Geog 380: Geospatial Communication PDF

Geog 380: Geospatial Communication Source: https://earth.nullschool.net Topic 03: Spatial Data & Models GEOG 380 (Topic 03) © Geoffrey Hay (2023) 1 Topic 03 - Learning outcomes By the end of this topic, a successful student will be able to: § Explain the differences between rasters and vectors...

Geog 380: Geospatial Communication Source: https://earth.nullschool.net Topic 03: Spatial Data & Models GEOG 380 (Topic 03) © Geoffrey Hay (2023) 1 Topic 03 - Learning outcomes By the end of this topic, a successful student will be able to: § Explain the differences between rasters and vectors § Explain the pros and cons of rasters and vectors § Explain the variety of geographic phenomena and the various representation options § Explain the pros and cons of different types of data models for representing real world features § Explain types of resolution and how they manifest in geospatial data GEOG 380 (Topic 03) 2 Why is spatial data special? GEOG 380 (Topic 03) Source:https://www.safe.com/what-is/spatial-data/ Why Special? https://youtu.be/eTLCpEq29gc 3 Different ways of understanding spatial § § § § Geographic: the Earth’s surface and near-surface Spatial: any space (not just Earth’s surface) Spatial analysis: application of techniques to geographic and non-geographic spaces Geospatial: subset of spatial https://www.nvidia.com/en-us/geforce/guides/minecraft-rtx-world-conversion-guide/ GEOG 380 (Topic 03) 4 Spatially explicit vs. implicit § Explicit § Coordinate systems § Lat/long: 51.5893º N, 114.7325º W § UTM: Zone 11U 701335.567 E, 5662646.5720 N § Address § Postal codes § Street address § Implicit § All data has some spatial element – e.g., where it was collected § Geocoding – converting text-based description of a location to geographic coordinates to identify a location on the earths surface § Impacts how you use/analyze data https://www.pubnub.com/learn/glossary/what-is-geocoding-and-reverse-geocoding/ GEOG 380 (Topic 03) 5 Tobler’s First Law of Geography § § § Waldo Tobler (1969) “Everything is related to everything else, but near things are more related than distant things” Highlights one of the most important aspects of spatial analysis – what is near? – i.e., scale How do we group (spatially)? Source: http://gisgeography.com/tobler-first-law-of-geography/ GEOG 380 (Topic 03) 6 Geographic representations - approaches GEOG 380 (Topic 03) 7 Components of geospatial data § Object/phenomena of interest - identifiable § These are the entities § Represented using vector and/or raster models - § Descriptions of the characteristics of the object/phenomena https://www.researchgate.net/figure/The-six-basicelements-to-represent-a-geographicobject_fig2_332278405 GEOG 380 (Topic 03) § These are the attributes of the entities § The range of attributes is enormous – anything you can observe/measure about the “thing” § Nature of the attributes determined by measurement level and are linked to the entities through the database 8 Modeling geographic phenomena § What is the best way? § Purpose? Fig 3.; Heywood et al. (2011) GEOG 380 (Topic 03) 9 Discrete Objects and Continuous Fields GEOG 380 (Topic 03) § Discrete objects and continuous fields are two conceptual models of geographic representation; neither of which is ‘correct’ § They are the foundation of the two dominant methods of representing geographic data on maps and in computers § Raster: continuous fields § Vector: discrete objects § These are NOT universal rules!! Figure: https://desktop.arcgis.com/en/arcmap/latest/extensions/3d-analyst/discrete-and-continuous-data-in-3d-analyst.htm 10 Continuous Fields § Continuous Field (or continuous surface) § Measurable at any point § Changes continually – how smoothly is dependent on the scale of investigation https://sites.allegheny.edu/creekconnections/the-modules/topographic-maps/ GEOG 380 (Topic 03) § Somewhat less efficient than discrete objects, because it requires regular representation on a map § Works well for many ‘natural’ phenomena 11 Raster & Vector Approaches § Most (if not all) entities can be represented using either model § Usually one model is more effective § Imagery – raster § Urban infrastructure - vector Fig 3.9; Heywood et al. (2011) GEOG 380 (Topic 03) 12 The Raster Data Model § Space is divided up into a regular array of cells – usually squares § The basic entity is a pixel: picture element § Attributes (values) are assigned to each pixel Modified from Fig 3.8; Longley et al. (2005) Mixed conifer Oak forest Douglas fir Grassland GEOG 380 (Topic 03) § Commonly used for digital photography, satellite imagery, digital elevation models § for phenomena suited to the continuous field model of geographic representation 13 Raster File Structure Fig 3.11; Heywood et al. (2011) GEOG 380 (Topic 03) 14 Discrete Objects § Discrete object § Also known as categorical or discontinuous data § Well-defined boundaries § Can be counted § Powerful and efficient § Easily represented by discrete symbols § Properties about the object can be summarized in an attribute table https://www.communiquepr.com/avoid-getting-lost-in-the-crowd/6493/ GEOG 380 (Topic 03) § Problematic for many natural phenomena that don’t “fit” into clean objects 15 The Vector Data Model § Geographic phenomenon represented by points, lines, and polygons § Usually limited to straight lines connected by vertices § A polyline describes a curved phenomenon represented by straight line segments § Attributes (values) are assigned to each entity Data source: AGS Maps 207 and 213. GEOG 380 (Topic 03) § Commonly used for socio-economic data, urban data, and other phenomenon suited to the discrete object model of geographic representation 16 Vector Data Model § Point dictionary vector data structure § (explains all variable names/values/types in data) § Avoids redundancy § No real topology § “Topology = properties of space that are invariant under any deformation”. Source: https://uwaterloo.ca/pure-mathematics/about-pure-math/what-is-puremath/what-is-topology Fig 3.14; Heywood et al. (2011) GEOG 380 (Topic 03) 17 Vector Data Model Fig 3.15; Heywood et al. (2011) § Topological vector data structure § File structure informs where one feature is relative to ones around it § Topology: The spatial relationships between adjacent or neighboring features GEOG 380 (Topic 03) 18 Resolution § Spatial § Attribute § Temporal Fig 3.10; Heywood et al. (2011) GEOG 380 (Topic 03) 19 Raster Versus Vector: Which is Better? § Each data model also lends itself to unique processing efficiencies GEOG 380 (Topic 03) 20 Summary § Spatial and geospatial features § Location § Geometry § Attributes § Two complementary models § Raster § Vector § Location and relative position is important – geospatial analysis GEOG 380 (Topic 03) 21