1 / 24

Geog 458: Map Sources and Errors

Geog 458: Map Sources and Errors. January 9 2006 Representing Geography. Outlines. Importance of geographic representation How do human conceptualize surroundings? How is human conceputalization coded in computer databases?

akiva
Télécharger la présentation

Geog 458: Map Sources and Errors

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Geog 458: Map Sources and Errors January 9 2006 Representing Geography

  2. Outlines • Importance of geographic representation • How do human conceptualize surroundings? • How is human conceputalization coded in computer databases? • Reading Spatial Data Organization in FGDC Metadata Content Standard

  3. 1. Importance of geographic information • What is geographic representation? • Why is geographic representation important?

  4. What is representation? • Viewing the real world (or surroundings, reality) • Forcing the real world into a manageable concept • Human conceptualization of the world • Putting the real world into a computer • How would you represent the followings in a computer? Does the same data model fit all? • Vegetation, soil, traffic volume, land use, tornado, clouds… • Data modeling (building data model)

  5. Why is representation important? • Forms the basis of metadata organization • Identification, spatial data organization, entity and attribute • Many operations depend on data model • Routing • Spatial interpolation • Forms the basis for understanding data structure and data format used in a GIS • Vector, point, line, node, area, topology • Raster, pixel, tessellation, sampling, planar enforcement

  6. 2. How humans conceptualize surroundings? • Object vs. field • Dimensionality • Space, time and attribute • Scale

  7. Do-It-Yourself • Exploring different ways to represent spatial concepts • Group A: how land parcel is represented in a GIS • Group B: how elevation is represented in a GIS • Group C: how tornado is represented in a GIS • Group D: how traffic volume is represented in a GIS • Each group should discuss the representation of each theme in terms of (1) object vs. field (2) dimensionality (3) how space, time and attribute are organized (4) appropriate scale of space and time

  8. Land parcel & Elevation • We can observe discrete boundary of land parcel but we can’t observe that of elevation • Land parcel forms polygon in terms of geometry, but elevation does not conform to well-defined geometry • Identity change over time is gradual in the corresponding time scale  temporal element is largely ignored, the relation between space and attribute determines representation

  9. Traffic volume & Tornado • Movement of things with some pronounced properties across geographic path • Can have all dimensionalities by leaving out less important details • Temporal element is important • Finer temporal scale is required to describe the subject properly  attribute is largely ignored, the relation between space and time determines representation

  10. (1) Object vs. Field • Discrete object • Identifiable boundaries  easier for manipulation like a tabletop object conceived easily by direct human experience • Building, population, county boundary • Continuous field • Variation in a given spatial scale  requires spatial sampling scheme (tessellation) • Temperature, population density, tax rate per county • Any limitations? • Soil boundary, mental map of localities • Planar enforcement (i.e. polygon doesn’t overlap)

  11. (2) Dimensionality • By identifying dimensionality of object, you can place them in Euclidean geometry • Zero-dimension (point) • One-dimension (line) • Two-dimension (area) • Three-dimension (volume) • Any limitations? • Even though most of data format (e.g. shapefile) available in GIS is only allowed to have one of possible dimensions, reality is that some object (e.g. lake) can have multiple dimensions depending on scale and applications • Multiple representation (i.e. representing geographic entities across multiple scales)

  12. (3) Space, Time, and Attribute • Geographic information has three components: space, time and attribute • Data model can be understood through measurement framework • Measurement framework: all of three components are not fully measured, but rather one of them is measured, controlled, and fixed respectively (Sinton 1978)

  13. (4) Scale • Scale determines the way in which phenomenon is described • Astronomy, geography, human biology • Universe time, geological time, geographic time • Scale influences the way humans cognize surroundings • Our experience of surroundings is quite different depending on scale: e.g. tabletop object (direct experience) versus cities • Scale  human conceptualization  data model  GIS implementation • Representing reality in some geometry (e.g. city as point) is a reasonable approximation only at a particular scale • Scale determines accuracy and thus fitness of use of data to particular purposes

  14. Is GIS a container of digital maps? • Partially yes (mainly because we are used to it) • DRG, DLG, DOQQ, Satellite image • But pitfall of limiting your thought on geographic representation to this doesn’t help us explore other potential geographic representations • The way of describing geographic information is not necessarily limited to point, line, polygon, and surface, but rather relational or propositional (e.g. I live in Seattle, you should turn left at the intersection)  first-order logic (deductive database) • City does not have geometry and attributes, but also has different functions (as adminstrative unit, as economic unit, as ecological unit)  object-oriented database • Geography is not the same as geometry indeed!

  15. 3. How is human conceptualization coded in computer databases? • Putting discrete objects into a computer (vector GIS) • Spatial primitives • Topology • Generalization algorithm • Putting continuous fields into a computer (raster GIS) • Gridded raster • TIN • Georelational model as a special case of vector topological model (ArcInfo coverage)

  16. (1) Spatial primitives • Three spatial primitives are used to represent discrete objects • Point: as a point (x, y) • Line: as a set of vertices • Polygon: as a set of vertices that form to close

  17. (2) Topology • What is topology? • Non-metric properties of geographic objects that remain constant when the geographic space of objects is distorted (e.g. projection change, transformation) • Why is topology important? • From data perspectives, it validates geometric accuracy (e.g. network connectivity, line intersection, overlap, duplicate lines)  link to logical consistency in data quality • From analytics perspectives, it optimizes queries by storing spatial relation information in a table • How are they stored in a table? (see next slide)

  18. Connectivity • Arc 2 connects from 11 to 12 • Containment • Polygon C is surrounded by arc 2,4, 9, and 6 • Contiguity • Polygon B/C in the left/right of arc 6

  19. (3) Generalization • Douglas-Poiker algorithm • Process for simplifying line by reducing the number of vertices in its representation • E.g. Cape Cod in 1:1,000,000 map  Cape Cod in 1:25K • How it works? • For illustration, see Figure 3.17 at Longley et al (p. 82) • What does it achieve? • It attempts to preserve pronounced changes in angle within a given tolerance • It reduces the size of data, and speeds up the process for display and further analysis

  20. (4) Raster grid • Variation in values are stored in each cell • Many geographic data (e.g. satellite image, air photo) are derived from this data structure • It takes up large space; it leads to development of many different raster compression methods

  21. (5) TIN • What is TIN? • Triangulated Irregular Network • Represents a surface as contiguous non-overlapping triangular plane where three points have different z-values (See Figure 8.12 in Longley et al 2005 (p. 189) • Why is TIN popular? • Allows for varying density in sampling points • Less storage because it stores only critical points (cf. raster grid)

  22. Georelational model • Geometry of spatial object is stored in a file and attribute is stored in a table • File is linked to attribute through a common identifier • Separation between spatial and non-spatial attribute • See Figure 8.10 (e.g. Arc/Info coverage)

  23. 4. Reading Spatial Data Organization in CSDGM

  24. Do-It-Yourself Air Quality – Lake Monitoring Sites • What is Indirect Spatial Reference? Take any example. • What is Direct Spatial Reference Method? • What is STDS point and vector type? • What is “Entity Point”? • What is the difference between point and node? • Resources • http://fgdc.gov/metadata/csdgm/03.html • http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_nov97/part1b10.html Metadata from Geospatial One-Stop

More Related