Fall 2018 offering of Geog 355: Delaware Data, Midterm, and Project

September 30, 2018

Regardless of your position on police bunnies, we are nearing the halfway point of Geog 355 and I have a few updates for you:

  1. You are diligently compiling your progress on the readings and ArcGIS tutorial, on a blog created for the course. Please continue to update me as you add blog postings. Roughly follow the course schedule.
  2. Get going on the Delaware Data Inventory, and complete (with a blog posting indicating such) by Sunday, October 14.
  3. Review the postings from last spring on the Delaware Neighborhood project:
    1. https://gisci.wordpress.com/2018/01/23/week-2-updates/
    2. https://gisci.wordpress.com/2018/01/30/week-3-updates/
    3. https://gisci.wordpress.com/2018/02/09/week-4-updates/
  4. Janelle is working on getting some background information on the Neighborhood Mapping project and she and I will be in touch about the project in the near future.
  5. Once you finish the Delaware Data Inventory and ArcGIS tutorial, you can get going on the Midterm Evaluation. This will be a butt-load of fun and will put your brain deep into the world of doing stuff with GIS. Due Friday, October 26.

Week 3: Updates

January 30, 2018

After our rousing meeting on Monday a few things can be reiterated:

  1. Please create your class blog and get me the URL by Thursday
  2. Please consult the course schedule and make a single weekly blog posting that includes
    1. reading notes (so I know you read the stuff)
    2. progress on the tutorial (when we get to it)
    3. project ideas, work on proposal, and updates
    4. other pertinent stuff (such as progress on your writing option, if you are doing one for this course)
    5. email me when you post your weekly post (no later than the Sunday at the end of the week)
  3. Lets plan to meet on Mondays at 1-2pm until further notice.
  4. I’m going to look into ArcOnline accounts for us
  5. We seem to be settling into the Neighborhood Mapping project
    1. scale is an issue:
      1. Delaware has wards (four of them, look it up)
      2. Below that scale, there are neighborhoods (“Downtown”)
      3. Below that scale, there are smaller neighborhoods (“Downtown SoWill” – downtown south of William St.)
    2. sometimes neighborhoods correlate with subdivisions (“Wesleyan Woods”): GIS data on subdivisions
    3. subdivisions have HOA boundaries (Home Owner’s Association): GIS data on HOA in Delaware?
    4. demographic data: from US Census: blocks, block groups, Census tracts (see below: two graphics)
      1. demographic data can be used to create neighborhoods, but this is a bit dangerous (why?).
      2. demographic data can be associated with neighborhoods (say, poverty rate), but this might also be a bit dangerous (why?)
      3. if the City wants to be able to match demographic data to neighborhoods, the neighborhood boundaries have to follow census boundaries (block, block group, etc.). This is important to build into the neighborhood boundary making from the start.

  1. Preliminary proposal, due Friday, February 9. This may be one big proposal, or several related smaller proposals. We’ll figure that out.
    1. Details on Project Proposal
  2. Go back and, individually, complete the Defining and Mapping Neighborhoods stuff from the previous update. Put on ye blog!
  3. Teh major issues: preliminary: these may be sub working groups and writing options if need be:
    1. Literature on Neighborhoods
      1. Academic – Planning, Urban Studies, Geography, etc.
      2. Other literature
    2. Literature and info on methods for determining neighborhoods
      1. surveys
      2. historical research (on Delaware)
    3. Using GIS to map and communicate neighborhoods
      1. including ArcOnline to make the data easily available

Please email or talk to me if you have questions along the way. Bothering me is better than you not doing what you are supposed to do!

Reading: Schuurman ch 2 & 3

February 18, 2014


On no snow for Christmas in Finland

Schuurman: GIS: A Short Introduction

Ch. 2: GIS, Human Geography, and the Intellectual Territory Between Them

GISystems & GIScience based on assumptions that privilege certain approaches to understanding the world (natural and human).

Geography: diverse, undisciplined discipline, origins in 1800s

GIS since the late 1960s, parts of cartography & quantitative methods

  • sometimes rocky relationship between these and Geography in general

Mind the Gap: The Distance Between Human Geography and GIS

Little overlap between GIS and Human Geographers until the late 1980s

Geographers critique: GIS is mere technique, no intellectual component

  • GIS processes facts, but can’t generate meaningful understanding
  • GIS based on positivism and/or naïve empiricism: neither well respected approaches/theories in Geography

Positivism/empiricism: experiment/test/trial: sense perceptions are the only admissible basis of human knowledge and precise thought; natural and social processes can be understood (via hypothesis testing and data analysis) and follow strict laws; designed to supersede theology and metaphysics.

  • ex) central place theory in Geography
  • ex) much of science and some social science.
  • ex) less comfort with qualitative methods
  • ex) less comfort with theories that use empirical data but don’t see laws governing human behavior and activity (feminism: role of gender in shaping society, but these are not laws – they can be overcome and changed for the better of all)

1980s: lots of debates

  • GIS people with a more positivistic, scientific approach vs human geographers with more qualitative, social theory approaches
  • GIS very limited view of the world, requires very specific, empirical data, can ask very specific questions, and get very specific results.
  • GIS driven by corporate and military needs
  • GIS expensive and exclusive; elitist

Brian Harley (The Nature of Maps), Denis Wood (The Power of Maps)

  • critique of maps: social constructions for creating and maintaining power
  • selectively show certain things, not others; create and enforce social status quo
  • maps create a space of political territories (broader scale) and privately owned property (detailed scale) and make those human conceptions real in the landscape
  • ex) the “nation” / “states” – relatively new concept; problem in Mid East
  • ex) property ownership: relatively new human concept
  • ex) zoning

Maps created by elites to shape and enforce geographic reality to suit their needs

John Pickles: Ground Truth: apply same critique to GIS

  • GIS is for maintaining order, just like paper maps before them
  • Friday Harbor meeting: beginning of a dialog
  • alternatives: Particapatory GIS, counter-mapping, qualitative methods “GIS2”

Epistemology and Ontology in GIS

  • epistemology: the methods we use to study the world; each has assumptions and perspectives that shape the questions, analysis, and interpretation of results
  • ontology: what things really are (how the world must be to make sense of it)
  • ontology (computer science)

ex) we have extensive GIS technology for determining the fastest route for an ambulance to get a sick person to the hospital, but we don’t ask why so many people get sick.

ex) GIS is used extensively to plan new developments and roads but is very much less able to help understand the extensive negative impact of such development on the environment

ex) Forest in India

Use quantitative methods to test IR energy reflectance from various types of vegetation and different kinds of land cover in an area; gather data, test hypothesis, generate specific measurable value that differentiates a forest from other areas.

  • Use remote sensing to define areas that have a certain % reflection of IR energy; any less than that % is not a forest, any more than that % is.
  • then create a map of forests (and not forests; can do this all in GIS from afar)
  • Empirical epistemology, we can “sense” reflected energy and use that do define and distinguish forest from other areas.
  • Empirical ontology: the world consists of measurable objects, some of which reflect energy and specific kinds of energy reflectance lead us to understand and locate real forests.

Use qualitative methods such as interviews and mental mapping to have different people in the same area of India show where forest is on a map, and describe what forest is to them

  • state foresters: will claim much more territory is in forest as it is their job to preserve and create forested areas
  • farmers: will identify tree covered territory as wasteland or unproductive land, not forest
  • forest dwelling, hunters/gatherers: will focus on areas that are diverse and provide them with food and resources; not “forests” planted by the foresters (not diverse, not a good source of food and resources)
  • a qualitative epistemology: assumes that the reality of “forest” is shaped by human social factors; collect data (mental maps) but interpretation leads to ideas of how a forest is a human construct, even “untouched” forest
  • a qualitative ontology that suggests that sensory measurements in the world are incapable of measuring and helping to understand the social construction of forest; “reality” is shaped and made via social processes.  Social theory explains social processes, but these are not “laws” or unchangeable.
  • Counter Mapping: Peluso: Whose Woods Are These?

Data Models and Ontology

Vector data model: point, line, area (necessary to encode data into the GIS)

  • People: US Census blocks
  • define an area as a particular block, count the number of people
  • all of space is filled with blocks
  • what is the real nature of humans and where they live?

Raster data model: grid of cells

  • land use: each cell (can be very fine) assigned a type of land use based on energy reflected from it
  • complex mosaic of land uses often generalized into agricultural, commercial, etc
  • again, all space is some kind of land use
  • what is the real nature of land use?

Object oriented data models

  • see all geographic features as objects; location as one attribute
  • group together similar objects (roads) and have subclasses (federal, state, local)
  • hierarchical: each “parent” object has attributes common to its subclasses
  • discrete, separate entities in a neutral space; can fill space or not
  • what is the real nature of any geographic feature?  “Forest” as an object?

All data models are reductionist: they simplify complex reality

Need to know how that is occurring and how it shapes understanding when using
GIS or any method

Looking for the Social in GIS

Social aspects of science, technology, GIS

  • ex) funding and research on health biased towards white males

Science as autonomous vs social

Kuhn: The Structure of Scientific Revolutions

  • paradigms: accepted practices and belief systems in science; structure how science is done until enough doubt is cast to accept a new system.
  • what is to be observed and scrutinized, the kind of questions that are supposed to be asked and probed for answers in relation to this subject, how these questions are to be put, how the results of scientific investigations should be interpreted.
  • ex) Copernicus proposed a cosmology with the Sun at the center and the Earth as one of the planets revolving around the Sun.

GIS technology: how have applications developed for the military and environmental science come to shape studies using GIS for non-military and non-environmental science applications?

What is the point ?

GIS is growing rapidly as a method used by diverse people and for diverse applications use growing faster than an understanding of the assumptions and limits of GIS particularly more conceptual, theoretical, even philosophical issues

Important to approach and use GIS with a few things in mind

  • it does tend to privilege one of many approaches to understanding the world: empirical, positivist, scientific; it is not the only way to address particular issues and is not “neutral” or “objective” or necessarily better than other approaches with different assumptions.
  • it is connected to social context: it is a powerful, persuasive tool that has been developed for military and government applications; it is used by experts with training and organizations with big budgets and power; it is in many ways a very elitist method for understanding the world.
  • GIS is always in flux: GIS is not set in stone: development of internet GIS: GoogleMaps and GoogleEarth and slew of similar applications: how will this more “populist” GIS open the door to different kinds of GIS and GIS analysis?  GIS technology will always evolve within a social context.

Schuurman GIS: A Short Introduction

Ch. 3: The Devil is in the Data: Collection, Representation, and Standardization

“Data are not the transparent manifestation of reality in digital terms.  They are the expression of particular points of view and agendas that begin as observations, and are transformed into numbers in data tables that provide the basis for spatial analysis.”

“Data are an artifact that reflects people, policy, and agendas.”

The Politics and Practicalities of Data Collection

Human data collection: often by area (Census block, zip code)

  • U.S. Census: Politics of counting people
  • Census count leads to allocation of money, voting districts
  • undercount of homeless, poor, minorities (3.3 million in 2000)
  • Pima Co. Az: 15,000 undercount, $30 million loss in funds
  • Delaware Co. OH: undercount of about 8000
  • statistical sampling can correct (opposed for Political reasons)

Environmental data collection: often by location

  • GPS: relatively to very accurate locations
  • Military origins; selective availability; competing system (Galileo, EU)
  • primary (collect yourself) vs secondary (use already collected) data

Organizing Data

  • table of data in GIS: like a spreadsheet: ArcGIS Demo
  • spatial data: location (in some coordinate system): where
  • attribute data: describe the spatial data: what
  • consistency: should not be gaps or missing data (although common)
  • scale: large vs small scale maps;
  • scale does not exist in computers; generalize to view: ex) Google Maps
  • scale at which data has been collected (detailed vs general): ex) DALIS data vs. ESRI data
  • aggregation: group of Census blocks > Block group > Census Tract: ex) Geog 222 Exercise 6
  • data interpolation: filling in missing values: terrain shading, temperature

Metadata: Data about Data

Sharing data leads to the need to know about data: when collected, at what scale, who…

  • ex) DALIS data

Sharing Data: Interoperability

  • “a common language for computational environments”
  • cross-platform and cross-software data compatability
  • like a text (.txt) file

Semantic interoperability: the practical problem associated with “philosophical” issues

  • ex) pond: means different things to different people/institutions, thus different in different data sets: how to integrate?
  • ex) wildlife biologists (forest classified in terms of habitat vs foresters (forest classified in terms of resource assessment)
  • ex) different ways of defining what a road isuse metadata to assess these differences
  • they will always exist: ignoring them can lead to problems

Moral of the story: data are not reality!


“Data are compiled with a particular purpose in mind, and they reflect the assumptions and preconceptions of both the data collectors and data users.  They are, in fact, stories about the world that change depending on the teller.”

  • data is the basis of all GIS analysis
  • not a matter of good or bad data
  • not a matter of more or less accurate data
  • but a matter of the appropriateness of the data to a particular task
  • metadata clarifies the story the data can tell: who collected it with what assumptions under what conditions and for what purpose
  • vital to be critical and understand your data, not just take it as a given

Reading: Schuurman ch 4 & 5

February 18, 2014


Maurizio Cattelan  Love Saves Life  1995

Schuurman: GIS: A Short Introduction

Ch. 4: Bringing it All Together: GIS Analysis

GIS is often used to store data; analysis greatly extends the functionality of  GIS by allowing us to learn more about the stored data

Cadastral systems: property and attribute information (Delaware DALIS project)

  • storing data vs analysis (how many residential properties within 1000’ of river)

Examples of analysis:

  • measurement & distance calculation (perimeters, areas, line lengths)
  • point in polygon queries: does a point lie in an area?
  • shape analysis: shape of a line to assess difficulty of driving on a road
  • edginess analysis: deer habitat (prefer ediginess, forest grass boundary)
  • slope calculation

Overlay Analysis, Set Theory, Map Algebra

  • query: a question (show all owl locations > 500’ from road)
  • buffer: an area around a pt, line, area (show residences within 500’ of liquor lic. Appl.)
  • overlay analysis (find all soils of a particular type within a floodzone)
  • difficulty of polygon overlay: extensive calculation
  • set theory & map algebra: mathematical basis of GIS analysis

Spatial Analysis in the Field: Environmental Modeling

  • ex) modeling industrial pollution
  • predict the impact of a new industrial development in a particular location; help
  • in decision-making
  • air emission, noise, risk
  • link environmental modeling to spatial data

Building Intuitive Models: Multi-Criteria Evaluation

  • location decision analysis
  • find the best location for a new industrial development, given multiple criteria (away
  • from people because of pollution; near necessary transportation corridors, labor).
  • ex) locating a dump
  • factors and criteria: p. 110
  • resulting map: worse and better locations: p. 111

The Power of the Eye: Visualization and the New Cartography

  • ex) TB example

From Data to Analysis: A Case Study of Population Health

  • ex) population health: relating housing to health
  • ecological fallacy: aggregation or scaling introduces bias (p. 120)

MCE and Analysis

  • example of health vs density of population

Calculation and the Rationalities of GIS

  • critical perspectives

Ch. 5: GIS Training and Research

  • GIS is slow
  • Evolving research in GISci
  • Not ontology again!
  • Feminism and GIS
  • Systems vs Science

Some Resources:

W J 24: The Geographic Analysis Process: Mitchell ch. 1 + Projects

January 27, 2014


Mitchell: The ESRI Guide to GIS Analysis, ch. 1

GIS technology 30 years old

Good for making maps: but can do more than that: GIS Analysis

  • maps (and GIS) don’t just show us what exists, the help us discover new things, help make decisions
  • maps result from GIS analysis: have important impact (visual)

Why GIS might not be used:

  • lack of data (changing rapidly, but still a problem)
  • difficult software (but now easy interfaces; still complex though)
  • lack of understanding about GIS analysis capabilities (the book)
  • where are things in geographic space?
  • mapping variations in amount: least and most
  • mapping density
  • finding what is inside
  • finding what is nearby
  • mapping change

What is GIS Analysis?

GIS Analysis as a process

  • simple visual analysis to complex digital modeling
  • akin to the research process

1. Frame the Question:

  • where are endangered ecosystems in Delaware County?
  • where are potential recreational trail corridors in Delaware County?
  • how can viable OWU food waste be efficiently distributed to area food banks?
  • where does the food sold on campus come from, and what are the consequences of our consumption of these foods?
  • what are the bird habitats on campus and how can they be enhanced?
  • how can Delaware Run be restored in a campus-community-private sector collaboration?
  • how can urban heat islands in central Ohio be assessed? Using what tools?
  • how can drones be used with other remotely sensed imagery to assess environments?
  • who is your audience?  what is your final goal?

2. Understand your Data

  • what is the context of your question?  who are the experts?  literature, people
  • what do you have to know about the context of the question to answer it?
  • best to do solid research first then start to ask/bother people: they are apt to be more helpful if you come to them knowing something
  • what is an endangered ecosystem?  what are specific examples?
  • what are the goals of recreational trails?  what do they connect?
  • how is food waste reuse assessed and how is it collected?
  • what or who can help you to understand the issue: literature, people

3. Choose a Method

  • what data is available to help answer your question?  source? cost? compatibility?
  • what data do you have to generate yourself? easy vs. difficult vs impossible
  • what specific data will you need for your project?

4. Process the Data: specific analysis

  • ex) generate endangered areas by comparing areas defined as important ecosystems to their closeness to recent development
  • ex) generate potential trails by generating important points and areas to connect; and determining feasible paths between those points; relate potential trails to property ownership and other factors
  • ex) generate a plan for distributing food waste from campus to area food banks
  • ex) analyze the global impact of specific food consumption on campus
  • what kind of GIS or other analysis will you need to understand for your project?

5. Look at the Results

  • generate a map (with a database) and use it to present results
  • ex) map of endangered ecosystems in Delaware Co: distribute to ??
  • ex) map of potential trails in Delaware Co.: planners, bike clubs, etc.
  • ex) a map that guides distribution of OWU food waste
  • ex) map of the global impact of what we eat
  • vital part of the process: communication and advocacy
  • Simple in concept; complex in application!

Understanding Geographic Features

  • we reduce the complexity of the real world in order to collect data and map it

A feature: “something inherent and distinctive”

Types of features (mappable data)

1. Discrete Features: at any location, the feature is there or is not there

  • point, line, and area example: p. 12
  • corresponds to vector data structure in most GIS programs

2. Continuous Features: feature is everywhere in varying amounts

  • ex) temperature
  • ex) elevation
  • ex) soil or bedrock (Delaware Data)

3) Features Summarized by Area: census or count data

  • define an area; count features in the area; assign total to the area
  • know how many features in an area, but not where they are in the area
    ex) US Census data, animal census

Remember what I said about repetition of some concepts from reading to reading… as a way to assess concepts that are more important…

Two Ways of Representing Geographic Features

1) Vector: points, lines, and areas

  • each point has a unique location in a coordinate system: latitude/longitude
  • points connect to make lines
  • series of points, connected to make lines, which close are areas

2) Raster: grid of varying resolution with cells

  • air photo, remotely sensed image, camera image (drone, thermal data)

Different data structures; can be related in GIS but generated differently and stored and processed differently.

Map Projections and Coordinate Systems

Review from Geog 222 or 353

  • coordinate systems: based on the idea of a graph
  • locations in geographic space: x, y
  • latitude longitude vs state plane coordinate system
  • coordinate layers of GIS information
  • map projection
  • 3D earth to 2D map
  • distortions inherent in process (shape, area)
  • distortions less evident at detailed scales
  • but GIS layers must have same map projection or will not align properly

Understanding Geographic Attributes

  • a geographic feature (point, line, area) has one or more attributes
  • ex) area is a vernal pool, it is 1 acre, it is on private property (3 attributes)

Types of attribute values

  • categories: qualitative
  • ex) vernal pool (area) vs river (line)

Ranks: quantitative with order

  • ex) water quality: high, medium, low

Counts and amounts: quantitative, total numbers

  • ex) 35 robins in one nature reserve, 67 in a second reserve

Ratios: relationship between two quantities

  • ex) people per household in census tracts in Delaware county

Data tables: the ‘database’ or spreadsheet where the feature attributes are found

  • ex) select all properties in Delaware County that are residential land use
  • ex) calculate and summarize the total value of all properties a proposed trail crosses

Course Project Ideas


Below the fold find additional material from previous course projects.

Read the rest of this entry »

M J 23: Geospatial Analysis text: Intro + Conceptual Frameworks

January 21, 2014

Technology shapes how we do things…stairs vs slides in buildings.

First: any additional introductions?

Geospatial Analysis – A Comprehensive Guide

Notes and examples on “Introduction & Terminology” and “Conceptual Frameworks for Spatial Analysis.”


Introduction & Terminology

1. On applications

2. GIS, Spatial Analysis, and Software

3. Terminology & Definitions

Conceptual Frameworks for Spatial Analysis

The Geospatial Perspective: “a distinct perspective on the world, a unique lens through which to examine events, patterns, and processes that operate on or near the surface of our planet.”

The domain of geospatial analysis is the surface of the Earth, extending upwards in the analysis of topography and the atmosphere, and downwards in the analysis of groundwater and geology. In scale it extends from the most local, when archaeologists record the locations of pieces of pottery to the nearest centimetre or property boundaries are surveyed to the nearest millimetre, to the global, in the analysis of sea surface temperatures or global warming. In time it extends backwards from the present into the analysis of historical population migrations, the discovery of patterns in archaeological sites, or the detailed mapping of the movement of continents, and into the future in attempts to predict the tracks of hurricanes, the melting of the Greenland ice-cap, or the likely growth of urban areas.

Geospatial Analysis: what happens where, and makes use of geographic information that links features and phenomena on the Earth’s surface to their locations.

1. Basic “Primitives”

  • place: complicated concept: Wikipedia
  • attributes: “any recorded characteristic or property of a place” + measurement levels (qualitative, quantitative) + examples in ArcGIS
  • objects: raster (images) & vector (points, lines, areas) below (from Making Maps):


justscale generalization

2. Spatial Relationships


  • spatial interpolation: filling in between known data


  • smoothing and sharpening (generalization; see above)

3. Spatial Statistics

4. Spatial Data Infrastructure


  • Interoperability: standards for spatial data (so everything works together): OGC

…All this jargon…



Next: Discuss and brainstorm ideas for course projects + working groups.

Assign: Mitchell ch. 1 (PDF) & refining ideas for the course project (including working groups, division of labor, etc.)

Mitchell Ch. 1 is useful as an overview of the GIS Analysis process. Akin to the research process in general. I will review this chapter for our next meeting.

Consider (and include in your blog posting for the reading):

  • How the course project you have an interest in can be approached and organized using the GIS Analysis / research process: a way of structuring your work on the project
  • How a project proposal (check schedule for due date) can be developed, including a plan and schedule for implementation, for your project. Work on this proposal will happen simultaneously with discussion of the readings and work on the software tutorial.
  • Identify and questions or issues you have, terminology, concepts, examples, etc.


W J 15: GIS & GIS Applications I: Schuurman ch 1

January 14, 2014


1. Readings

Schuurman ch. 1, “Geography Matters”

Introducing the Identities of GIS

The Success of GIS: is it now what Geography is?  Ubiquitous computing (example)

“This book is designed to inform the reader about precisely how GIS affects them as well as myriad social processes” (1)

  • a more human & social approach to technology, intellectual rather than only technological

The problem of GIS and geography: love/hate

  • GIS as one way of understanding “geography” – other approaches may be lost in the dust
  • quantitative vs. qualitative methods
  • epistemology: The branch of philosophy that studies the nature of knowledge, its presuppositions and foundations, and its extent and validity.  How we know.
  • ontology (what the world must be like in order to be known): in GIS, points, lines, areas… is that what the world is like? Or what it is like in order for us to understand it? What the world must be like to understand it with GIS?
  • Many approaches to the study of Geography (particularly in the cultural, social, human realm) are not that amenable to GIS.

The Identity of GIS: What Is It?

Delware County Ohio: DALIS Project: a tool for storing complex data; practical problem solving

  • what is where: data input, analysis, output

“PsychoGeography” maps / Mental Maps

  • a different what and where it is
  • weird stuff

Delaware Recreational Trails

  • what is most important when locating a recreational trail?
  • logic of quantitative methods for optimizing, or qualitative data used to anticipate how people will react (and why)?  Epistemological issues!
  • Delaware Trails research paper (PDF): more in a moment…

Where Does GIS Come From? Intellectual Antecedents

1930s) J.K. Wright: “The Terminology of Certain Map Symbols” (1944): point, line, area:  for map symbolization; basis of vector data

1960s) McHarg and the GIS “overlay” method: locating a road: pre-computer era

  • encode in a computer: technology and a particular way of knowing
  • what is not taken into account in this approach
  • spatial analysis: a means of extracting information (knowledge) from data
  • let a computer do what McHarg did
  • maps allow us to see raw data, or interact with data as we are analyzing it, or show the results of what we did
  • 1950s-60s: development of computational analysis and spatial analysis tools
  • wed technology to methods of knowing

What does GIS stand for?

  • definitions describe technology (systems; application): GIS(ystems) = GIS
    • hard/software for data input, analysis, output
    • “black box:” assume the methods in the software are legitimate, don’t question or think about what is going on in the box
  • definitions describing methods and process (science; theory): GIS(cience) = GISci
    • origin of the methods, critique of the methods, new methods
    • conceptual models of geographic space, sphericity of the real world vs. flat world of GIS
    • uncertainty and error, analytical methodologies, cognitive aspects.
    • also Participatory GIS, Critical Cartography & GIS: myriad of human/social issues
    • justifying and shaping an intellectual/academic role in GIS
  • myriad of issues of intellectual importance (that one may not think about at all if only approaching GIS as black box technology).
  • Understanding the World
    • quantitative vs. qualitative methods
    • epistemology: The branch of philosophy that studies the nature of knowledge, its presuppositions and foundations, and its extent and validity.  How we know.
    • ontology (what the world must be like in order to be known): in GIS, points, lines, areas… is that what the world is like? Or what it is like in order for us to understand it? What the world must be like to understand it with GIS?
    • Many approaches to the study of the world (and Geography) (particularly in the cultural, social, human realm) don’t seem amenable to study by GIS.
      • ex) certain kinds of data easier to collect and analyze and map, they seem more intuitive maybe because they are what we are used to doing.
      • ex) Historians reluctance to use GIS: Historical GIS
  • Maps (as part of GIS) complicate things even more: example) species range maps (what is a range? a species?)

Are maps propositions?

  • does geography (and its concepts/theories) drive GIS, or does GIS drive geography?  Debates in the field.

Data in, Information Out: Common Ground between GISys and GISci

GISys and GISci hard to differentiate in practice

  • ex) data classification: the categories we put things into
  • ex) house: what defines what a house is?  Is an apartment a house?  A dorm?  A condo?  A long-term residential hotel?  The kind of issue both Sys and Sci people have to deal with
  • ex) boundaries: complexity in drawing: neighborhood boundaries have to be drawn if you are using GIS, but where to draw them?  How do you define a neighborhood (which is a classification of place)
  • visualization: using intuition and knowledge to see patterns and connections:
    different epistemological approach – visual, not analytical.
  • Dr Snow example: Broad St. pump and cholera p. 15

Geography Matters

2. Your Introductions & Interesting GIS application (w/examples)

3. Next Time

  • see course schedule
  • after class: blog clean-up and questions