Qualitative GIS: An Open Framework Using SpatiaLite and Open Source GIS
Bibliographic record
Abstract
Abstract Qualitative GIS is a relatively new methodological approach for analyzing and visualizing qualitative data within a geographic context. Qualitative data can take many forms, including interviews, documents, photographs, and audio and video clips. Content analysis for example, is an effective qualitative method for analyzing text‐based data. We argue that basic concepts, (i.e. how to store data, data requirements, visualization techniques, and modes of analysis) within qualitative GIS have not been adequately defined, rendering difficult the replication of work performed and hindering the development of incremental knowledge in the field. Database management systems provide a means for storing, managing, and analyzing qualitative GIS data. A standardized and well‐designed open source database system provides a mechanism for qualitative GIS projects, ensuring consistency and project replication. Qualitative GIS data stored in a database allows for additional visualization options, such as geographic word clouds. To demonstrate the concepts we performed content analysis on Master Transportation Plans from Calgary and Montreal using SpatiaLite, an open source database system. We developed Structured Query Language (SQL) queries to generate and populate groups and theme tables within the SpatiaLite database. We present our database design and queries in the hopes that they will help others conducting qualitative GIS research.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".