OBIS and species distributions Tony Rees discussion presentation, March 2003 Some fundamental intentions for OBIS... –Choose any species and discover its.

Slides:



Advertisements
Similar presentations
Tony Rees Divisional Data Centre CSIRO Marine Research, Australia Application of c-squares spatial indexing to an archive of remotely.
Advertisements

Data Models There are 3 parts to a GIS: GUI Tools
Geographic Information Systems GIS Data Models. 1. Components of Geographic Data Spatial locations Attributes Topology Time.
Geographic Information Systems
C-squares - a new simple, XML friendly, display/ query/ exchange format for representing spatial data extents at the metadata level System concept and.
File Processing : Hash 2015, Spring Pusan National University Ki-Joune Li.
1 Enviromatics Spatial database systems Spatial database systems Вонр. проф. д-р Александар Маркоски Технички факултет – Битола 2008 год.
Overview of key concepts and features
Flood Map Library MD. M. HAQUE DWR-HYDROLOGY. Building a Flood Map Library Indexing existing flood maps and geospatial data for search and retrieval Separate.
Bioinformatics GIS Applications Anatoly Petrov.
Spatial Indexing, Search, and Mapping for Species level databases Tony Rees, CSIRO Marine and Atmospheric Research (CMAR), Hobart, Tasmania, Australia.
For Mapping Biodiversity Data Data Management Options.
Tony Rees and Glenelg Smith Divisional Data Centre + Remote Sensing Facility CSIRO Marine Research, Australia Application of c-squares.
Welcome to EDINA Digimap Digimap is an EDINA service offering online access to a range of spatial data. It is authenticated using Athens and is available.
Welcome to EDINA Digimap Digimap is an EDINA service offering online access to a range of spatial data. It is authenticated using the UK Federation and.
Week 17GEOG2750 – Earth Observation and GIS of the Physical Environment1 Lecture 14 Interpolating environmental datasets Outline – creating surfaces from.
Geographic Information Systems
NPS Introduction to GIS: Lecture 1
©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Part 1. Understanding Spatial Data Structures by Austin Troy, University of Vermont.
1 Spatial Databases as Models of Reality Geog 495: GIS database design Reading: NCGIA CC ’90 Unit #10.
Lecture 4. Interpolating environmental datasets
Geographical Information System GIS By: Yahia Dahash.
CORDRA Philip V.W. Dodds March The “Problem Space” The SCORM framework specifies how to develop and deploy content objects that can be shared and.
Rebecca Boger Earth and Environmental Sciences Brooklyn College.
Introduction to ArcGIS for Environmental Scientists Module 2 – GIS Fundamentals Lecture 5 – Coordinate Systems and Map Projections.
Day 1-3. Variable Selection and GIS Processing 1.Discuss V mapping goals, targeted system (what is vulnerable?), framework 2.Choose data layers (criteria:
 A data processing system is a combination of machines and people that for a set of inputs produces a defined set of outputs. The inputs and outputs.
Spatial Data Model: Basic Data Types 2 basic spatial data models exist vector: based on geometry of points lines Polygons raster: based on geometry of.
Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 1- 1 Chapter 1 - Introduction: Databases and Database Users - Outline Types of Databases and.
ESRM 250 & CFR 520: Introduction to GIS © Phil Hurvitz, KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation,
A Grammar-based Entity Representation Framework for Data Cleaning Authors: Arvind Arasu Raghav Kaushik Presented by Rashmi Havaldar.
Map Scale, Resolution and Data Models. Components of a GIS Map Maps can be displayed at various scales –Scale - the relationship between the size of features.
Chapter 3 Sections 3.5 – 3.7. Vector Data Representation object-based “discrete objects”
GIS in Real Estate Phil Hurvitz CAUP-Urban Form Lab April 13, 2005.
Fundamentals of GIS Materials by Austin Troy © 2008 Lecture 18: Data Input: Geocoding and Digitizing By Austin Troy University of Vermont.
C-squares - a simple, XML friendly, query/ display/ exchange format for representing spatial data at the metadata level System concept and development.
Scale, Resolution and Accuracy in GIS
Geographic Information System GIS This project is implemented through the CENTRAL EUROPE Programme co-financed by the ERDF GIS Geographic Inf o rmation.
A1 Agenda RESULTS Training RESULTS & Mapview RESULTS & Mapview MOF Information Management Group 2005 Module 8 – RESULTS & Mapview.
OBIS Portal Architecture Concepts plus potential for utilization as a basis for Regional OBIS Nodes Tony Rees, CSIRO Marine Research, Hobart (and OBIS.
Chapter 3 Digital Representation of Geographic Data.
How do we represent the world in a GIS database?
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Introduction to GIS for the Purpose of Practising.
Search Engines. Search Strategies Define the search topic(s) and break it down into its component parts What terms, words or phrases do you use to describe.
CSIRO Marine Research Data Centre linked databases - CAAB, MarLIN and Divisional Data Warehouse.
Extent and Mask Extent of original data Extent of analysis area Mask – areas of interest Remember all rasters are rectangles.
Spatial Issues in DBGlobe Dieter Pfoser. Location Parameter in Services Entering the harbor (x,y position)… …triggers information request.
Advanced GIS Using ESRI ArcGIS 9.3 Spatial Analyst 2.
Navigation Timing Studies of the ATLAS High-Level Trigger Andrew Lowe Royal Holloway, University of London.
NDD (National Oceans Office Data Directory) development overview as at 1 July 2002 Tony Rees/Miroslaw Ryba CSIRO Marine Research, Hobart.
NR 143 Study Overview: part 1 By Austin Troy University of Vermont Using GIS-- Introduction to GIS.
1 Overview Finding and importing data sets –Searching for data –Importing data_.
INTRODUCTION TO GIS  Used to describe computer facilities which are used to handle data referenced to the spatial domain.  Has the ability to inter-
MarLIN - CSIRO Marine Laboratories Information Network.
Search Engine using Web Mining COMS E Web Enhanced Information Mgmt Prof. Gail Kaiser Presented By: Rupal Shah (UNI: rrs2146)
CPSC 252 Hashing Page 1 Hashing We have already seen that we can search for a key item in an array using either linear or binary search. It would be better.
1 CSCD 326 Data Structures I Hashing. 2 Hashing Background Goal: provide a constant time complexity method of searching for stored data The best traditional.
Towards Unifying Vector and Raster Data Models for Hybrid Spatial Regions Philip Dougherty.
The GPS Tools Form Maintaining spatial data about assets, events and other objects.
System concept and development by: Tony Rees Divisional Data Centre CSIRO Marine Research, Australia c-squares - a new method for representing, querying,
C-squares concept: Data items are represented by the grid squares in which they are located 1: Data items2: Data items and relevant grid squares 3: Grid.
NVS New Zealand National Vegetation Survey. What is NVS? NVS (National Vegetation Survey) – New Zealand’s largest archive facility for plot-based vegetation.
System concept and development by: Tony Rees Divisional Data Centre CSIRO Marine Research, Australia c-squares - a new method for representing, querying,
In this session, you will learn to: Create and manage views Implement a full-text search Implement batches Objectives.
MESA A Simple Microarray Data Management Server. General MESA is a prototype web-based database solution for the massive amounts of initial data generated.
Semantic metadata in the Catalogue Frédéric Houbie.
Flood Map Library MD. M. HAQUE DWR-HYDROLOGY. Building a Flood Map Library Indexing existing flood maps and geospatial data for search and retrieval Separate.
Geog. 314 Working with tables.
Geographic Information Systems
2018, Spring Pusan National University Ki-Joune Li
Presentation transcript:

OBIS and species distributions Tony Rees discussion presentation, March 2003 Some fundamental intentions for OBIS... –Choose any species and discover its distribution –Click on a body of water and discover what lives there –Then... Examine/discover correlations with environmental variables, etc. Currently: –OBIS connects to sources of point data, retrieves relevant points, displays on map and/or passes to modelling packages (also displays some relevant ancillary information and provides link to more if available)

Point vs. polygon data Point data... –Limited by: restricted distribution of sampling activity (vs world ocean area) only a subset of possible specimens retained (or catches recorded) only a further subset of these acessible to OBIS –“Real” distribution will always require interpolation/ extrapolation (=> ranges, expressible in polygons) –Only then can do meaningful queries as per initial slide Polygon data... –Could need mechanisms for OBIS to: import store query display export

Types of polygons May cover more than simple binary (presence/absence) states, e.g... –“Normal” vs. extralimital (occasional/vagrant) distribution –Breeding vs. non-breeding ranges –Seasonal distributions (winter/summer/migration paths etc.) –Juveniles vs. adults –infraspecific variation (subspecies, varieties...) –confidence limits (known vs. doubtful) –historic vs. current (decadal variability) –etc. May also vary by source/ method of construction: –Comprehensive sampling (present vs. definitely absent) –Indicative sampling (with inferred presence/absence where not sampled) –Modelled distribution based on proxies (environmental, other species)

Examples of polygons (butterfly data) - actually a gridded dataset with both presence+absence data

Examples of polygons - cont’d (bird species)

Examples of polygons - cont’d (whale species)

Examples of polygons - cont’d (modelled fish distribution)

Expression of polygons Polygons expressed in various forms.. –DIGITAL - vector string of points connected by simple lines as above, but with exclusion areas *** need GIS back end for spatial queries and display –DIGITAL- non-vector gridded (tiled) representation nominated regions/zones *** potentially simpler to query and display, but may be less exact –NON-DIGITAL paper / diagrammatic representation ms textual descriptions *** need digitising before accessible to online querying

An approach using gridded (tiled) data Express any species distribution as list of tiles (grid squares) in which that species occurs Tile size would be matched to scale of query system would be designed to support, e.g. 1 x 1 deg [100 x 100 km approx.], 0.5 x 0.5 deg. Could then query any tile and rapidly extract a list of what species occur there - especially if secondary (“inverted”) index built based on tile IDs Could accept species distributions as gridded data directly, or as vector polygons (then use polygon converter tool on the data)

“C-squares” system does exactly this Provides a unique, easily organised and searchable nomenclature for individual tiles (gridsquares) Prototype “polygon-to-c-square” converter available to look at / try out Distributions expressed in c-squares are straightforward to map using existing c-squares mapper (or OBIS could construct its own) Could accept species distributions as gridded data directly, or as vector polygons (then use polygon converter tool on the data) - True GIS system might be better (ultimately more accurate, smoother diagonal lines/curves, more flexible overlays etc.) but may be slower for searching ?? (considering numbers of species potentially involved)

How might this be implemented? ? OBIS master distributions table: Species 1... square A... source x Species 1... square B... source x Species 2... square B... source y etc.... can then easily extract: –all squares occupied by Species 1 –all species in square A –source of any record (also needed for internal tracking) would potentially need to qualify “source” information as to type of record (as previously discussed) could also need to qualify by depth, season, decade (etc.) in “sources” list (table)

Getting these data into OBIS... ? A persistent index, regularly refreshed/updated by querying distributed data sources –OBIS could handle the polygon => gridsquare conversion as a background task ? Live broadcast query, polygon/raster data interrogated on-the-fly –would be quickest if polygon => gridsquare conversion had already been undertaken at the partner end... data could potentially be transferred as vector data (polygon boundaries), or gridded data (either standardised, or non-standardised). Need to consider optimum method(s) for transfer, with implications as to where required conversions would be done.

“Click on a square” search interface (from current “MarLIN” system)

Could generate (for example)... species are registered in OBIS