Chapter 1:
Concepts & Definitions
- GIS Analysis: looking at spatial data to identify patterns and relationships
- Geographic Features: Discrete feature = exact (roads); Continuous phenomena = measurable everywhere (temp); Summarized by area = counts or an aggregation (population per country)
- Data Models: Vector = points, lines, x,y coords in tables; Raster = grid/cells, each has a value (continuous data)
- Map projections & coord systems: projection = going from curved surface to flat map; coord system = defines measurement units and origin for locations
- Geographic Attributes = descriptive info tied to features; Categories = groups features (crime type); Ranks = order features by value; Counts = number of features; Amounts = measurable quantity; Ratios = relationships between quantities
- Continuous and Noncontinuous values: noncontinuous = fixed set values; continuous = any value in a range
- For Data tables: Select by using queries to filter data; use =,<,>; calculating by adding new fields or computing values; summarizing by getting totals, averages, and frequencies
Notes
- GIS can be used for data exploration and is not just cartography
- Framing the right questions is highly important, along with the analysis
- Data tables seem to be the backbone of GIS analysis
- How specific you need to be depends on what data you are trying to collect
- Reading this text, while illuminating, doesn’t fully give me an idea of how to map, sadly
- Chapter lays the foundations: features, attributes, models, projections
- Need good questions, data, and choices for a good GIS map
- Fundamentals of GIS have remained the same despite technology advancing rapidly
- Knowing your audience is important, casual versus scientific versus legal contexts
- Two similar maps can answer completely different questions depending on the data used
- GIS can be used for infrastructure planning
Questions
- What does GIS actually look like?
- How do you factor error into your data on GIS?
- How many layers can you add to a map?
- How friendly are the tools to a newcomer?
Chapter 2:
Concepts & Definitions
- Category values = feature that has a code that identifies its type, like whether a crime is a homicide or theft
- General code for attributes is the major type and detailed code is the sub type
- Single type map = all features use same symbol (very basic)
- Grouping categories = multiple categories grouped together to make patterns easier to view; instead of 1. Heavy industrial, 2. Light industrial, 3. Medium industrial, group to just Industrial
Notes
- Maps used to see where or what an individual feature is
- Patterns help to better understand an area while mapping
- Locations and features can allow you to see patterns
- For geographic patterns in data, mapping features in a layer using different kinds of symbols is ideal
- If an audience is unfamiliar with an area/data shown on map, use information that will provide reference locations, like roads or lakes
- GIS reads location information or latitude and longitude values and assigns geographic coordinates
- Many categories are hierarchical, state highways into how heavy traffic is on them
- GIS can use coordinate pairs to define the location of an address (4 points of a square)
- GIS can be used to map a subset of the data; all crimes into just selecting only jaywalking, which can reveal patterns
- Mapping subsets most common for individual locations
- Map showing only subsets of features could be incomplete
- Can change the color and symbols/characteristics of categories
- Features might belong to more than one category
- If patterns complex or features close together, creating a separate map for each category can make patterns easier to view
- If showing several categories on one map, display no more than seven categories
- When smaller areas mapped, individual features easier to see so using not enough categories can leave information out
- The way categories grouped or changed influence the perception of information
- Can group categories by using a general code to ‘combine’ them or by using two tables with the detailed codes corresponding to a general code
- Text labels can help identify categories
- Landmarks always helpful for people
- Zooming in and out can reveal patterns, like clusters
- Patterns may be the result of a multitude of factors, so statistics to measure the relationship between these features is important
Questions
- Can you use any shape or symbol for categories?
- How hard is it to specify a location using points?
Chapter 3:
Concepts & Definitions
- Continuous phenomena = defined areas or a surface of continuous values
- Data summarized = amount of category in each area
- Counts = actual number of features on the map
- Amount = total value associated with each feature
- Ratios = relationship between two quantities; averages, proportions (%), densities
- Densities = where features concentrated; ex: population of a city / land area (Sq Mi), people per square mile
- Ranks = putting features in order from highest to lowest
- Classification schemes = grouping similar values to look for patterns in data; may want map to focus more on highest income households or focus more on the number of classes; four common schemes = natural breaks, quantile, equal interval, and standard deviation
- Z-factor =Â a value that increases variation in the surface for 3D
Notes
- Mapping features based on quantities can add additional levels of information beyond just a location, like amount of customers at a shop instead of shops with customers
- Make sure to keep the purpose of your map and audience in mind; exploring data versus showing a map
- Knowing the type of quantities being mapped is the best way to showcase the data
- Counts and amounts can skew patterns if areas vary in size, using ratios or percentages can be more accurate to represent features
- Proportions great to show what part of a whole you want a quantity to represent
- Ratio = 1/10 versus percent = 1/10 * 100
- Can create ratios by adding an extra field in the layer’s data table
- ArcGIS lets you create them by setting up the calculation
- Ranks useful for direct measurement; may rank suitability for growing crops; 1-10
- Block groups can show off data values using shades
- Mapping individual values may give an accurate showcase of the data but is more time consuming, so ranks may be better for your sanity
- Each classification scheme has pro’s and con’s, just depending on what you want the map to showcase, creating a bar chart can help
- If outliers, using natural breaks can help isolate them
- If trying to use shades to showcase different percent’s, use up to seven colors on a map
- Page 93 of chapter 3 good resource for what map you wanna make
- Can create pie charts on graduate symbols