Rachel Bowes

Chapter 5: Finding What’s Inside

Monitor activity occurring within an area, summarize and compare info for several areas

Three ways of finding out what’s inside:

• Drawing areas and features
• Selecting features inside the area
• Overlaying the area and features

Chapter 6: Finding What’s Nearby

Features within set distance; monitor events in area, find area served by a feature

Three ways of finding out what’s nearby:

• Straight-line distance
• Distance or cost over a network
• Cost over a surface

Chapter 7: Mapping Change

Mapping movement and changing conditions for predictive modeling or evaluating impacts; gain insight into how things behave

Three ways of mapping change:

• Creating a time series
• Creating a tracking map
• Measuring and mapping change

Chapter 2: Nick Striler

Chapter 3: Rachel Bowes and Michael Davidson

Chapter 4: Veronica Malencia

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Chapter 3: Mapping the Most and Least

Why map the most and least?

Why?

• To find places that meet particular criteria
• To see relationships between places

Map most and least: map features based on a quantity associated with each

What do you need to map?

Mappable quantities: Keep purpose of map and audience in mind!

• discrete features (individual locations, linear features, or areas)
• continuous phenomena (defined area or surface of continuous values)
• data summarized by area

Understanding quantities

Quantities:

• counts or amounts: shows total numbers
  • count: actual number of features
  • amount: total value associated with each feature

• ratios: relationship between two quantities
  • divide one quantity by another for each feature
  • useful when summarizing by area: evens out differences between different sized areas
  • most common ratios:
    • averages (comparing places with differing amounts of features)
    • proportions (what part of whole each quantity represents)
    • densities (where features are concentrated)

• ranks: put features in order, from high to low (relative values)
  • often used in combination with other attributes or features

Creating classes

Displaying quantities: mapping quantities involves a trade-off between presenting the data values accurately, and generalizing the values to see patterns on the map

Mapping individual values

• assign individual value own symbol
• more accurate, difficult to distinguish/confusing

Using classes

• Creating classes manually
  • features that meet specific criteria, or comparing features to a specific, meaningful value
  • explicitly state what each class represents (specify upper and lower limits for each class and assign symbols)
• Using standard classification schemes
  • group similar values to look for patterns in the data: based on distribution of data values (by plotting data values on a chart)
  • 4 most common schemes: all have advantages and disadvantages
    • natural breaks (natural groupings of data values, inherent in data)
    • quantile (equal number of features in each class)
    • equal interval (difference between high and low values is the same for each class)
    • standard deviation (classes based on how much values vary from the mean)

  

  • Choosing a classification scheme: need to know how data is distributed and what your purpose is
    • uneven distribution: natural breaks
    • even distribution with emphasis on the difference between features: equal interval or standard deviation
    • even distribution with emphasis on relative difference between features: quantile
  • Dealing with outliers: outlier can be own class if spread out, if clustered, can be put into one class
  • Deciding how many classes: 3-7 is good range
  • Making that classes easier to read: want to interpret/understand quickly

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

Geography: physical nature of earth’s surface, role of geography in constructing politics and shaping behavior     vs.    GIS: cartography

GIS and human geography: separate spheres of geography until late 1980′s, tension, debates, critiques of GIS

Difficulties integrating different scientific classification systems

Epistemology:

• “the methods we use to study the world and the lenses that they entail”
• depends on perspective of the researcher

Ontology:

• “what something really is”
• interpreted through epistemology

Representation:

• “the ways that phenomena are depicted after having been interpreted”

Positivism:

• theory based on repeatable observations
• evidence subject to perception

Realism:

• “abstractions that identify and explain causal structures for phenomena under very specific circumstances
• presumes that there are facts that are independent of the mind

Pragmatism

• antifoundationalist (knowledge builders as participants)
• knowledge is instrumental tool for organizing the world
• truth not absolute

Ontology:

• formally defined set of objects in which all the potential relationships between objects are well defined
• data models
  • Raster data models
  • Vector data models
  • Object data models
  • Aristotelian logic: 3 laws
    • Law of identity: everything is what it is
    • Law of noncontradiction: something and its inverse cannot both be true
    • Principle of the excluded middle: every statement is either true or false

GIS research: extending ontological integrity of models, creating systems of classification with greater flexibility; technology influenced by social processes; science and culture inseparable

Technology developed for social purposes

Five levels of ontologies:

• human-independent reality,
• observations of the physical world that are obtained using measurement systems
• objects with properties that are discerned through experience and cognition
• social reality based on conventional names for things
• subjective knowledge

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

Data subject to social influence as well as parameters of hardware and software

Data Collection

• Statistical generalization verses direct questioning
• Primary data: captured using direct measurements specifically for use in a researcher’s project
• Secondary data: collected for another purpose

Data organization

• Tables
• Location
• Attributed data
• Consistency
• Scale

Metadata

• Data about data
• origins, quality, and applicability of data

Sharing Data

• interoperability
• Semantic heterogeneity
• Standardization/homogenization

Data: compiled with a particular purpose in mind, reflect the assumptions and preconceptions of data collectors and data users

Chapter 4: Bringing it all Together: Using GIS to Analyze and Model Spatial Phenomena

Data, models, and more sophisticated analyses of spatial data (“query power”)

GIS can visualize cadastral data (definitions, attributes of things), and answer “queries” about data

“Point in polygon” algorithm: whether a spatial entity is contained within an area

“Edginess index”: measurement techniques can contribute to spatial understanding and decision making

GIS: able to query spatial data, analyze spatial relationsjips, characterize regions, model spatial change over time and shape

Visualization: process in which the eye detects patterns in data

Overlay: reveals patterns common to two or more attributes

• Map algebra: allows raster attribute values to be transformed
• Reclassification: allows generation of new values for spatial areas without changing the definition of spatial units

Models: rely on a morphism or mapping between the entity and the representation; “all models are wrong, but some are useful”

EDA process:

1. Exploratory visualization using GIS
2. Statistical and numerical analysis of spatial patterns and trends
3. Visualization and communication of results using GIS
4. Repetition of stage 2 if needed with different statistical and/or numerical methods
5. Final results as tables and graphs

Chapter 5: Where Do I Go From Here? GIS Training and Research

Relationship between GISystems and GIScience: Not the same

GISystems = software, hardware

• Implementing
• users must be trained

GIScience = theory, underlying assumptions

• Intellectual premises
• researchers ensure reliability of results, generate solutions to limitation

Two influential areas of GIScience research:

1. Ontology research
2. Feminism and GIS

Ontology research: Ontologies and epistemologies identify “the object of inquiry”

Interpretations of “ontology”

1. A proxy for data models and debates about the limits of their power to present spatial phenomena
2. Description of cognitive and perceptual impressions of space and spatial entities
3. As a surrogate for classification systems and taxonomies
4. Formal computational ontologies: based on a fixed delineation of entities within a knowledge domain, where the definition of each entity and its relationship to every other entity is proscribed

Feminist analysis: Illustrates how knowledge production is always partial

What is GIS analysis?

• It is the process of looking at geographic patterns in your data and at relationships between features

Process for performing an analysis

1. Frame a question
2. Understand your data
3. Choose a method
4. Process the data
5. Look at the results

3 Types of geographic features

1. Discrete features: locations
2. Continuous features: boundaries and area in between
3. Features summarized by area: census

2 Ways to represent geographic features

1. Vector: coordinates
2. Raster: layers

Map projections, Coordinate systems:

• Map projection: translates locations from sphere to flat surface
• Coordinate system: specifies units used to locate features in 2D-space

Geographic Attributes: identifying/describing geographic features

Continuous, non-continuous values:

• non-continuous: categories, ranks
• continuous: counts, amounts, ratios