i read the chapters 🙂
CHAP 4
- Map density shows where the highest concentration of features is
- Useful for looking over patterns as opposed to individual features
- Density maps allow for one to look at features with a higher concentration than others
- These kinds of maps are highly useful when varied by size
- You can differentiate between defined areas or just density size
- You will need to include conversion factors in calculating density if map units is different than density units
- If defining density by a specific region, you run the risk that density might change in a given space and not remain uniform throughout
- Dot maps are density maps which use dots to represent density, 1 dot per a certain amount of the count.
- GIS can be used to summarize feature values within polygons
- Cell size: defines how course the patterns are, needs to be goldilocked to balance pattern definition with storage saving
- Search Radius: affects generalization of patterns
- There are 2 methods to generate density maps; the “simple” method and the “weighted” method.
- GIS lets you specify areal units, which are the unit the map measures itself in (i think?)
- Centroids: density surface map feature which allows you to define an area
- Contour lines and colors both often used for map density
- Density maps show us how values vary across regions of a map as well as distribution of samples
- Sometimes density map data can be inaccurate; if you are studying how many employees there are, the suburbs would be empty because there are no businesses in them
CHAP 5:
- Mapping an area helps us monitor what occurs within it
- Data must be analyzed based on whether it is a singular discrete area or multiple areas/continuous areas
- Data for analysis can be arranged as either a list, count, or summary
- There are three ways of figuring out whats inside
- method 1: drawing area and features. Requires a dataset with a boundary of an area, good to see whats inside of it
- method 2: selection of the features inside the area. needs a dataset with a boundary like above, but also all of the attributes you want to summarize. its good for getting a list of summaries from within 1 group
- method 3: overlaying areas and features. this method requires the same things as the above method, good for finding which features are in each of several areas
- Method 1 is only visual, method 2 only works for one area, and method 3 requires the most processing
- maps can be made by drawing features in different or same symbols
- Discrete areas can be made by either shading the area in on top of other boundaries or by making the boundary of the area thicker than surrounding ones or by doing both
- You can select different parcels within your area for summarization
- Count: a summary which shows the total number of features within an area
- Frequency: a summary which shows the number of features with a given attribute or value inside an area. This is displayed most commonly as a table, but can be turned into a pi chart
- The most common numerical summaries are:
– SUM: all of the features numerical values added together
– Average, AKA mean: the sum of the numerical values divided by the amount of features
– Median: The absolute middle value of the numerical values
– standard deviation: showcases how much the values stray from each other. - You can overlay features on top of each other
CHAP 6:
- Mapping nearby areas helps to identify the area, and may be useful for study- like a study on travel distances or trying to plan a walkable city or any other sort of example
- “nearby” is based on a distance set by you, either whatever is in the source feature’s area or within a certain amount of travel from the area
- nearby can also be measured by “cost”, such as how long it takes someone to get through heavy traffic
- analysis differs depending on if you’re accounting for the curvature of the earth
- You can specify a single range or several ranges
- “Inclusive Rings” are used to see how total amounts increase as distance increases
- “Distance Bands” are used to compare distance to other characteristics
- There are three ways of mapping what is nearby:
- Method 1: Straight line distance. Specifies a source feature and the distance and GIS finds the area or surrounding features within it, presumably with just a straight line. Requires a layer containing source feature and surrounding features.
- Method 2: Distance and/or cost over a network: Specifies source locations and a distance or travel cost between them using linear features. You can use the featured segments to find surrounding features.
- Method 3: Cost over a surface. You specify the location of source features and travel cost, allowing GIS to make a new layer showing travel cost from each feature. Requires a layer containing source features and a raster showing cost surface.
- You can create a buffer by specifying source location and buffer distance- this creates a line around the feature(s). You can even have the GIS sense when these overlap and create a single buffer area out of all of them.
- With the buffer you can specify only the feature points within the buffer, allowing for analysis of data within
- You can create seperate buffers per range and overlap them with inclusive rings, or have GIS make multiple distance bands
- You can also use selection to specify points within a range, which works similarly to buffer selection methods
- You can have GIS ID the actual distance between two locations
- You can specify maximum distance in which locations can be included
- GIS can also identify nearby networks, such as streets
- It can also also ID across a geographical surface such as streams or mountains