David’s Portfolio of Spatial Informatics, GIS & RE Financial Modeling (Archived)

Note: This is an archived page of David’s Academic/ Early Professional portfolio, which is no longer in use. Our tech firm has little to do with Spatial Analytics, or RE Investments for that matter

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Sample Corporate Projects - Part I
Spatial Informatics & GIS Modeling - Part I
RE Financial Modeling
Spatial Informatics & GIS Modeling - Part II

Not to be confused with David’s Portfolio of Conceptual Art & Design

Selected Corporate Projects & Exhibits

Southlake-Ontario Health Cancer Screening Test of Change (to be deployed provincially), GWR Auto-Prediction Results Published via Interactive App, Region: SCOHT Area (pipeline programmed in Python), 2022-2023

Selected from a number of comparable test-of-change and AI projects across the province and hailed as “The Gold Standard” by the Ontario Government, the state-of-the-art spatial prediction models and associated automated components were designed and programmed by David, the sole expert who represented Southlake & Ontario Health (SCOHT) at multiple interagency committees and working groups. Among the participants were ESRI and Carleton University Department of Health Informatics. The models enabled the automatic GWR modeling of any designated urban region, which resulted in local predictor significance (adaptively classified by the proxy statistic β coefficient/SE). Navigate the “Scrollable Instructions”” for more info on the project and how the proxy statistic should be interpreted.

GWR Auto-Prediction Results Published in Interactive Web App - Region: East Toronto Unincorporated Area, 2022-2023

The models were tested, independently evaluated, and successfully replicated in another urban region, which produced adaptive results and new insights for policymakers; worth noting was that urban areas were better generalized by the existing ONMarg Indicators than areas of suburban/rural setting. GWR performance also scales with the unit of measurement (generally, the smaller the better, e.g. block groups, if available, tend to produce more useful results than dissemination areas)

Penn Exhibition Website: Urban Real Estate Valuation Tool (Standalone & Portable) (programmed in Python), 2021

This adaptive spatial prediction model David designed allows RE investors to identify high-return RE investment opportunities in adjoining neighborhoods pending population growth in any designated urban region, in addition to generating reliable assessment figures for insurance companies and tax agencies (parameters such as regional inflation rate, 10-year risk-free interest rate, and construction cost psf can be auto-detected from the model inputs and plugged into predefined equations, given correct field names).

Ontario Investment Office Interactive Web App, (programmed in Javascript), 2018

An example of an embedded interative web app built from scratch in JavaScript (JS), HTML and CSS by David. (note: there were slight modifications made by others in recent years).

Spatial Statistics, Machine & Deep Learning Modeling Guide by David Pan (with in-built Excel selection tools)

David’s automated model/test selectors + modeling guide, among other productivity tools, were previously published on the Ivy League Education website TSL. This popular free tool, a.k.a. the “Quick Stats Helper,” has educated and helped academics, students and professionals alike as they engage in statistical modeling/learning; and was used by organizations such as Carleton University’s Health Sciences & Informatics Department