associations
- combining data on the map can be used to look at association between dataset and location
- exposure and disease: combined polygon data and points with residential locations
- crop yields and soil type: polygon with polygon data combination
overlay analysis
- data is often combined using overlay analysis
- stacking spatial data on top of each other and processing a combination of both spatial features and attributes to create a new layer
- overlay multiple data layers
- modify geometry
- create new information
vector overlay
- estimates the geometric intersection of multiple datasets of the same or different geometries and combines them to create a new output dataset
boolean logical operators
- overlay analysis operations are analogous to boolean logical operators
- AND: intersection overlay
- OR: union overlay
- NOT: area exclusive to only one type
analysis framework
- data from different sources are collected and laid out together in the same framework using spatial analysis
result variability
- take care of the changes that may occur when combining data
- changes maybe made to the data during analysis
- boundaries of the analysis area determine the aggregated values
- the analysis for the same location may yield different results
- if different area boundaries are used
- same issue applies to scale of analysis
- results are function of area used to analyze the data
- take care when visualizing a map with area data
- this variability is addressed by normalization
- totals should be normalized when being mapped by area
- this converts counts to intensity
normalization: done to compare data from different scales and different boundaries
- ratios such as means, medians, percentages and rates are already intensities (normalized)
choropleth map
- a thematic map in which areas are distinctly colored or shaded to represent classed values of a particular phenomenon
- always should show normalized data, not counts
- since they are mapped over unequal areas or populations
- this is very commonly overlooked and is a common error
cartography
- maps are essential to communicate result obtained from spatial analysis
- technology makes it easy to make great maps
- at the same time, it makes it easy to make really poor ones to
- the poor ones are usually the ones that overkill technology use
- that distort info to be conveyed by the map
- without understand how the distortion affects perception
- of the information they are trying to convey
- tools in the GIS software exist to make good and well-meaning maps
- go beyond the tool defaults
- the defaults rarely are exactly what you need to convey the true meaning
carto-fails
avoid rainbow colors
- use monochrome - light to dark
- to convey more or less
avoid choropleth map totals
- using count values for unequal areas and unequal populations
- to show the counts
- this is like comparing apples to oranges to draw conclusions
- normalize with area to show rate/density/intensity in choropleths
- count vs per capita
use symbol maps with size of symbol indicating count
- to show counts
- instead of choropleth maps
projection selection is important
- understand limits of each type of projection
- state the projection used as a disclaimer if you are unsure how the visual data is distorted by the projection used
3D maps fails
- do not use projection view for 3D extrusion visuals
- projection views look ‘cool’ but have not information conveying function
- unless your intent is to create a fancy web page banner
- use isometric views to convey real meaning
- don’t let fancy get in the way of conveying the core meaning
- this is cool for the person who benefits from the info provided
- make interactive 3D maps and add rotation to ovoid occlusion
- helps retain a coolness factor while keeping the info to be conveyed intact
transparency overkill
- mashing up thematic schemes on a basemap is common
- thematic coloring of a basemap is introduces a lot of visual clutter
- makes the information more cluttered than necessary
- usually done when people are trying to show off being able to use the GIS tool over using the tool to convey real meaning
- defeats the purpose and looks tacky and unprofessional to the trained
- avoid basemaps altogether, especially if you are using cholopleths
- just the cholopleth is 6 colors
- but the basemap with the choropleth is 44000 colors
- it is practically useless to gather any useful data
- (you can claim time spent on the project though, so some people will do it anyway, it looks like a lot of work was done)
- if you have to choose a basemap, pick something that is plaid and dull
- so it is un-obtrusive with the choropleth color scheme
death by push pin
- you can kill the information that is to be conveyed by adding a thousand pushpins on the entire map
- couple it with a rainbow colored cholopleth and multi sized pushpins, and you might as well have your customer do coke to take them on a trip
- to avoid this mess, show restraint on the use of pushpins
- simple map: easier to quickly digest information
- convey the meaning with out destroying the meaning with the tools used to convey the meaning
- gridded choropleth in place of randomly scattered symbol map
- the data is there, but only for the sake of just being there
- nothing useful is conveyed
- analysis paired with sensible cartography supports sensible information interpretation
overstating facts
- the map is only as good as the data and the analysis put into it
- be careful of evaluating the value of information conveyed by the map
- it is easy to be carried away and over-state the info
- if you underestimate the bias, uncertainty and error of the data source, and make a map and sell it because it looks ‘cool’, it doesn’t aid any meaningful interpretation of what is in reality
- even simple layer ordering can change the entire meaning conveyed by the map
- even though all the data ‘is still there’
- maps made to look cool lie and maybe considered data art, but it is not analysis or cartography
- maps lie
- all maps can lie
- the goal of analysis and cartography is to extract the truth and present it to the viewer
- data art is not analysis or cartography
evaluating maps
- it is great to be inspired by maps made by others
- but if you don’t know what you are looking for, it is hard to decide what is good or bad
resources to make better maps