Feature Specifications

19F

The main features are the same as detailed in the 19W Feature Specs.

Front-End: Download Button

The download button in the left hand corner of the map on the "Predictive Model" page allows users to download the currently displayed map as a PDF (see image on next page for an example map PDF). When the button is clicked, the current state and year selected in the dropdowns above the map will be displayed in the downloaded map and used to name the file. Currently, the only data that can be downloaded is the probability of any spots occurring in the selected states. This is reflected in the downloaded PDF title.

19W

For more information on feature specs, please click here.

18F

Main Features are split into the front and back-ends.

Front-End

Completed:

• Built front-end UI for visualizing historical data and running the predictive model
• Embedded Mapbox maps to view data geographically/spatially
• Added plots from Chart.js to visualize data numerically
• Developed upload data page (password protected for admin users)

Ongoing:

Historical Data Page

• The year selection on the historical data page can be converted into a slider for easier use
• The map could be improved to show more full-fledged data. Currently, it shows average spots over the selected period. There may be more informative metrics and map-based visualizations.
• The summary statistics could be more customized to the scope of the filters. The best nation-wide summary statistics may be different from local summary statistics.
• Include past predictions on historical data chart to compare with actual spots
• Use county and forest map shapes instead of dots

Predictive Model Page

• Refine the micro-interactions
• Hovering over regions on the map can trigger outlining or bolding of that region
• Improve fidelity and consistency of county/national forest shapes on the map

New Pages

• Live data-watching page: Some users expressed interest in being able to watch the progress of data collection
• CSV upload page: Just like the UploadSurvey123Data page, we can have another hidden password-protected page which could upload CSV directly into the mongodb database, without needing to run a command line script
• Predictive model management page: Another hidden password-protected page which can change the predictive model. Update the R script being run. Change the inputs and outputs of the R script that is expected by the application

Back-End

Completed:

• Create database to store trapping/spot data
• Upload historical data to database
• Develop pipeline to fetch new data from Survey123 and add to database
• Develop pipeline to fetch all data in database and send to front-end
• Create method to run R-script (predictive model) and feed to front-end

Ongoing:

• Data validation - error check new data from Survey123 and predict/locate outliers
• Survey123 Ingestion Process - the Survey123 ingestion process could be more lenient in terms of updating data. It would be a complex but achievable undertaking to build a system which could efficiently check whether values need to be update and update them. Achieving would enable a host of neat features such as automatic pulling of data.