Geospatial Data Fundamentals Flashcards

1
Q

Spatial Model

A

basic properties and process for a set of spatial features

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2
Q

Cartographic Model

A

temporally static, combined spatial datasets, operations and functions for problem-solving

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3
Q

Spatio-temporal Model

A

Dynamics in space and time, and time-driven processes

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4
Q

Network Model

A

modeling of resources (flow, accumulation) as limited to networks

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5
Q

Data models (Goodchild)

A

entities and fields as conceptual models

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6
Q

Static Modeling (Goodchild)

A

Taking inputs to transform them into outputs using sets of tools and functions

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7
Q

Dynamic Modeling (Goodchild)

A

An iterative model with sets of initial conditions that applies transformations to obtain a series of predictions at time intervals

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8
Q

Passive and prescriptive data models (DeMers)

A

passive models are like description of the study area; Prescriptive models are active, imposing best solutions.

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9
Q

Vector

A

A coordinate-based data model that represents points, lines, and polygons.

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10
Q

Points

A

Discrete locations on the ground, and represented by a coordinate pair

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11
Q

Lines

A

Linear features, such as rivers, roads, and transmission cables, are composed of vertices (beginning and ending at vertices), represented by an ordered list of vertices

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12
Q

Polygon

A

Form bounded areas, such as islands, land masses, and water features, that are composed of nodes and vertices, with the start node the same as the end node.

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13
Q

Raster

A

Composed of rectangular arrays of regularly spaced square grid cells. Each cell has a value (attribute). It can be single or multiple bands, with each cell having one attribute value. And the raster coordinates are stored by ordering the matrix.

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14
Q

Pixel

A

smallest resolvable piece of the scanned image. It’s always a cell but a cell is not always a pixel.

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15
Q

Geodatabase

A

It’s an object-oriented spatial model that includes feature classes, feature datasets, nom-spatial tables, as well as complex components such as topology, relationship classes, and geometric networks.

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16
Q

Relationship classes

A

model real-world relationships that exist between objects such as parcels and buildings

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17
Q

Grid

A

Parallel and perpendicular lines for reference as a map projection or coordinate-system

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18
Q

TIN

A

Triangulated irregular network - it portions vector data into contiguous, non-overlapping triangles. It creates Delaunay triangles.

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19
Q

Advantage of TIN

A

It’s best for small areas with high-precision elevation data, and it’s more efficient storage than DEM or contour line.

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20
Q

Topological

A

features that need to be connected using specific rules

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21
Q

Hierarchical

A

database that stores related information in a tree-like structure. And records can be traced from parent records to a root record.

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22
Q

Network

A

A collection of topologically connected network elements (edges, junctions, turns). And each element is associated with a collection of network attributes

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23
Q

Object-oriented data

A

data management structure stores data as objects (classes) instead of rows and tables as a relational database. The examples include SQL Server, Oracle, and PostgreSQL.

24
Q

Type of relationships

A

one-to-one; one-to-many; many-to-many

25
One-to-one relationship
Each object of the origin table can be related to zero or one object of the destination table
26
One-to-many relationship
Each object in the origin table can be related to the multiple objects in the destination table
27
Many-to-many relationship
Multiple objects of the origin table can be related to multiple objects of the destination table
28
Typological relationships
Equal, disjoint, intersects, touches, contains, covers, covered by, within, crosses, overlaps
29
Equals
Two features are typologically equal: a = b
30
Disjoint
Two features have no point in common: a ∩ b = ∅
31
Intersects
Two features have some common interior points: a ∩ b ≠ ∅
32
Touches
One feature touches another with at least one boundary point in common: (a ∩ b ≠ ∅) ∧ (aο ∩ bο = ∅)
33
Contains
a ∩ b = b
34
Covers
Every point of one feature is a point of another: aο ∩ b = b
35
Covered by
reverse of the "covers"
36
Within
a ∩ b = a, opposite of "Contains"
37
Crosses
a crosses b at some point
38
Overlaps
a and b have common interior points
39
Geometric Accuracy
the closeness of a measurement to its true value
40
Root Mean Squared Error (RMS)
A calculation to describe the difference between the measurement and the true value.
41
Thematic Accuracy
The accuracy of non-spatial data, such as the street name accurate on a street feature class
42
Resolution
The smallest separation between two coordinate values (for raster, resolution refers to the cell size)
43
Precision
The level of measurement and exactness of attribute data
44
Fitness for use
Does the data fulfill the needs of the project?
45
Confusion matrix
assesses accuracy of image classification based on additional ground truths
46
Quality Assurance
The process that orients and focuses on defect prevention. It applies the Managerial Tool/Peroid Audits, which is the establishment of a good quality management system and assessment of its adequacy
47
Quality Control
The product focuses on defect identification. Such as the corrective tool, which finds and eliminate sources of quality problems through tools and equipment
48
Imprecision
All data is taken from a 3D globe and transferred to a 2D surface through spatial transformations (projection and datums) which cause distortions with the data
49
Uncertainty
The GIS data that was created/collected at a certain point of time, may already be out of date. This implies the differences between the GIS and the real world. It may be visible from the original data or measuring that data; it may result from the assumptions made when creating the data; It's related to the model structure, including retrieval errors, sampling error, and inadequate ground observations.
50
Data Resolution
the cell size of a raster (the area covered by the ground represented by just one cell)
51
Validation
To ensure the accuracy of the data is preserved, which uses ground observations to ensure data accuracy, or the data can be compared to model-generated data (which is less accurate).
52
Temporal
data that represents a state in time, for example, the rain fall for one day.
53
FGDC
Federal Geographic Data Committee: who, what, when, where why and how. Its format includes the title, abstract and date, geographic extent and projection info, attribute label definitions, and domain values.
54
CSDGM
Content Standard for Digital Geospatial Metadata
55
OGC
Open GIS Consortium. It describes the basic data model for holding geographic data.