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Introduction to GIS / GISc01 / 25 / 2017

Topics Today:• GIS components• GIS knowledge• GIS functions (intro)• Validation and Verification• Problem Solving

Six Components of a GISystem

Network

People

Hardware

Software

Data

Procedures

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What is GIScience?

GISc advocates claim that:• the procedures of users, and

• the functions of GIS software (geographic knowledge) 

in conjunction with the • data stored in tabular databases 

accommodate the ability for both • Idiographic, and 

• Nomothetic analysis simultaneously, 

thus GISc is born!

Do you agree?  Think about it for a while~

Nomothetic and Idiographic

• Epistemological terms to describe two distinct approaches to producing and comprehending knowledge

• Epistemology – ‘theories of knowledge’ or ‘ways of knowing’

• Nomothetic – concerned with the ‘universal’ and the general• Usually quantitative

• Idiographic – concerned with the ‘unique’ and the particular• Usually qualitative

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One of many definitions for GISc

A system of integrated, computer‐based tools for end‐to‐end processing (capture, storage, retrieval, analysis, display)  of data using location on the earth’s surface for integration  in support of  integrated decision making.

• set of integrated tools for spatial analysis

• encompasses end‐to‐end processing of data• capture, storage, retrieval, analysis/modification, display

• uses explicit location on earth’s surface to relate data 

• aimed at decision support (and on‐going operations)

What is GIS?

• GIS’s are spatial (geographic) databases that support a myriad of organizations and activities

• Therefore, they are crucial to operation of organizations

• Organizations like: US EPA; US NGA; US DoD; US DHS; US NOAA; US NPS; US FEMA; PA DEP; PA DCNR; PennDOT; Federal Express; Chase Manhattan Bank; Sears; USA Today

• Or Apple, FourSquare, Facebook, Googel, Giant Eagle… the list goes on, and on, and on~

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… from Longley, et. al~

• GIS is ‐‐‐• A container of maps in digital form

• A computerized tool for solving geographic problems

• A spatial decision support system

• A mechanized inventory of geographically distributed features

• A tool for revealing what is otherwise invisible in geographic information

Defining Geographic Information Systems (GIS)

• The common ground between information processing and the many fields using spatial analysis techniques (Tomlinson, 1972)

• A powerful set of tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world (Burroughs, 1986)

• A computerized database management system for the capture, storage, retrieval, analysis and display of spatial (locationallydefined) data (NCGIA, 1987)

• A decision support system involving the integration of spatially referenced data in a problem solving environment (Cowen, 1988)

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The Purpose of a GISystem

Allows the geographic features in real world locations to be digitally represented so that they can be abstractly presented in map (analog) form, and can also be worked with and manipulated to address some problem

Provides a digital representation of the real world for use in operational management, decision making, and science 

Who Uses GIS and How do They Use It?

Urban Planning, Management & Policy Zoning, subdivision planning Economic development

Code enforcement

Emergency response Crime analysis

Tax assessment

Political Science Redistricting

Analysis of election results

Business Demographic Analysis Market Penetration/ Share 

Analysis

Site Selection

Environmental Sciences Monitoring environmental risk Management of watersheds, 

floodplains, wetlands, forests, aquifers

Environmental Impact Analysis

Hazardous or toxic facility siting Groundwater modeling and 

contamination tracking

Real Estate Neighborhood land prices

Traffic Impact Analysis

Determination of Highest and Best Use

Health Care Epidemiology Needs Analysis

Service Inventory

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What GIS Applications Do:manage, analyze, communicate

Make possible  the automation of activities involving geographic data map production calculation of areas, distances, route lengths

measurement of slope, aspect, viewshed

logistics: route planning, vehicle tracking, traffic management 

Allow for the integration of data previously confined to independent domains (e.gproperty maps and air photos)

By tying data to maps, permits the succinct communication of complex spatial patterns (e.g environmental sensitivity)

Provides answers to spatial queries (how many elderly in the Pittsburgh region live further than 10 minutes at rush hour from ambulance service?)

Perform complex spatial modeling (what if scenarios for transportation planning, disaster planning, resource management, utility design)

Five “M’s” of GIS Applications:

1. Mapping Traditional Output – Perhaps the least powerful output

Maps are source of input data too (data capture)

2. Measurement Extracting distance information from data i.e., stream length from A location to B location

3. Monitoring Accessing information spatially and temporally 

4. Modeling Assembling the data housed in the hardware in an organized and analytical 

manner in the software for ‘knowledge’ extraction

5. Management The creation, deletion, storage, organization, updating and archiving of data

Theory

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Functional Elements of a GIS The Functional “Steps” in a “Typical” GIS Project

I.  Data acquisition (never underestimate the cost!) paper maps

digital files

remote sensing/satellite

fieldwork

II. Preprocessing: preparation & integration format conversion

digitizing and/or scanning

edge matching and rectification

III.  Data Management

variable selection & definition

table design (performance v. usability)

CRUD policies/procedures: Create (data entry), Retrieve (view), Update (change), Deletion (remove) 

IV.  Manipulation and Analysis (all the user cares about!)

address matching

network analysis

terrain modeling (e.g. slopes, aspects)

V.  Product Generation

tabular reports

graphics (maps and charts)

Practice

Steps FOLLOWING project scoping:

The GIS Data Model: Geographic Integration of Information

• Data is organized in layers, coverages orthemes (synonomous concepts), with each theme representing some phenomena on the earth’s surface

• Layers are integrated using explicit location on the earth’s surface, thus geographical location is the organizing principal.

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Changing Domain and Role of GIS

2010

1998

1992

1985

Source: Forer and Unwin, 1998

Evidence and Wisdom:

• Evidence is somewhere between Information and Knowledge

• Evidence –• can be a thing or things helpful in forming a conclusion or judgment

• to indicate clearly; exemplify or prove

• in science, evidence usually goes toward supporting or rejecting an hypothesis

• scientific evidence is usually empirical 

• Wisdom is at the top of the decision making process hierarchy

• Wisdom –• knowledge of what is true or right coupled with just judgment as to action

• Insight into a process (whether physical or conceptual)

• the ability to optimally (effectively and efficiently) apply perceptions and knowledge and so produce the desired results

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Validation vs. Verification

• Validation‐• The process of checking to see if something satisfies a certain criteria

• to give official sanction, confirmation, or approval to; substantiate

• Models are often validated

• Verification‐• evidence that establishes or confirms the accuracy or truth of something

• the process of research, examination, etc., required to prove or establish authenticity or validity of results

• Results should be verified (but this is rarely done)

Qualitycontrol

Qualityassurance

Problem Solving:

• How do we solve problems?• Do we first define what we want to know?

• Are we confronted with a situation in which we have no solutions or answers?

• What is the difference between a solution and an answer?

• We must define the problem‐

• We must determine what kind of data is needed to provide a solution to the problem

• Then, we must understand how to make information from the data

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From Data to Information to Knowledge:  Basic way:

Categorization of Data (idea)User‐determined characteristics to be sought out in the data

Used to Identify patterns in data

Patterns are interpreted as information

Information used in problem solving

Classification of Data (method)Method to determine differences or similarities of data based on knowledge (often based on rules)Rules are determined on agreed upon procedures

Should be based on knowledge (and a little wisdom, too)

How Do We Represent These Data Types in a GIS? 

•The Raster and Vector data models…

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Rasters and Vectors

• How to represent phenomena conceived as fields or discrete objects?

• Raster• Divide the world into square cells

• Register the corners to the Earth

• Represent discrete objects as collections of one or more cells

• Represent fields by assigning attribute values to cells

• More commonly used to represent fields than discrete objects

0 1 2 3 4 5 6 7 8 90 R T1 R T2 H R3 R4 R R5 R6 R T T H7 R T T8 R9 R

Real World

Vector RepresentationRaster Representation

Concept of Vector and Raster

line

polygon

point

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Characteristics of Rasters

• Pixel size• The size of the cell or picture element, defining the level of spatial detail

• All variation within pixels is lost

• Assignment scheme• The value of a cell may be an average over the cell, or a total within the cell, or the commonest value in the cell

• It may also be the value found at the cell’s central point

Legend

Mixed conifer

Douglas fir

Oak savannah

Grassland

Each color represents a different value of a nominal-scale field denoting land cover class

Raster representation

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Data Sets: then array is populated with data

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2. Urban (High-Density Built-Environment)

3. Residential (Low-Density Built-Environment)

4. Agriculture / Grass (Open Space)

5. Forest

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Data Sets: data is then coded and displays like‐patterns (using color)

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4. Agriculture / Grass (Open Space)

5. Forest

Data Sets: but is still just data until the user makes information from it

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4. Agriculture / Grass (Open Space)

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Representing Data using RasterModel

• Area is covered by grid (usually) with equal‐sized cells

• Location ‐ of each cell calculated from origin of grid: “two down, three over” • (usually from upper left, but lower left in ARCVIEW)

• Called raster cells or pixels (picture elements) • raster data often called image data 

• Attributes ‐ are recorded by assigning each cell a single value based on the majority feature (attribute) in the cell, such as land use type

0 1 2 3 4 5 6 7 8 90 1 1 1 1 1 4 4 5 5 51 1 1 1 1 1 4 4 5 5 52 1 1 1 1 1 4 4 5 5 53 1 1 1 1 1 4 4 5 5 54 1 1 1 1 1 4 4 5 5 55 2 2 2 2 2 2 2 3 3 36 2 2 2 2 2 2 2 3 3 37 2 2 2 2 2 2 2 3 3 38 2 2 4 4 2 2 2 3 3 39 2 2 4 4 2 2 2 3 3 3

commercial

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Representing Data using RasterModel

• Easy to do overlays/analyses, just by ‘combining’ corresponding cell values: • “crime index” = juvenile + adult crime rate

• (I think it’s potentially improper…)

• why raster is faster, at least for some things

• Simple data structure:• directly store each layer as a single table 

• analogous to a “spreadsheet”)

• no computer database‐management‐system (DBMS) required

• although GIS systems incorporate them

commercial

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Legend

Mixed conifer

Douglas fir

Oak savannah

Grassland

Each color represents a different value of a nominal-scale field denoting land cover class

Raster representation

Raster Data Sets:• Data – Landsat TM(1992) & ETM+ (2002)

10/02/1992 10/06/2002

17/32 17/32

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Classified Data:

Vector Data

• Used to represent points, lines, and areas

• All are represented using coordinates• One per point

• Areas as polygons• Straight lines between points, connecting back to the start

• Point locations recorded as coordinates

• Lines as polylines• Straight lines between points

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Representing Data using the VectorModel: General concept

The fundamental concept of vector GIS is that all geographic features on the earth’s surface (or on a map) can be represented either as:• points (no area): trees, sample locations, crime locations, rocks, manhole covers• lines (arcs): streets, sidewalks, transmission lines, streams• areas (polygons): cities, counties, buildings, states, ponds and lakes, land uses, land 

covers

• Which is used in a particular instance depends on scale, among other things: • for example – Leonard Hall may be a point or polygon

• Because representation depends on shape, ArcView refers to files containing spatial data as shapefiles.

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Representing Data using the VectorModel:

• Point  (node): 0‐dimension• one x,y coordinate pair• zero area• tree, oil well, label  location 

• Line  (arc): 1‐dimension• two (or more)  connected  x,y

coordinates• road, stream

• Polygon : 2‐dimensions• four or more ordered and connected 

x,y coordinates • first and last x,y pairs are the same• encloses an area• census tracts, county, lake 

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Point: 7,2y=2

Line: 7,2 / 8,1

Polygon: 7,2 / 8,1 / 7,1 / 7,2

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Latitude = YLongitude = X

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Latitude is the Y axis, longitude is the X axis

Latitude lines run east‐west. They are parallel to each other. The further north you go is determined by latitude. And latitude values (Y‐values) range between ‐90 and +90 degrees

Longitude lines run north‐south. They converge at the poles. And X‐coordinates are between ‐180 and +180, which are called longitudes.

Attribute data on the right are linked to spatial data by the label ID of map features

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Representing Data using the VectorModel:Data implementation

Coordinates TablePoint ID x y

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YCommon identifiers provide link to:

coordinates table (for where)

attributes table (for what or when)

Point ID Crime time1 robbery 9:302 assault 11:303 drug 5:004 robbery 2:305 b & e 3:00

Concepts are those of a relational data base, which is really a prerequisite for the vector model (or need object-oriented computing environment)

Features in the theme (coverage) have unique identifiers--point ID, polygon ID, arc ID, etcUsually referred to as the Feature ID (FID)

(Vector) Topology: What is it?Programmed rules in GISs like ArcGIS that establish spatial relationships between features (points, lines, polygons)

• Spatial Relationships• each arc has a beginning and 

ending node

• arcs connect to other arcs at nodes

• connected arcs form polygon boundaries

• arcs have polygons on their left and right

• containment

• Spatial Properties• length, directionality of arc

• connectivity

• area, perimeter of polygons

• adjacency or contiguity

• point or line ‐in‐polygon

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The Vector Data Model: Topological vs. Non‐topological Spatial Data

• Topological Vector Data – contains data regarding the (spatial) relationships between features

• Ex. ArcGIS coverages, geodatabases

• Non‐topological – no data regarding spatial relationships is recorded or stored

• Ex. ArcGIS shapefiles

The data structure of a point data model

Point (node): 0-dimension• single x,y coordinate pair• zero area(Example: tree, oil well, label location)

(0,0)

3 (2,2)

2 (4,4)

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1 (2,9)

ID X,Y

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4 6,2

Point List

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The data structure of a line data model

Line (arc): 2-dimension• From Nodes (points) – T Nodes (points)•x,y coordinate pair(s) depend # of Nodes and vertices• zero area(Example: tree, oil well, label location)

The data structure of an area (polygon) data model

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GIS  Data Models:   Raster vs. Vector

• Raster data model• location is referenced by a grid 

cell in a rectangular array

• attribute is represented as a single value for that cell

• many data comes in  this form • images from remote sensing 

(LANDSAT, SPOT, QuickBird)

• scanned maps

• elevation data from USGS

• best for continuous features:• elevation

• temperature

• soil type

• land use

• Vector data model• location referenced by x,y,z

coordinates, which can be linked to form lines and polygons

• attributes referenced through unique ID number to tables

• many data comes in this form• DIME and TIGER files from US 

Census

• DLG from USGS for streams, roads, etc

• census data (tabular)

• best for features with discrete boundaries

• property lines

• political boundaries

• transportation

Vector and Raster Comparison and Conversion 

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Do you see a pattern here?

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Review: Representing Geography

• When representing real‐world geographic objects in a digital form, there are 3 governing principles:

1. The representations in a GIS have spatial coordinates

2. The characteristics of the representations are selective (based on what attributes we wish to represent), and therefore incomplete

•The world is infinite and the computer is finite

3. We use models to represent the real‐world in two ways:•As clearly defined, discrete objects with known shapes or boundaries 

•(represented as vector data models)

•As continuous data that extends over space, that we think of as fields •(represented as raster data models)

EnvironmentalSystemsResearchInstitute

ArcGeographicInformationSystemsv. 10

Today:

• Licensing options• ArcView• ArcEditor• ArcInfo

• GUI description

• Components of ArcGIS• ArcMap• ArcCatalog• ArcToolbox

• Setting up your project• Data organization / workspace• ‘Geodatabases’• Project files (.mxd)• Relative & absolute paths• Data Frame Properties

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ArcGIS Desktop – 3 license levels

ArcGIS Desktop is available in different levels, each with additional functionality

The 3 levels are:

1. ArcView     BASIC

2. ArcEditor STANDARD

3. ArcInfo ADVANCED

ArcView

ArcEditor

ArcInfo

ArcGIS Desktop – ArcViewWith ArcView, you can• Author maps and interact with your data by generating reports and charts and printing and embedding your maps in other documents and applications

• Use map templates to create consistent style in your maps

• Build process models, scripts, and workflows to visualize and analyze your data

• Read, import, and manage more than 70 different data types and formats including demographics, facilities, CAD drawings, imagery, Web services, multimedia, and metadata

• Print, publish, and share GIS data 

• Use tools such as Find, Identify, Measure, and Hyperlink

ArcView

ArcEditor

ArcInfo

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ArcGIS Desktop – ArcEditorWith ArcEditor, you can• Have multiple users to simultaneously modify and edit data

• Build and maintain spatial relationships between features using topology rules and a process called validation

• Support multiple workflows, manage work order processing, and implement QA procedures for validating edits

• Monitor the database over time and evaluate what‐if scenarios

• Perform raster‐to‐vector conversion and create data from scanned maps

ArcView

ArcEditor

ArcInfo

ArcGIS Desktop – ArcInfoWith ArcInfo, you can• Perform advanced GIS data analysis and modeling

• Use tools designed for overlay analysis, proximity analysis, surface analysis, and raster processing and conversion

• Publish and convert data in many formats

• Create and manage personal geodatabases, multiuser geodatabases, and feature datasets

• Use high‐end cartography tools to generate professional‐quality, publication‐ready maps

• Design customized symbols and place sophisticated annotation and labels on your maps

ArcView

ArcEditor

ArcInfo

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ArcGIS – How is it Structured?

• Mapfile – What is it?• Document that…

• Data Frame – What is it?• Organizational entities…

• Data Layer – What is it?• Spatial data sets…

ArcGIS Mapfile

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ArcGIS Mapfile

• How do mapfiles work?• They contain pointers to spatial and non‐spatial data 

• (not the data itself)

• You save them as .mxd files and as long as they have access to the base data, you are in “good shape”

• Example • H:\introgis\streams.shp 

• or E:\assignment1\streams.shp

Mapfile Example

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ArcGIS: Data Frames and Data Layers –What is the Difference?

• Data Frames • Represent some “domain” in space that can have some properties (that you can assign)

• Scale, coordinate system, labeling, etc.

• Contain data layers that occur within this domain that are in the same coordinate space

ArcGIS: Data Frames and Data Layers –What is the Difference?

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ArcGIS: Data Frames and Data Layers –What is the Difference?

• Data layers are graphic representations of spatial data layers (in a map file)

• They are accessed by “adding data” to a mapfile…• the data will then appear in the table of contents

• Data layers also have properties that are separate and distinct from those of data frames

• sometimes this is hard to keep track of…but we’ll manage

ArcGIS – Data Layers

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Organization of your data/workspace

• Workspaces are the directories, databases, and file folders on disk that hold numerous datasets, including geodatabases, as well as external datasets in many file formats—for example, folders containing shapefiles, JPEG images, DXF CAD files, dBASE tables, or Excel spreadsheets

• You can create and manage your workspaces in ArcCatalog as well as make connections to other workspaces

• Your workspaces may consist of a single file folder containing many datasets and other related documents that are organized around themes or projects

Organization of your data/workspace

• You can organize projects by layer type such as workspaces for roads, water, parcels, administrative boundaries, and so forth

• For example, the Shopping Mall Project workspace organizes datasets in folders by the theme of the data

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Organization of your data/workspace

• In other situations, you may want to organize workspaces around a project such as a road development project or new power plant project

• You can also organize workspaces within workspaces

• The New Power Plant workspace below contains sub‐workspaces for each element of the major development project

Proper organization of your data and workspace results in a logical ‘catalog tree’

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Graphical User Interface (GUI) /ArcMap Display Interface

Setting up your Project File (.mxd)

File → Map Document Proper es

Go to ‘Pathnames’ and click ‘Store relative pathnames to data sources’

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First thing to do is to ‘tell’ .mxd/project file which coordinate system, projection, and datum to use in the data frame

Setting up your Project File (.mxd)

Go to the ‘View’ pull‐down menu and navigate to ‘Data Frame Properties’

In the ‘Data Frame Properties’ click the ‘Coordinate System’ tab…Now what???

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Querying the database,Boolean Operators, 

ANDTopological OVERLAY FUNCTIONS 

IN gis(or, ‘Geoprocessing’)

Multiple Layer Operations

• How can we conceptualize “multiple layer” operations?

2 Kinds:• Those that do not change the geometry of spatial data

• Those that do change the geometry of spatial data, necessitating changes and updates to (vector) shapes     

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Multiple Layer Operations

• Multiple layer operations that do not change the geometry of spatial data

• Select by Location• Spatial relationship is specified, spatial features are selected, but spatial data is not geometrically altered

• Spatial Join• Data is appended from a “source” spatial data table to a “destination” spatial data table based on common location, again spatial data is not altered  

Select by Location

• Relationships between layers (that you can specify):• Intersect

• within distance of

• Completely contain

• Are completely within

• Have their center in

• Touch the boundary of

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The GIS Data Model: Geographic Integration of Information

• Data is organized in layers, coverages or themes (synonymous concepts), with each theme representing some phenomena on the earth’s surface

• Layers are integrated using explicit location on the earth’s surface, thus geographical location is the organizing principal

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Queries

• Aspatial Queries –• Querying the attribute data using commands

• Spatial Queries –• Querying the attribute data using the geography of other data

Aspatial Query in ArcGIS

Select by Attributes

Build the SQL statement by simply clicking and double‐clicking

Check you SQL statement with the ‘Verify’ button

Click ‘Apply’ then ‘OK’

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Query – asking a question of the attribute data

Standard Query Language (SQL) is used to query the data

There are 4 basic statements used to get information from 2 (or more) datasets

AND – if you are desiring the subset of each dataset that is ‘true’ of both datasets

OR – if you are desiring the subset of each dataset that is ‘true’ of either one or     both datasets

NOT – if you are desiring the subset of one dataset that is only true of one dataset

OR, BUT NOT BOTH (XOR) – if you are seeking the subset of data that is ‘true’ of  one and another dataset, but not both datasets

Boolean Operators:   AND

A B

A AND B = True if Both

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Boolean Operators:   AND

A AND B = True if Both

Boolean Operators:   OR

AA B

A OR B = True if one or other

A

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Boolean Operators:   OR

A

A OR B = True if one or other

Boolean Operators:   NOT

AA

A NOT B = True if Neither

B

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Boolean Operators:   NOT

AA

A NOT B = True if Neither

Boolean Operators:  A XOR B

A B

A OR B, but not both (XOR)

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Boolean Operators:  A XOR B

A OR B, but not both (XOR)

Polygon on Polygon Vector Overlay Operations 

CLIP

SELECT (NOT)

SPLIT

XOR

UNION

INTERSECT

INPUT LAYER 1 INPUT LAYER 2 OUTPUT LAYER 

Splits 1 into manylayers based on 2

Overlays polygons and keeps all of both [1 OR 2]

Overlays but keeps onlyportions of layer 1that fallwithin layer 2 [1 AND 2]

Cuts out a piece of layer 1using layer 2 as cookie cutter[1 AND 2]

Erases (deletes) part of layer 1using layer 2 [1 NOT 2]

Layer 1 or 2, but not both[1 XOR 2]

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Line in Polygon VectorOverlay Operations 

Point in Polygon Vector Overlay Operations 

INPUT LAYER 1

INPUT LAYER 2

OUTPUT LAYER 

INPUT LAYER 1

INPUT LAYER 2

OUTPUT LAYER 

Even or Zero intersects to each side = NO

Odd intersects to each side = YES

MinimumBounding Box –

MinimumBounding Box –

Even or Zero intersects to each side = NO

Odd intersects to each side = YES

Also called the ‘even-odd rule algorithm’

Multiple Layer Operations

• Types of geoprocessing operations• Dissolve

• Append (spatial join)

• Clip 

• Intersect 

• Union

• Buffer

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Dissolve Operation

Change in geometry based on common attribute values

Clip Operation

The “Clip feature” is used as a cookie cutter

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Buffer Operation

Proximity is measured from target features

Union

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Union

Union and Intersect

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Union is an AND operation that produces a 3rd output dataset

BCA

Data Capture• Primary Sources• Secondary Sources

• Vector• Raster

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Overview

• Introduction

• Primary data capture

• Secondary data capture

• Data transfer

• Capturing attribute data

• Managing a data capture project

Data Collection• One of most expensive GIS activities

• Many diverse sources

• Two broad types of collection– Data capture (direct collection)– Data transfer

• Two broad capture methods– Primary (direct measurement)– Secondary (indirect derivation)

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Data Collection Techniques 

Raster Vector

Primary

Digital remote sensing images GPS measurements

Digital aerial photographs Survey measurements

SecondaryScanned maps Topographic surveys

DEMs from maps Toponymy data sets from atlases

Stages in Data Collection Projects

Planning

Preparation

Digitizing / TransferEditing / Improvement

Evaluation

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Primary Data Capture

• Capture specifically for GISci use

• Raster – remote sensing– e.g. SPOT and IKONOS satellites and aerial photography

– Passive and active sensors

• Resolution is key consideration– Spatial

– Temporal

– Spectral

Vector Primary Data Capture

• Surveying– Locations of objects determines by angle and distance

measurements from known locations

– Uses expensive field equipment and crews

– Can also be performed on an airborne platform

– LiDAR, Radar, etc.

– Most accurate method for large scale, small areas

• GPS– Collection of satellites used to fix locations on Earth’s surface

– Differential GPS used to improve accuracy

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Total Station

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Secondary Geographic Data Capture

• Data collected for other purposes can be converted for use in GIS

• Raster conversion– Scanning of maps, aerial photographs, documents, etc

– Important scanning parameters are spatial and spectral (bit depth) resolution

Vector Secondary Data Capture

• Collection of vector objects from maps, photographs, plans, etc.

• Digitizing– Manual (table) – Heads-up and vectorization

• Photogrammetry – the science and technology of making measurements from photographs, etc.

• COGO – Coordinate Geometry

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Scanner

The most prevalent wayTo capture non-digital raster data

Scanners are:•Flatbed•Scrolling•Drum

Vector Over Raster

‘Heads-up’ data capture means the by-handdigitization of objects in vector format from a raster data source

i.e., tracing by hand-

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Digitizer

(A) undershoots and overshoots(B) invalid polygons

(C) sliver polygons

Types of human errors in digitizing:

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Error induced by data cleaning

Mismatches of adjacent spatial data sources that require rubber‐sheeting

Rubber-sheeting – is a procedurefor adjusting the coordinates of allthe data points in a dataset that allow a more accurate match between known locations and a few data points within a dataset

Rubber-sheeting preserves the Interconnectivity between points and objects through stretching, orreorienting their interconnectinglines

Object edges don’t align

Object edges align afterrubber-sheeting, butsometimes itcauses problemselsewhere