GeoConnections geospatial return on investment case study: BCeMap (MASAS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In late 2009 GeoConnections commissioned a series of Geospatial Return on Investment Case Studies to add to the body of knowledge of case studies based on the GITA ROI methodology for financial analysis of geospatial projects. This study focuses on BCeMap, developed by Emergency Management BC (EMBC) and GeoBC to enhance the Emergency Management Information Service (EMIS) being implemented by EMBC. The BC Emergency Map Viewer (BCeMap) as part of the Multi-Agency Situational Awareness System (MASAS) is intended to enable emergency management practitioners in preparing for and mitigating the impacts of emergency incidents through timely sharing of geospatially referenced information. BceMap focuses on situational awareness data aggregation and connection to the national MASAS. BCeMap provides a single resource to aggregate relevant incident data for emergency management and public safety personnel within BC and across Canada (via the MASAS integration) rather than use of several disparate systems where overlap, higher level effects and trends could be overlooked. In addition to the inherent efficiencies of seeing multiple data types in one common view, BCeMap has the potential to improve agency collaboration and improve public safety. BCeMap was developed as a pilot project for the 2010 Olympic and Paralympic Games, using $198,000 in GeoConnections funding and matching in-kind resources. The pilot demonstrated capabilities for southwest British Columbia in the area where the games were held. EMBC is currently working to secure funding to move BCeMap to a full production mode and to extend its data capabilities to include the entire province. There is also an effort to extend awareness and training regarding the tool set throughout the stakeholder community. Areas of where greatest tangible benefits were projected for this study include: - Improved staff efficiency for routine operations - Improved efficiency for event response - Greater efficiency in protection of critical infrastructure - Greater efficiency in preservation of business continuity Areas where additional significant benefits were projected include: better retention of volunteers, improved effectiveness of volunteers, enhanced revenue from critical infrastructure owners, improved logistics regarding available transport during events, cost avoidance from redundant field data collection in areas with frequent fires, time saved maintaining critical infrastructure contact information, and time saved seeking information by district health staff. Forward-looking five-year analysis of BCeMap: Cumulative benefits are $2.4M. Net Present Value is $1.8M, with an annualized Return on Investment of 60%. Payback period is two years. This high rate of return and short payback period points to the dramatic benefits that can be realized through use of real-time data feeds, development of standards to support interoperability, and implementation of interagency data sharing for situational awareness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it