McFarland Week 3

Chapter 4 (Mapping Density):

Density maps give a clearer distribution array than simply mapping features.

Two types of density mapping: based on features summarized by defined area or by creating a density surface

Defined area: Ex( Using a dot map to represent density of individual locations)

  • Use if data already summarized by area or in lines/points easily summarized by area
  • Easy, but doesn’t pinpoint exact densities

Density Surface: Ex( Raster layer with each cell being assigned a density value such as per square mile)

  • Use if given individual locations, points, or lines
  • more precise view of density, but is more difficult

pop_density = total_pop / (area/27878400)

27878400 square feet in a square mile

Dot density map seems to be a combination of defined area and density surface.

It is possible to map defined areas using individual features, but you have to make sure it meets your criteria.

When GIS runs the program to create density surface it creates a neighborhood or area around each cell that creates a smooth transition from cell to cell.

How to find the right cell size:

  1. Convert density units to cell units
  2. Divide by the number of cells
  3. Take the square root to get the cell size (one side)

Finding the right cell size is just finding the sweet spot between not using too much processing power while still showing the detail of patterns.

It is possible to map density surface with data summarized by defined area. You can use census tract centroids for each cell to create a smoothed surface.

It is possible to use the four different classification schemes to achieve different outcomes.

Often higher densities are shown using darker colors, but using lighter colors could draw the reader’s eyes to the area more effectively.

Chapter 5 (Finding what’s inside):

In order to map what’s inside you need to define your area of study and combine that with features to create summary data.

Single area:

Analyzing activity or summary information in that area

  • A service area around a central facility
  • A buffer that defines a distance around some feature
  • An administrative or natural boundary (parcel of land or watershed)
  • Manually drawn area (proposed sales territory)
  • Results of a previous model (floodplain modeled in GIS)
  • Combination of several areas treating them as one

Multiple Areas:

  • Contiguous (such as zip codes or water sheds)
  • Disjunct (state parks)

Discrete features are unique, identifiable features. Continuous features represent seamless geographic phenomena.

When using a list or count of features you should include those features that are partially within the boundaries of the mapped area.

Three ways of finding what’s inside:

Drawing areas and features:

  • Create a map showing the boundaries of areas and the features to see if features are inside the areas. All you need is a dataset containing the boundary of the area/s and a dataset containing the feature/s.

Selecting features inside the area:

  • Specify the area and the layer containing the features, then GIS selects a subset of the features inside the area. Good for getting a list or summary of features inside an area. Need a dataset containing the areas and one with features.

Overlaying the areas and features:

  • GIS combines the area and the features to create a layer with both attributes to compare them. Good for calculating summary statistics and finding which features are in each of several areas, or finding out how much of something is in one or more areas.
  • Need data with areas and data with features (including attributes you want summarized)

Shade outer area to emphasize features and fill outer area with translucent color to emphasize outer area when mapping discrete areas.

If a feature falls within two or more areas, the GIS splits the feature where it crosses the area boundary. Most any types of maps can be overlayed for comparison.

Chapter6 (Finding what’s nearby):

Mapping what’s nearby can be used to find out what’s happening within a set distance of a feature.

Distance can be measured in distance or travel cost.

Three methods:

  • Straight-line Distance
    good for creating a boundary or selecting features a set distance away from a feature. Layer containing main feature and surrounding features.
  • Distance or cost over a network
    Good for finding what’s within a certain travel distance/ travel price over a fixed network. Need locations of source features, a network layer, and a layer containing surrounding feature (usually)
  • Cost over a surface
    Good for calculating overland travel cost. Need layer containing source features and a raster layer with the cost surface.

Choosing a method:

  • straight-line when defining area or want a quick estimate of travel range
  • cost or distance over network when measuring travel over a fixed infrastructure network
  • cost over a surface when measuring overland travel

When analyzing features within an area color-coding can be used to draw attention to different categories of features.

When creating a distance surface you can set a maximum distance for which GIS will only calculate to that point.

Cost in a cost over surface map can be time, money (such as cost to develop an area), or effort expended. For example a deer might expend less energy moving through open forest than through thick brush.

Is an elevation/ topography map a version o a cost over surface map?

A lot can be done with a cost over surface map. No maximum can be set, or a maximum can be set, or the area outside a certain limit can be selected.

When using more than six or seven ranges, you can use two or three hues to help distinguish the ranges.

1 thought on “McFarland Week 3”

Leave a Reply