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Introduction to GIS / GISc01 / 23 / 2015

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)

Contributing Disciplines to GIS: IThe convergence of technological fields and traditional disciplines

Geography “thinking” spatially

long tradition in spatial analysis

provides techniques for conducting spatial analysis

Cartography concerned with the display of

spatial information

maps have been a major source of information input for GIS

long tradition in map design which is an important output from GIS

Remote Sensing

images from air and space are a major (& growing) source of spatial data

low cost and consistent update of input data anywhere in the world

interpreted data from remote sensing can be merged with other GIS data

Photogrammetry

uses aerial photographs for making accurate spatial measurements

source of most data on topography (elevation) used in GIS

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Contributing Disciplines to GIS: IIThe convergence of technological fields and traditional disciplines

Geodesy

Source of high accuracy positional control for GIS

GPS (global positioning system) technology is revolutionizing efficiency, cost, and accuracy

Statistics many GIS models are statistical

many statistical techniques used in GIS analysis

statistics important to understanding issues of error and uncertainty in GIS data

Computer Science earlier computer-aided design (CAD)

work in CS

computer graphics and visualization

database management systems (DBMS)

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)

Research Question (example)

What do we want to know?How much and what kind of land-use changes occurred in southwestern PA from 1992 to 2002?

What kind of data is needed to provide a solution to the problem?

Satellite image data (raster) from 1992 and 2002

How will we process (make information) from the data?Categorize then classify the data, then search for differences between the two years

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Supervised Classification Anderson Level II (idea to categorize the data)

Maximum Likelihood (Probability) (method to classify the data)

Recoded to Five basic information classes (data categories)1. Water2. Urban (High-Density Built-Environment)3. Residential (Low-Density Built-Environment)4. Agriculture / Grass (Open Space)5. Forest

Image Differencing Technique ‘Matrix’ Analysis

Research Question (example)

How much and what kind of land-use changes occurred in southwestern PA from 1992 to 2002?

Data – Landsat TM(1992) & ETM+ (2002)

10/02/1992 10/06/2002

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Data Sets:

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Data Sets: Raster (starts with an array)

Data Sets: then array is populated with data

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5. Forest

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

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

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The GIS Model: example

Here we have multiple layers:--vegetation --soil--hydrology

They can be related because precise geographic coordinates are recorded for each layer.

longitude

Layers may be represented in two ways:•in vector format as lines•in raster(image) format as pixels

Tableof

ZonalResults

Matrix

2002Classified

Image

1992Classified

Image

25 from-to land-

use classes

Analysis Model:

Model was validated by others with knowledge that the process was sound and that it wouldprovide the intended results required to answer the problem:

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SpatialAnalysisClassifiedResults:

An attempt to verify the results of the analysis was performed

‘Accuracy Assessment ‘

256 points were verified out of millions of pixels

Accuracy Reports Overall Classification

Accuracy: 1992 – 82%

2002 - 82%

Change Detection

Accuracy: 67%

Verification of Results

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Information Class Change Distribution by Pixel Count (Percentage)

2002 Water Urban Residential Forest AgGrass 1992 1 2 3 4 5 TOTAL

109,877 12,883 438 90 436 123,724 Water 1 (89%) (10%) (<1%) (<1%) (<1%)

17,977 227,855 235,707 25,919 211,610 719,068 Urban 2 (3%) (32%) (33%) (4%) (29%)

434 57,175 1,069,053 287,927 126,498 1,541,087 Residential 3 (<1%) (4%) (69%) (19%) (8%)

267 36,924 586,994 8,506,123 496,285 9,626,593 Forest 4 (<1%) (<1%) (6%) (88%) (5%)

224 81,399 1,293,977 1,329,593 4,733,095 7,438,288 AgGrass 5 (<1%) (1%) (17%) (18%) (64%)

TOTAL 128,779 416,236 3,186,169 10,149,652 5,567,924

Categorized Tabular Results:

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Forest to Urban Forest to Residential Forest Open space

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Combine Data for further Information

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At- Risk Municipalities

Query Information for Knowledge