An Analysis of Interactive Solar Energy Web Maps for Urban Energy Sustainability
Bibliographic record
Abstract
Maps and geographic information systems (GIS) have become vital tools for decision-making, communication, and outreach in the domain of urban energy sustainability. One emerging example involves interactive online maps that allow users to assess rooftop solar energy potential on a building of interest. These maps are interesting in two ways: they are new forms of technology in and of themselves, and they have only become relevant with the changes in renewable energy technologies that allow individuals to participate in this new economy of energy production. The purpose of this study is to describe and analyze the cartographic representation and functionality of urban-scale solar energy maps in the United States. Using competitive analysis, we assess twelve interactive online maps to understand their: (1) design, (2) usage of visual variables and interaction operators, and (3) content, purpose, and goals. Across these three types of assessment, we find both a wide variety as well as some consistent themes. Our results also show that some maps followed cartographic conventions (Brewer 2016; Slocum et al. 2009) while others did not. Through our analysis we develop a set of best practices that can be used to improve the effectiveness and widen the functionality of online solar energy maps. In particular, we make recommendations on how to develop future online, interactive renewable energy maps in a way that keeps the end user in mind while communicating relevant information to a broader range of stakeholders involved in urban energy sustainability (homeowners, utility operators, city officials, and urban planners).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".