Geospatial Analytics: Insights from Ayanthi Gunawardana on Mapping with R
In a recent presentation, Ayanthi Gunawardana discussed the intricacies of creating precise and aesthetically-pleasing maps using R. This focused exploration of geospatial data offered valuable insights that go beyond common knowledge in the data science community. For the full presentation, you can watch the video here.
Understanding Geospatial Data and Mapping
Gunawardana draws attention to various geospatial data types—predominantly vector and raster. Vector data encapsulates geographic information related to distinct points, lines, and polygons. Each shape (point, line, or polygon) is stored in separate files, which is crucial for maintaining data integrity and accuracy. Meanwhile, raster data comprises grid-based representations, with each pixel signifying a specific value—typically used for elevation or land cover analyses.
Analyzing the interplay between spatial and non-spatial attributes becomes imperative. Spatial attributes include latitude and longitude, while non-spatial attributes encompass additional information like population statistics. Misunderstanding this distinction can lead to flawed conclusions about data patterns.
Projection Systems
A pointed observation is the distortion inherent in map projections. A geographical coordinate system uses degrees to describe locations based on a sphere (the Earth). When mapping, we flatten this globe, creating a projected coordinate system. The distortion manifests differently based on the chosen projection method. Gunawardana highlights three common types:
- Conical Projection: Useful for mid-latitude regions, but distorts areas as one gets further from the standard parallels.
- Cylindrical Projection: Employed in world maps, distorting areas far from the equator.
- Planar Projection: Ideal for localized regions, offering a more accurate representation of small areas.
Best Practices in Geospatial Visualization
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Know Your Audience: Tailor the information according to their familiarity with the subject, avoiding over-reliance on jargon—especially geographic specifics like “five boroughs” in NYC.
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Normalize Data: Given varying geographic boundaries, failure to normalize can misrepresent data concentration. For instance, mapping absolute numbers without accounting for population distribution can mislead a viewer about density patterns.
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Minimize Over-labeling: A common pitfall is excessive labeling, which complicates the visual narrative. Gunawardana exemplifies this by showing maps cluttered with street names, which detracts from comprehension.
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Choose Colors Wisely: Consider color-blind accessibility and organizational color standards. Using industry-standard color codes for land use (like yellow for residential) is critical for intuitive understanding.
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Document Changes: Manipulating geospatial data in R provides a flexible environment, but meticulous documentation throughout the analysis process is a must to ensure reproducibility and integrity.
Misleading Maps and Visualization Failures
Gunawardana critiques prevalent cartographic inaccuracies. For instance, misleading election maps often utilize area rather than population density, painting an inaccurate picture of electoral support. A map that simply correlates ZIP code boundaries can obscure real geographic patterns, as these boundaries may represent arbitrary mail routes rather than meaningful geographic divisions.
R Packages for Geospatial Mapping
Several R packages enhance geospatial analysis. Key players include:
- sf (Simple Features): Essential for manipulating spatial data and performing spatial joins.
- ggplot2: A robust package for creating static maps.
- leaflet: Preferred for interactive and dynamic visualizations.
Data Presentation
Gunawardana concludes with examples of maps that yield divergent interpretations of the same dataset, emphasizing that visualization techniques fundamentally impact data interpretation. Two visualizations—one utilizing a gradient and the other categorizing data into quintiles—result in different perceptions of restaurant trends in the East Village.
Resources for Further Learning
For those eager to explore geospatial analytics and R, Gunawardana recommends several resources, notably:
- “How to Lie with Maps”: An engaging introduction for data scientists venturing into geospatial.
- GeoComputation in R: Available online for free, covering key methods in geospatial data manipulation.
- New York City Open Data Portal: A comprehensive repository for geospatial datasets.
Ayanthi Gunawardana’s talk emphasizes not just technical proficiency in R but also the critical thinking required to visualize data accurately. With careful adherence to best practices, data scientists can leverage R’s capabilities to unveil insights that matter.