Lecture 9 â Cartograms, Flow maps and Dasymetric Maps Cartograms ...
Lecture 9 – Cartograms, Flow maps and Dasymetric Maps Cartograms – Maps in which the spatial proportions are distorted in accordance with a theme, typically proportion of a single variable (usually population). Analogue to proportional symbol maps Scale size of enumeration units by attribute values (purposeful distortion of space) Point/Distance cartograms Area cartograms (most common): Noncontiguous – preserves shapes Contiguous – preserves contiguity/ topology Example of Distance cartogram: Subway map, distance distorted to reflect time between stops. Area cartogram: noncontiguous vs contiguous Noncontiguous: Cartogram in which the size of each state is proportional to the total # of seniors SHAPE IS RETAINED Contiguous Cartograms: Cartogram in which contiguity (kept together) of data is prioritized, as a result shape must be greatly distorted. Flow Maps: Depict movement b/w geographic locations typically using lines of varying widths if quantitative Other visual variables used if qualitative (hue, texture, etc.) Can use standardized or unstandardized data Unstandardized fine for true point locations Interchangeably called (with some slight differences): Desire Line: straight line between origin and destination (commuting flow between traffic analysis zone) Linear Cartogram: highly generalized flow map (ex: subway route map) Types of Flow Maps: Distributive Flow Map: Displays distribution of flow between places (ex: trade flow map) Network Flow Map: Show interconnectivity between places (airline route map) Radial Flow Map: shows flow out of and into the node (traffic flow map) Continuous Flow Map: Traditional ‘wind arrow’ map Dents Principles of Flow Maps: 1. Flow lines should be highest in importance and thus in the visual hierarchy 2. Smaller flow lines should appear above larger 3. Use arrows if direction of flow is critical 4. Use contrast to differentiate land and water 5. Type of line symbol, centering, and aspect all communicate the flow pattern (Projection)
6. Keep flow line scaling and other info simple 7. Keep legends clear and unambiguous Dasymetric Maps Data Enumeration Units & Mapping Units Aggregate data vs. Individual data Census population / Public use microdata sample Traffic volume / speed Enumeration and Mapping units EU: The spatial extent in which the data were collected/recorded MU: The spatial extent that shares the same map symbol Mapping Aggregated Data In Choropleth maps – each spatial unit (polygon) is filled with uniform color or pattern and each enumeration unit of data is the same as a mapping unit In Dasymetric maps: Disaggregating aggregated information Use ancillary info (such as land use/land cover map) to enhance choropleth symbology. Mapping units different from enumeration units but still assumed to be uniform Thus, use dasymetric maps for Standardized Data (like choropleth maps). Technique: Choropleth map have administrative boundaries. Dasymetric maps have boundaries according to density 2 methods: No prior boundaries known and prior administrative boundaries known Dasymetric Population Mapping: Method of mapping population within an aggregation are using population data and land cover data Dasymetric mapping VS. Aereal Interpolation Areal interpolation is the redistribution of values from one zonation to another Is dasymetric mapping areal interpolation Benefits of Dasymetric over areal interpolation Less influenced by uncertainty and error in imagery More accuracy Customized approach More accuracy Use of Preset Data More accuracy Example of use: CSZ at high risk of tsunami, need to focus rescue efforts on areas of highest population, census data only for residents. Need to consider tourists and commuters. Look at fluctuations with time of day. Separateland into populated and unpopulated then further divide by population density—Then night time population estimated by resident population. – Then daytime,
incommuter, outcommuter totals, employees, students, no. of workers away from home/at home. Then make 3 maps: A) Day time population B) Night time population C) Highest changes in population with a tsunami inundation line. Implementing Dasymetric Mapping requires land cover data to be broken down into 4 classes. – User defined breaks, High/Low density, Nonurban inhabited, Inhabited Powerful tool for understanding population distribution Used for Disaster Planning Population modeling International aid BUT limited by Data set resolution, Computing resources
6. Keep flow line scaling and other info simple 7. Keep legends clear and unambiguous Dasymetric Maps Data Enumeration Units & Mapping Units Aggregate data vs. Individual data Census population / Public use microdata sample Traffic volume / speed Enumeration and Mapping units EU: The spatial extent in which the data were collected/recorded MU: The spatial extent that shares the same map symbol Mapping Aggregated Data In Choropleth maps – each spatial unit (polygon) is filled with uniform color or pattern and each enumeration unit of data is the same as a mapping unit In Dasymetric maps: Disaggregating aggregated information Use ancillary info (such as land use/land cover map) to enhance choropleth symbology. Mapping units different from enumeration units but still assumed to be uniform Thus, use dasymetric maps for Standardized Data (like choropleth maps). Technique: Choropleth map have administrative boundaries. Dasymetric maps have boundaries according to density 2 methods: No prior boundaries known and prior administrative boundaries known Dasymetric Population Mapping: Method of mapping population within an aggregation are using population data and land cover data Dasymetric mapping VS. Aereal Interpolation Areal interpolation is the redistribution of values from one zonation to another Is dasymetric mapping areal interpolation Benefits of Dasymetric over areal interpolation Less influenced by uncertainty and error in imagery More accuracy Customized approach More accuracy Use of Preset Data More accuracy Example of use: CSZ at high risk of tsunami, need to focus rescue efforts on areas of highest population, census data only for residents. Need to consider tourists and commuters. Look at fluctuations with time of day. Separateland into populated and unpopulated then further divide by population density—Then night time population estimated by resident population. – Then daytime,
incommuter, outcommuter totals, employees, students, no. of workers away from home/at home. Then make 3 maps: A) Day time population B) Night time population C) Highest changes in population with a tsunami inundation line. Implementing Dasymetric Mapping requires land cover data to be broken down into 4 classes. – User defined breaks, High/Low density, Nonurban inhabited, Inhabited Powerful tool for understanding population distribution Used for Disaster Planning Population modeling International aid BUT limited by Data set resolution, Computing resources
