Next-Generation Data Management in Movement Ecology
ORGANIZER - Francesca Cagnacci
VENUE - The Campus at Fondazione Edmund Mach
DATES - July 1-3 and July 6-10, 2015
FORMAT - An intense, two stages-course, with a mix of lectures, hands-on exercises and special topic lectures. The first module will provide Introduction to Spatial Databases (using PostgreSQL/PostGIS as reference system). The second part will focus onto Wildlife Tracking data Management, with ample reference to use and integration of remote sensing dataset, and statistical analysis through R. The two modules can be joined independently, or in combination. Basics of SQL and spatial databases are however essential to follow the second part.
FEES AND COSTS - The courses are supported by the International Research School in Applied Ecology (IRSAE). Fees and costs are therefore:
- IRSAE participants - Fees, board and lodging are covered for participants from IRSAE partners, who may also apply for a mobility grant to cover travel costs, after admission to the course (see application form at http://irsae.no/grants ).
- No IRSAE participants - Students: I module- 50 €; II module: 100€. Researchers/Managers: I module- 50 €; II module: 100€. Food and lodging in the campus can be booked at 80 €/day. Alternative lodging possibilities are available in the area.
PARTECIPATION - This course is open to all PhD and MSc students. Participation of post-docs, researchers, technicians and managers is also fostered. There will be room for a maximum of 20 students from the IRSAE network and 15 attendees outside the network, so strong competition for attendance is expected.
EVALUATION AND CREDITS: ETS credits will be assigned, after positive grades in a final exam.
REGISTRATION - Email the application form, containing a brief description of your PhD project and description of the relevance of the course to your research, along with a CV to Francesca Cagnacci (firstname.lastname@example.org) or Lucrezia Gorini (email@example.com). Deadline April 30, 2015.
SCIENTIFIC CONTENT - 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. In addition to the increasing resolution and size of data sets available from tagging technology, locations of animals come with complex associated information related to the environmental context, such as population density, presence of competitor species/predators, human activity pressure, weather, habitat types and vegetation indexes based on remote sensing. 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. This two-stage course is designed to address this gap: 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. PostgreSQL and its spatial extension PostGIS are the proposed software platform to build the wildlife tracking database. This spatial database will allow management of virtually any volume of data from wildlife GPS tracking in a unique repository accessible by multiple users, while assuring data integrity and consistency, avoidance of error propagation, and limiting data duplication and redundancy. In the first module of the course, participants will be exposed to basics of Spatial Databases, and SQ Language. Then, in the second part, we will focus on the specific requirements of wildlife data, and specifically wildlife tracking data and related information. We will also consider related topics, such as use of remote sensing data, concept of landscape variability, dissemination of information on the web and data sharing. Last but not least, we will consider how standardized organized database can interface with statistical tools, such as those provided by R Programming language. At the end of the course, participants will have a solid understandings of data management state-of-the-art tools, such as relational databases and spatial databases, and independence in developing a tracking data management system for their own data sets. Specifically, they will be able to acquire, upload, structure, manage, and query their tracking data in a spatial relational database in a multi-users environment, and to visualize and analyse data using specific client applications (e.g., GIS desktop, statistics packages) and to integrate environmental data sets into the database.
Introduction to Spatial Databases
- Tue, 30th June Afternoon - arrivals at FEM, San Michele all’Adige
- Wed, 1st July - General introduction to relational databases and spatial databases. SQ Language and basic querying applications
- Thu, 2nd July - Advanced SQL querying and problem solving
- Fri, 3rd July - Case studies, interactive querying of test DB with students
Wildlife Tracking Data Management
- Sun, 5th July - Afternoon: arrivals at FEM, San Michele all’Adige
- Mon, 6th July - Requirement analysis of wildlife tracking data management. Wildlife tracking and sensor technology. Creation of a wildlife tracking database on PostgreSQL/PostGIS using students’ data sets or testDB. Extending the database with other meta information: capture, mortality, population data.
- Tue, 7th July - From data to information: associating locations to animals. Integration and management of spatial ancillary information. From locations to steps: the movement trajectory. Disseminating data on the web: review of the main available tools.
- Wed, 8th July - Sensors data: how to ingest into the database. How to extract environmental information related to location data. Data quality: how to detect and manage outliers
- Thu, 9th July - Accelerometers: investigating animal's activity. Integrating activity and tracking data. Analyzing and managing movement data: representations, methods, and tools in the database framework. Movement ecology and landscape variability. Data sharing and dissemination.
- Fri, 10th July - Analyze movement data in a statistical environment: R
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