Analytic Maps
Maps are data-driven. The world is full of spatial data that we can use to find patterns, assess needs, inform policy, or advocate for a cause. Using GIS analysis techniques and data visualization I can create a spatial decision-making system to help you harness the power of data.
Where should we plant new street trees? A spatial decision-making system
Using data from NYC Department of Health’s Environment and Health Data Portal (EHDP) and other sources I created a spatial decision-making system that could be used to select locations for new street trees in New York City. I created eight data layers that connect to considerations that stake holders will have when choosing locations for new street trees. A user can combine these eight factors using Weighted Linear Combination and give higher priority to the factors that are most important to them. Below are some sample maps from the perspective of city agencies and residents. The final frame shows consensus areas where stakeholders across the city show the most agreement. Across all maps the darkest blue areas are most suitable for new street trees and the light yellow areas are least suitable.
Dept of Health perspective prioritizing cooling, air quality, and health.
EPA perspective prioritizing cooling and air quality.
NYPD perspective map prioritizing crime prevention.
Emergency Management Dept perspective prioritizing cooling and flood damage prevention.
Social justice/equity perspective prioritizing cooling, existing vegetation, health, social equity, and population density.
Resident perspective prioritizing cooling, existing vegetation, air quality, health, crime prevention, and social equity.
Map created with even weights (prioritization) for all eight factors.
Sample of consensus areas created by adding all of the perspectives from various stakeholders.
Areas to Target Pandemic Recovery Resources
Starting with pandemic and demographic data from the NYC OpenData portal, I looked at both risk factors and social need factors that made certain communities more vulnerable to the effects of the Covid-19 pandemic. The third panel shows the overlap of zip codes with high risks and high needs, giving a starting point for targeted interventions and preventative measures by federal, state, and local government.
