New York City – Vacant Lots Suitable for Residential Development

Based on reports published by the Department of City Planning, New York City’s population is projected to grow from 8.2 million in 2010 to 9 million in 2040.

For the same period, the number of housing units is projected to grow from 3,375,002 to 3,696,359.
New housing can be achieved by redevelopment of existing buildings or new development on vacant spaces and brownfields.

Approach

My study proposes a framework that combines different quantifiable indicators into a composite index. This concept brings two subsequent questions: Which are the most relevant metrics to be included in the index aiming at assessing urban density and residential development suitability? What is good versus bad performance?

There is a solid body of literature on both Multi-Criteria Decision Making (MCDM) and Urban Residential Densification. nyc_lots multi-criteria decision analysis can be thought of as a process that combines and transforms geographical data (input) into a resultant decision (output) (Malczewski, 1999). The number of factors and relationships among them cause difficulties in decision making. GIS and MCDM techniques offer support to decision makers solving nyc_lots decision problems.

A number of studies were done in different countries, in the past twenty years in the area of urban development and densification – one of the pioneers being Urban Governance, Social Inclusion and Sustainability (Europe, 200-2003). After reviewing the literature another question arose: What targets must be pursued in densification strategy for it to be successful? Due to New York City’s geography, densification has to make more people live in a given area. Successful densification also makes people travel less to their work, shopping and leisure places in order to reduce car use, commuting time and cost, and congestion. Therefore, densification has to create mixed-use environments. Other targets are the accessibility to public transportation, parks and open spaces, and schools. Eventually, suitable densification results in a high-quality urban environment that is not deemed as over-crowded by residents.

Keeping these targets in mind, I selected eight factors to compute relevant metrics. These are the key performance indicators that make up the composite assessment index:
1. Residential density
2. Housing density
3. Land use diversity
4. Proximity to subway
5. Proximity to schools
6. Proximity to parks
7. Real estate market
8. Proximity to buses

Study Areas

1600 feet buffers were derived around lot centroids and they were clipped to the shoreline, so the density calculations are consistent. This would provide a study area of approximately 0.29 square miles around each lot, which is appropriate for the computation of densities and distances. Out of the 7,936 lots very few are larger than the centroid buffer and I considered the fact not to impact the study. Based on location, the number of schools, transit stops, parks and other features vary within each buffer.

Study areas around vacant lots.

NYC Street & Transit Network

A transit network using GTFS data was created for all proximity and travel time calculations. It includes streets, subway, bus, express bus and ferries systems. Details regarding the network creation and analysis can be seen here.

Vacant lots location relative to the subway network.

Compilation of Composite Index

The different indicators were defined and calculated then weighted and aggregated in the composite index. To do so, the results were first normalized, then each indicator was assigned a weight. I obtained the normalized values by dividing each study area’s value by the highest in the category, resulting in a 0 to 1 range. There are various ways to calculate weights. I originally adjusted the neutral scores to “add or reduce weight”. Subsequently I used rating and pairwise comparison methods to compile alternative indexes. The idea is that the users/decision makers are able to decide upon the importance of the factors for their own purpose. The results presented here show just one in many possible outcomes, the map to the left was produced using the adjusted weights (values in purple in the table below).

Results & Interpretation

The map shows the results of our analysis. The ranking is divided in seven classes, displayed here in quantile mode, from yellow to dark red. The histogram shows a right skewed distribution.

Looking on the map we see that all boroughs have a good mix of lots with a higher concentration of the better ranking ones in Brooklyn. It’s apparent that proximity to the subway is one factor which influences the results the most, in this particular case.

The composite index diagram shows the six top ranked sites where we can see that all scored very low on the Real Estate factor, mixed and medium on Proximity to Schools and Parks and very high on the others. It would be very facile to implement other scenarios by changing the factors, as well as their computation method and weights.

This suitability model provides a flexible tool, which can be used for various scenarios by assigning different factors and weights in the index computation.