putting manual cartographic techniques back into the digital era - a python-based algorithm for...
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Putting Manual Cartographic Techniques Back into the
Digital EraA Python-based Algorithm for Enhanced Dot
Density Thematic Mapping
Daryn HardwickSaint Cloud State UniversityDepartment of Geography & Planning
The Problem Expectations/Hypothesis Background• Python• Center of Gravity Principle• Improvements to the Dot Density Technique• Anonymity in Agricultural Data
Methods• Algorithm• Test on Agricultural Data
Results Additional Functionality Discussion/Conclusion• Critiques of the Algorithm• Future Research
Outline
Randomness in Dot Density Thematic Mapping• Use of GIS Software
Tobler’s First Law of Geography• “…near things are more related than distant
things”
Previous Solutions• Drawing Programs• Feature masks
The Problem
Expectation• The algorithm presents a solution to the problem
of randomness using the center of gravity principle
Hypothesis• The dots produced from the algorithm will be
significantly closer to actual farmland than the random dot distribution produced by GIS software
Expectations/Hypothesis
What is Python?• An object-orientated scripting language
How does it work?• Methods (procedures) performed on or between
objects1
• In GIS, objects are data with properties Why Python?• ArcGIS compatibility• Software quality, developer productivity, program
portability, support libraries, component integration, and enjoyment2
Background - Python
1: (Stefik and Bobrow 1985)2: (Lutz 2008)
What is the center of gravity principle?• “…the cartographic ideal, back when all dots were
placed manually, has been to locate the dots as close to the real distribution as possible.”1
Algorithm provides solution• Placement of known points
Background – Center of Gravity Principle
1: (Dent et al. 2009)
Earliest dot maps1
• 1852 – cholera maps by August Petermann• 1863 – Maori population in New Zealand
Percentage Dot Maps2
Limiting amount of dot overlap3
The “Fuzzy Dot Map”4
Background - Improvements
1: (MacEachren 1979)2: (Mackay 1953)3: (Kimerling 2009)4: (Alqvist 2009)
USC (Title 7, Chapter 55, § 2204g)1
• “…information obtained may not be used for any purpose other than statistical purposes for which the information is supplied.”
Algorithm does preserve anonymity
Background - Anonymity
1: (Department of Agriculture 2008)
Critical Inputs• Enumeration unit areas• Known point locations• Two Geodatabases• Areas to be masked from receiving dots (i.e.
Water)• Dot value
Additional Inputs• Clustering• Number of Buffers
Methodology - Inputs
Split of enumeration areas
Area and buffer distances are calculated
Methodology - Algorithm
Why use variable width buffers?
Methodology - Algorithm
Methodology - Algorithm
Split of enumeration areas Area and buffer distances are calculated Buffers clipped Number of output dots calculated Buffered areas merged Enumeration areas merged Areas excluded from output dots masked Creation of the output dot map
2007 Census of Agriculture• Acreage, number of farms,
market value of ag. products sold
Dots created using the algorithm and random dot placement tested
Near analysis
Significance Testing
Methodology – Data Test
Land Cover data retrieved from the 2006 National Land Cover Dataset
Results
Acreage ofFarmland
Number ofFarms
Market Value Of Agricultural Products Sold
Results - AcreageRandom Algorithm
68.33%
141.37 meters
70.14%
103.39 meters
On Farmland
Distance toFarmland
One dot represents12,800 acres
Results – Number of FarmsRandom Algorithm
63.01%
189.01 meters
65.31%
125.82 meters
On Farmland
Distance toFarmland
One dot represents40 farms
Results – Market ValueRandom Algorithm
72.51%
66.38 meters
76.39%
38.82 meters
On Farmland
Distance toFarmland
One dot represents$5,000,000
Results - Significance
T-score P-value Significant?
Acreage: -1.93 0.0269 Yes
Number of farms: -2.72 0.0033 Yes
Market value: -3.74 0.0001 Yes
Operation within ArcGIS
Custom Toolbox
Two Parts• Step 1 – Script• Step 2 - Model
Additional Functionality
Clustering Options
Algorithm run with a low clustering effect Algorithm run with a high clustering effect
Buffer Options
Algorithm run with a two buffers Algorithm run with a four buffers
Critiques• Time• Placement of Known Points
Future Research• Improve the issue of time• Another way to solve this problem?
Critiques/Future Research
This algorithm• Increases the accuracy of placed dots• Adheres to the center of gravity principle• Removes some randomness without sacrificing
anonymity of underlying data
• Can be used in ArcGIS• Options to further customize the output
Conclusion
Ahlqvist O., (2009) “Visualization of Vague Category Counts – Introducing the Fuzzy Dot Density Map”. In: International Cartographic Conference 2009 Proceedings, International Cartographic Association.
Dent B., Torguson J., and Hodler T., (2009) “The Dot Density Map”. Cartography: Thematic Map Design, 6e: 119-130.
Department of Agriculture, (2008) “Authority of Secretary of Agriculture to conduct census of agriculture”. U.S. Code Title 7, Chapter 55, § 2204g.
Golledge R., (2002) “The Nature of Geographic Knowledge”. Annals of the Association of American Geographers, 92(1): 1-14.
Hey A., (2012) “Automated Dot Mapping: How to Dot the Dot Map”. Cartography and Geographic Information Science, 39(1): 17-29.
Hoonaard W., (2003) “Is Anonymity an Artifact in Ethnographic Research?”. Journal of Academic Ethics, 1: 141-151.
Kimerling A., (2009) “Dotting the Dot Map, Revisited”. Cartography and Geographic Information Science, 36(2): 165-182.
Lutz M., (2008). “A Python Q&A Session”. Learning Python, 3e: 3-20. MacEachren A., (1979) “The Evolution of Thematic Cartography / A Research Methodology and
Historical Review”. The Canadian Geographer, 16(1): 17-33. Mackay J., (1953) “Percentage Dot Maps”. Economic Geography, 29(3): 263-266. Stefik M., and Bobrow D., (1985). “Object-Orientated Programming: Themes and Variations”. AI
Magazine, 6(4): 40-62. Tobler W., (1970) “A computer movie simulating urban growth in the Detroit region”. Economic
Geography, 46(2): 234-340.
References
