uses the spatial tools of the database

to add a set of spatial ancillary information in the form of vectors (points, lines,

and polygons) and rasters and to connect these data sets with GPS tracking data.

Chapter 7 focuses on the integration of ancillary information that captures the

temporal variability of the environment, with a practical example based on the

remote sensing image time series of Normalized Difference Vegetation Index

(NDVI), a proxy of vegetation biomass. Chapter 8 is dedicated to the detection and

management of erroneous location and potential outliers. Chapter 9 introduces

methods for representing and analysing tracking data (e.g. trajectories, home range

polygons, probability surfaces) using database functions. Chapter 10 explains how

to connect R, a powerful software environment for statistical computing and

graphics, with the database and describes a set of typical tracking data analyses

performed with R. Chapter 11 introduces a more direct and efﬁcient approach to

integrate R and PostgreSQL: Pl/R. Chapter 12 discusses the integration of GPS

tracking data with other bio-logged sensors, with a practical example based on

accelerometers. Chapter 13 gives an overview of data standards and software

interoperability, with special reference to data sharing.

Apart from Chaps. 1, 12 and 13, which introduce more general and theoretical

topics, each chapter proposes exercises that are developed sequentially. Although

it is possible to perform an exercise from a later chapter on its own by restoring the

xxii Introduction

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