A GREENER NEW YORK IS A SAFER NEW YORK · trash, planting trees, and renovating parks and gardens....

PHOTOS COURTESY OF GILES ASHFORD

A GREENER NEW YORK IS A

SAFER NEW YORK

We have long understood that our spaces have tremendous impacts on the communities that surround them. Removing signs of disorder and demonstrating care sends a message to residents that their community is valued. It supports local groups to continue driving strength from within. We commissioned Glass Frog Solutions, a research and evaluation company, to help us more accurately understand the outcomes of our work. The results were startling:

There were 213 fewer felonies each year in neighborhoods with high NYRP activity than neighborhoods with no NYRP activity.*

Glass Frog’s study compared neighborhoods where we have a strong presence (greater than 0.1% of total square footage managed by NYRP in our parks or gardens) to demographically similar neighborhoods. Our ‘treatment’ neighborhoods were East Harlem North, East Harlem South, Highbridge, Hunts Point, Marble Hill-Inwood, Washington Heights South, and Washington Heights North. The comparison neighborhoods were Central Harlem-South, Kingsbridge Heights, and Soundview-Bruckner.

This has huge implications. A growing body of evidence drawn from cities like Philadelphia, Baltimore, and Youngstown, Ohio demonstrate that improvements to green spaces have a significant impact on safety, mental health, perceptions of worthlessness, and longer-term health outcomes. However, our research is one of the first focused on New York City, particularly the most underserved communities.

With New York City’s hyper real estate market and associated risk of displacement, our administration is rightly pushing for a denser and better optimized urban landscape. There is currently an estimated 1,800 acres of city owned land – approximately two Central Parks - classified as having no current use. If revitalized into parks and gardens, the land could serve as a low-cost and highly effective tool for improving quality of life and making a safer, healthier, and happier city.

I invite you to review the full report and welcome any feedback you may have.

Deborah Marton

LETTER FROM NYRP EXECUTIVE DIRECTOR DEBORAH MARTON

At New York Restoration Project (NYRP), we believe everyone deserves access to beautiful, high-quality public green space. Since our founding in 1995 by Bette Midler, we have been partnering with underserved communities to take back public space by picking up trash, planting trees, and renovating parks and gardens. As New York’s only citywide conservancy, we bring private resources to spaces that lack adequate municipal support, fortifying the City’s aging infrastructure and creating a safer, healthier, and happier city for every New Yorker. Working together, neighbors become neighborhoods.

*213 fewer crimes each year per 40,000 residents. This is based on research findings of 3.8 fewer non-major felonies per 1,000 residents and 1.5 fewer major felonies per 1,000 residents. Figures also compare pre-2003 rates and post-2003 rates.

i

ACKNOWLEDGEMENTS

This report was prepared by Rebecca Casciano, with significant research assistance from Saul Thorkelson,

Erica Chutuape, and Pratikshya Bohra-Mishra. Jennifer Puma provided project management assistance, and

Valerie Meter provided editorial and administrative assistance. The authors thank the New York Restoration

Project staff for their time, cooperation, and guidance throughout the project. This report was written for an

internal audience at NYRP and therefore may assume a degree of familiarity with the program and its history.

The report is intended to be an overview of the methods and results. Please direct additional questions to

Rebecca Casciano at [email protected].

Report prepared by Glass Frog Solutions 2018

NYRP Impact Report Glass Frog

Solutions

ii

TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................................................. 1

OVERVIEW ..................................................................................................................................................... 3

METHODOLOGY ............................................................................................................................................ 3

Outcomes ................................................................................................................................................. 7

Crime .................................................................................................................................................... 7

Business activity .................................................................................................................................. 8

Health ................................................................................................................................................... 9

Analytic Strategy ...................................................................................................................................... 9

Identifying a “pre” and “post” period ................................................................................................. 9

Identifying the “treatment” and “comparison” groups .................................................................. 10

Methodology for measuring NYRP’s impact ................................................................................... 11

FINDINGS ................................................................................................................................................... 14

Crime ..................................................................................................................................................... 14

Business activity ................................................................................................................................... 15

Health .................................................................................................................................................... 16

CONCLUSION ............................................................................................................................................. 18


Solutions

1

EXECUTIVE SUMMARY

New York Restoration Project (NYRP) is a nonprofit organization dedicated to restoring outdoor space as a

means of improving quality of life in New York City (NYC), particularly in low-income communities. The

goal of the project is to estimate the impact of NYRP’s renovation of vacant and distressed lots on three

indicators of neighborhood well-being: crime, business activity, and health outcomes. Specifically, we answer

two questions:

1. Have neighborhoods in which NYRP has made the most significant investment experienced more

positive outcomes relative to similar neighborhoods in which NYRP has not made an investment?

(RQ1)

2. To what extent is the prevalence of restored NYRP spaces in a neighborhood correlated with

improvements in key outcomes? (RQ2)

We used a comparative interrupted time series design to estimate NYRP’s impact on the outcomes of

interest. Specifically, NYRP began investing heavily in certain neighborhoods in 2003. The results can be

summarized as follows:

• Our analyses support the hypothesis that neighborhoods in which NYRP has invested more heavily

have experienced lower crime rates than similar neighborhoods with no NYRP investment. To

contextualize the effect sizes:

o In one analysis, the non-major felony rate was significantly lower post-2003 versus pre-2003

for neighborhoods with high exposure to NYRP parks/gardens, relative to the comparison

neighborhoods. The effect size was -3.759 (p = .001). Since this is a crime rate, we can

interpret the effect as: on average, post-2003, there were annually 3.8 fewer non-major

felonies per 1,000 people in NYRP neighborhoods than in non-NYRP neighborhoods. In a

neighborhood of 40,000 people, this would translate into 152 fewer non-major felonies per

year.

o In another analysis, neighborhoods exposed to NYRP parks/gardens showed greater drops

in major felonies after 2003, compared to neighborhoods with no exposure to NYRP

parks/gardens. The difference between neighborhoods with low to moderate coverage and

neighborhoods with no coverage was statistically significant (p = .032). On average, post-

2003, there were annually 1.5 fewer major felonies per 1,000 people in neighborhoods with

low to moderate NYRP investment than in non-NYRP neighborhoods. In a neighborhood

of 40,000 people, this would translate into 61.6 fewer major felonies per year. The NYRP

effect was also negative when we compared high coverage neighborhoods to no coverage

areas, though this difference was not statistically significant.

• Overall, the data do not support the claim that NYRP has a significant impact on neighborhood

business activity.

• Our analyses suggest that NYRP may have the desired impact on residents’ perceived neighborhood

safety, as respondents living in neighborhoods with high NYRP presence reported feeling safer after

2003 than people in similar neighborhoods with no NYRP presence. Additional research is required

to understand the true nature of the relationship since the sample size and years for which data was

available was limited in this analysis.


Solutions

2

• We did not find convincing evidence that high exposure to NYRP gardens/parks is associated with

improved mental or physical health. In one analysis, survey respondents in high exposure

neighborhoods reported fewer sick days than survey respondents in neighborhoods with no exposure

to NYRP parks and gardens, though additional research is required given the results for related

outcomes were mixed.


Solutions

3

Estimating the Impact of NYRP’s Parks and Gardens on Neighborhood Well-Being

OVERVIEW

New York Restoration Project (NYRP) is a nonprofit organization dedicated to restoring outdoor space as a

means of improving quality of life in New York City (NYC), particularly in low-income communities. Created

in 1995, NYRP was an outgrowth of a local grassroots effort to beautify public space, but has expanded such

that it now has a presence in all five boroughs, in spaces ranging from small plots to large parks covering over

50 acres. In 1997, NYRP acquired over 50 community gardens that they subsequently restored and made

available to the public. Today, it manages these gardens, maintaining them and using them to provide

programming to local communities.

NYRP hypothesizes that acquiring and restoring these gardens improves the quality of life in low-income

neighborhoods by creating and maintaining open space, beautifying deteriorating lots, and encouraging

outdoor activity. The goal of the project is to estimate the impact of NYRP’s renovation of vacant and

distressed lots on three indicators of neighborhood well-being: crime, business activity, and health outcomes.

Specifically, we answer two questions:



(RQ1)



To answer our questions, we conducted two sets of analyses, which we discuss in the following section.

METHODOLOGY

We draw on a comparative interrupted time series (CITS) design to measure NYRP’s impact on outcomes

related to crime, business activity, and physical and mental health. The outcomes in our analysis are detailed

below. All outcome variables are measured at the neighborhood level and, in all analyses, we operationalize

the “neighborhood” by using New York City’s neighborhood tabulation areas (NTAs). NTAs are aggregated

Census tracts, so they are larger than a Census tract, but smaller than Public Use Microdata Areas (PUMAs).

They align closely but not perfectly with well-known neighborhood boundaries in the city. We accessed NTA

geographic data from NYC Open Data made available from the NYC Department of City Planning.1

Table 1 shows borough-specific NTA population averages for specific years between 2000 and 2015. Maps of

NTAs in Bronx, Brooklyn, and Manhattan, the three boroughs most often used in our analyses, are shown in

Figures 1a-c.2

1 New York City Department of City Planning. 2016. New York City Neighborhood Tabulation Areas (Edition 16D), Bytes of

the Big Apple. Published October 27, 2016. 2 Maps for all boroughs can be found at the New York City Department of City Planning’s website:

https://www1.nyc.gov/assets/planning/download/pdf/data-maps/nyc-population/census2010/ntas.pdf?v=022016.


Solutions

4

Table 1. NTA population estimates by year and borough. Source: NYC

Department of City Planning 2016 NYC Neighborhood Tabulation Areas.

2000 2005 2010 2015

Brooklyn 48,340 48,726 49,112 49,498

Bronx 35,070 35,760 36,450 37,140

Manhattan 53,007 53,846 54,685 55,525

Queens 38,438 38,449 38,461 38,472

Staten Island 23,354 24,012 24,670 25,328

Average 41,068 41,496 41,924 42,352

Figure 1a. Bronx NTAs. Source: Population Division, New York City Department of City Planning.


Solutions

5

Figure 1b. Brooklyn NTAs. Source: Population Division, New York City Department of City Planning.


Solutions

6

Figure 1c. Manhattan NTAs. Source: Population Division, New York City Department of City Planning.


Solutions

7

Outcomes

Crime

The New York City Police Department (NYPD) uses a data management tool called Compstat (short for

compare statistics) to track up-to-date crime-related statistics. It was introduced in 1994 and has since been

adopted by other law enforcement agencies in other major cities across the country (e.g., Baltimore, Los

Angeles, and Washington, D.C.). In addition to tracking crime statistics, Compstat functions as an

accountability model that aims to provide transparency to the public and hold local police responsible for

crime in their areas (Eterno & Silverman, 2010) with the ultimate goal of reducing crime. This analysis uses

precinct-level crime data, made publicly available through Compstat, from 2000 to 2016.3 Using these

datasets, we computed the following variables, specific to each year and individual NTA:

Major felony crime rate: number of major felony crimes per 1,000 people in NTA. Felony crimes

include: murder and non-negligible manslaughter; rape; robbery; felony assault; burglary; grand

larceny; and grand larceny of motor vehicles.

Non-major felony crime rate: number of non-major felonies per 1,000 people in NTA. Non-major

felonies include: felony possession of stolen property; forgery/fraud/identity theft; arson; felony sex

crimes; felony dangerous drugs; felony dangerous weapons; and felony criminal mischief and related

offenses.

Misdemeanor rate: number of misdemeanors per 1,000 people in NTA. Misdemeanors include:

misdemeanor possession of stolen property; misdemeanor sex crimes; misdemeanor dangerous

drugs; misdemeanor dangerous weapons; petit larceny; assault 3 and related offenses; intoxicated and

impaired driving; vehicle and traffic laws; misdemeanor criminal mischief and related offenses;

criminal trespass; unauthorized use of a vehicle; offenses against the person; offenses against public

administration; administrative code; frauds; aggravated harassment; and other misdemeanors.

Figure 2 shows average NTA-level crime and misdemeanor rates between 2000 and 2015 in Brooklyn, Bronx,

and Manhattan (the boroughs primarily used in this analysis). As the chart indicates, all three rates dropped

between 2000 and 2015. These data are reported at the precinct level; we used geographic software to convert

the geographic identifiers to NTAs.

3 Prior to 2000 crime data were collected and reported using different methods and sources. Before 2000, the NYPD

published “Arrests and Complaints” (hereafter “AC”) for the seven major felonies, seven non-major felonies, and

misdemeanors at the precinct level. After 2000, the NYPD switched to Compstat for publishing crime statistics. However,

the reports of crime data pre-2000 do not always correspond with crime data post-2000 (as reported by Compstat).

Specifically, the precinct totals do not match across the two different data sources. For example, the total number of seven

major felony offenses in AC reports is different than the total number in Compstat reports for the same year. These

inconsistencies have been examined by researchers who have analyzed crime data over the past few decades (see for

example, Geller, A. (2011). Measuring New York City: A Cautionary Tale. Understanding the Crime Decline in New York City.

John Jay College of Criminal Justice, New York, NY.). Moreover, the introduction of Compstat changed the way crime data

were reported. Used as an accountability and managerial tool, police face organizational pressure to demonstrate reduced

crime in their areas, which may be represented by increasing the number of arrests and/or downgrading crimes to lesser

offenses. Such pressures to demonstrate a reduction in crime through statistical reports have resulted in some

inaccuracies in the reporting of crime data (see, for example, Eterno, J.A. & Silverman, E.B. (2010). The NYPD’s Compstat:

compare statistics or compose statistics? International Journal of Police Science and Management, 12(3). 426-449.). This

further underscores that Compstat data cannot be reliably compared to AC data pre-2000. As such, we do not include

crime data prior to 2000 due to these inconsistencies.


Solutions

8

Business activity

The United States Census produces zip-code level data on business activity using the North American

Industry Classification System (NAICS) to classify activity by type of business. The Census produces annual

estimates on the total number of businesses, as well as the number of businesses across 20 NAICS sectors.

We draw on this data to estimate changes in business activity over the course of the time period under study.

For each year and individual NTA, we computed the total number of businesses as well as the number of

businesses in the following NAICS sectors: construction; manufacturing; retail trade; transportation and

warehousing; information; finance and insurance; real estate and rental and leasing; professional, scientific,

and technical services; management of companies and enterprises; administrative and support and waste

management and remediation services; educational services; health care and social assistance; arts,

entertainment, and recreation; accommodation and food services; other services. Since these data are reported

by the U.S. Census at the zip code level, we used geographic software to convert the geographic identifiers to

NTAs.

Figure 3 shows average number of businesses between 1998 and 2015 in Brooklyn, Bronx, and Manhattan.

As the chart indicates, on average, the average number of businesses grew steadily over this time period.

0

10

20

30

40

50

60

70

80

2000 2005 2010 2015

# incidents per

1,000 people in

NTA

Figure 2. Average NTA-level crime and misdemeanor rates, by year. Source:

2000 - 2015 NYC COMPSTAT data.

misdemeanors

major crimes

non-major crimes


Solutions

9

Health

Starting in 2002, the New York City Department of Health and Mental Hygiene (Division of Epidemiology,

Bureau of Epidemiology Services) has administered an annual telephone survey to a probability sample of

New York City adult residents across all five boroughs. The survey, known as the Community Health Survey

(CHS), aims to track and provide public information on the health and health-related behaviors of New

Yorkers. We draw on data from the CHS to measure the extent to which proximity to NYRP parks and

gardens impacts residents’ mental and physical health, as well as their perceived feelings of safety. The CHS

does not ask all questions in all years, so in some cases we were limited to data from only two years of the

study. Table 2 below shows the variables included in the study, as well as the years from which they were

available. Importantly, CHS data is collected from a random sample of NYC residents across United Hospital

Fund (UHF) neighborhood sites; we used geographic software to convert UHFs to NTAs for purposes of

this analysis.

Analytic Strategy

Identifying a “pre” and “post” period

We use the CITS design to measure NYRP’s impact on neighborhood-level outcomes. A CITS design

requires us to determine a “pre” and a “post” period for the analysis. In this case, the “pre” period

corresponds to the time period directly preceding NYRP’s neighborhood investment and the “post” period

corresponds to the time period following NYRP’s investment. Since clean up on all properties began

sometime in 2001, we selected 2003 as the cutoff year. This allows for adequate time for the clean up to be

completed and be made available for public use. Thus, in all analyses, the “pre” period includes all pre-2003

years for which data are available and the “post” period includes 2003 and all post-2003 years for which data

are available.

0

20

40

60

80

100

120

140

1998 2003 2008 2013

# business

establishments

Figure 3. Average number of businesses in NTAs, by year. Source:

1998 - 2015 U.S. Census data.


Solutions

10

Table 2: CHS variables included in analysis.

Outcome Definition Years data

available

Average

across all

years*

Perceived neighborhood

safety

Score on “safe neighborhood scale;” scale

ranges from 1 (extremely safe) to 4 (not at all

safe). Based on question, “How safe from

crime do you consider your neighborhood to

be?”

2002, 2010 2.3

Mood scale

Average of responses to six questions; scale

ranges from 1 (poor mental health) to 5

(good mental health).

2002, 2003,

2005, 2006,

2008, 2009,

2010, 2012,

2013

4.3

Good physical health

Respondent indicated that health is excellent

or very good (yes/no, aggregated to a

percentage of NTA residents indicating that

health is excellent or very good)

2002 – 2014 45.7%

Number days poor physical

health

Number of days in past 30 days when

physical health was not good.

2002, 2005,

2006 4.0

Body Mass Index (BMI) Calculated based on self-reported height and

weight. 2002 – 2014 26.8

*Includes NTAs in Brooklyn, Bronx, and Manhattan only, since these boroughs are over-represented in the

analysis.

Identifying the “treatment” and “comparison” groups

As described earlier, this report seeks to answer two research questions (see page 1). The sample of NYRP

neighborhoods (i.e., “treatment” neighborhoods) and comparison neighborhoods differs for each analysis.

Sample for RQ1. This analysis compares neighborhoods that have received the greatest NYRP investment to

neighborhoods that were demographically similar in the pre-treatment period but did not receive any NYRP

investment. For this analysis, we selected the neighborhoods with the greatest NYRP coverage (as measured

in square feet). Neighborhoods with greater than .1% of total square footage covered in NYRP parks or

gardens are considered the “treatment” neighborhoods. These neighborhoods include East Harlem North,

East Harlem South, Highbridge, Hunts Point, Marble Hill-Inwood, Washington Heights South, and

Washington Heights North. The comparison neighborhoods were selected by using a propensity score

matching procedure to match other NYC neighborhoods to neighborhoods with the greatest NYRP coverage

on specific variables including poverty rate, unemployment rate, percentage black/Hispanic, and percentage

65 years or older as measured in year 2000. The comparison neighborhoods are Central Harlem-South,

Kingsbridge Heights, and Soundview-Bruckner. (Central Harlem-South is given extra weight in order to

create two balanced samples.) Table 3 shows characteristics of NYRP and comparison neighborhoods based

on 2000 Census data. As the table indicates, the two groups were similar, on average, prior to the NYRP

investment.


Solutions

11

Table 3. Year 2000 neighborhood-level characteristics for NYRP (i.e.,

treatment) NTAs and comparison NTAs. Source: 2000 U.S. Census data.

NYRP Comparison

% unemployed 16.9 16.5

% below poverty line 35.0 34.8

% black or Hispanic 88.5 88.8

% aged 65 or older 70.8 70.8

Sample for RQ2. This analysis compares neighborhoods with moderate or high exposure to NYRP parks

and gardens to those with no exposure. The analysis includes all neighborhoods in NYC with a federal

poverty rate of 30 percent or higher. We limit to high poverty neighborhoods because this is the type of

neighborhood that NYRP would invest in and therefore want to make generalizations about. For all

neighborhoods in NYC, we created a variable that indicates the total square footage of NYRP parks and

gardens within 1.5 miles of the neighborhood. This variable is meant to be a proxy for how much exposure

residents get to the NYRP parks and gardens. We assume that more proximate green space (i.e., green space

within or close to the neighborhood) is more impactful and therefore give those gardens/parks heavier

weighting than those that are farther away.4

We then use this measure to create a variable indicating each neighborhood’s overall exposure to NYRP

parks and gardens (i.e., the total square footage within 1.5 miles). This variable has three categories or groups:

Group Coverage Total square feet (weighted) % NTAs in group

Average Range

1 No coverage 0 0 11.6

2 Low to moderate 26,468 5,008 – 48,910 58.1

3 Moderate to high 994,896 52,103 – 4,268,300 30.2

Methodology for measuring NYRP’s impact

The CITS design then requires us to do the following:

1. Estimate a neighborhood’s “baseline” (i.e., pre-2003) trend. For all NTAs included in the analysis,

we estimated a baseline trend on the outcome variable. For example, if the outcome is the number

of new business establishments in an NTA, then the baseline trend is the number of new businesses

in the NTA from 1998 to 2002 and, more specifically, the annual change in the number of new

businesses during this time (i.e., the slope of the line). Importantly, for all analyses using the CHS

data, we only had one year of baseline data; thus, the baseline trend is not actually a trend and should

4 Each park/garden is given a weight that is equal to: 1.5 – “distance from the NTA.” This weight insures that

parks and gardens that are within an NTA (distance = 0 miles) receive the maximum weight (1.5) and parks

and gardens that are farther away receive less weight. For example, an NYRP garden that is 0.5 miles away

from NTA X will receive a weight of 1.0 (i.e., 1.5 – 0.5), whereas an NYRP garden that is 1.4 miles away from

NTA X will receive a weight of 0.1 (i.e., 1.5 – 1.4). This weight is multiplied by the total square footage of the

park or garden. As an example, if the garden is 10,000 square feet and is located within the NTA (i.e., is zero

miles away), it will be adjusted to be 15,000 square feet ((1.5 - 0) * 10,000). If the same garden is 1.4 miles

away, it will be adjusted to be 1,000 square feet. This weighting methodology helps ensure that parks and

gardens that are geographically closer to the NTA receive greater weighting in the analysis.


Solutions

12

instead be interpreted as the pre-treatment average in 2002.5 We estimate a baseline trend for both

the treatment and the comparison group.

2. Estimate a neighborhood’s post-treatment (i.e., 2003 and later) trend. In contrast to the baseline

trend, the post-treatment trend measures what occurred in the NTA after the NYRP completed its

investment. Using number of new businesses as an example, the post-treatment trend is the number

of new businesses in the NTA from 2003 to 2015 and, more specifically, the annual change in the

number of new businesses during this time (i.e., the slope). Once again, we estimate a post-treatment

trend for both the treatment and the comparison group.

3. Estimate whether there is a difference between the baseline trend and the post-treatment trend and

whether this difference is greater or smaller for NYRP neighborhoods, relative to the comparison

group(s). This estimation provides the “NYRP effect,” which is equal to the difference between the

pre-2003 and post-2003 effects for NYRP sites and the comparison sites.

This process is executed using a statistical model that also includes neighborhood-level control variables that

may be associated with both NYRP’s likelihood to invest in the neighborhood, as well as the outcome

variables, including: percentage female residents, percentage black or Hispanic residents, percentage

unemployed residents, percentage below the federal poverty line, percentage aged 18 to 24, percentage 65 or

older, population density, and borough fixed effects. Lastly, we also account for random effects at the NTA

level, since the NTAs are clustered over time.

For all analyses related to RQ1, we present one NYRP effect size, which is the difference in the baseline and

post-treatment trend lines between the highest investment NYRP neighborhoods and the comparison

neighborhoods. For RQ2, we provide two NYRP effects. The first (“low to moderate NYRP coverage”)

gives the difference in the trend lines between the low-to-moderate NYRP neighborhoods and

neighborhoods with no NYRP investment. The second (“high NYRP coverage”) gives the difference in the

trend lines between the high NYRP neighborhoods and neighborhoods with no NYRP investment.

Table 4 presents an overview of the results across all outcomes. We indicate where results are statistically

significant in the hypothesized direction. We also indicate results that are statistically significant but not in the

hypothesized direction, as well as results that are marginally significant or insignificant.

5 For analyses using the CHS data, the design is technically a difference-in-difference, because we do not have

sufficient baseline data to establish a trend.


Solutions

13

Table 4. Overview of results from NYRP impact analyses

RQ1 Analysis RQ2 Analysis

Crime

Major felonies --

Non-major felonies

Misdemeanors -- --

Businesses

Average # businesses -- --

Construction -- --

Manufacturing -- --

Wholesale trade -- --

Retail trade -- --

Transportation and Warehousing

Information -- --

Finance and Insurance -- --

Real estate --

Professional, scientific, and tech. services

Mgmt. of companies and enterprises --

Administrative and support and waste management and remediation

services -- --

Educational services --

Health care and social assistance -- --

Arts, entertainment, and recreation -- --

Accommodation and food services -- --

Other services -- --

Health

Perceived neighborhood safety --

Mood scale -- --

Good physical health -- --

# days poor physical health --

Body Mass Index (BMI) -- --

Key

Statistically significant in the hypothesized direction

Marginally statistically significant

Statistically significant, not in the hypothesized direction

-- No significant effect


Solutions

14

FINDINGS

Crime

Crime data was available for 2000 to 2016; thus, the baseline trend includes data from 2000 to 2002 and the

post-treatment period is 2003 to 2016. If NYRP is having an impact, we would expect the crime rates in the

neighborhoods in which NYRP invests more heavily to be lower than they are in similar neighborhoods with

no NYRP investment. This would mean we would expect the NYRP effect size to be negative. Negative

numbers indicate that crime rates for NYRP neighborhoods were lower after 2003 versus before 2003,

compared to comparison neighborhoods with no access to NYRP parks or gardens.

The results are presented in Table 5. The results for RQ1 are as follows:

• The major felony rate was lower in high exposure NYRP neighborhoods in post-2003, relative to pre-

2003 (compared to comparison neighborhoods with no exposure to NYRP parks/gardens), though

the difference was not statistically significant.

• The non-major felony rate was significantly lower post-2003 versus pre-2003 for neighborhoods with

high exposure to NYRP parks/gardens, relative to the comparison neighborhoods. The effect size was

-3.759 (p = .001). Since this is a crime rate, we can interpret the effect as: on average, post-2003, there

were annually 3.8 fewer non-major felonies per 1,000 people in NYRP neighborhoods than in non-

NYRP neighborhoods. In a neighborhood of 40,000 people, this would translate into 152 fewer non-

major felonies per year.

• Relative to the comparison neighborhoods, the misdemeanor rate was lower in high exposure NYRP

neighborhoods post-2003 than in pre-2003, though the difference was not statistically significant.

The results for RQ2 are as follows:

• Neighborhoods exposed to NYRP parks/gardens showed greater drops in major felonies after 2003,

compared to neighborhoods with no exposure to NYRP parks/gardens. The difference between

neighborhoods with low to moderate coverage and neighborhoods with no coverage was statistically

significant (p = .032). On average, post-2003, there were annually 1.5 fewer major felonies per 1,000

people in neighborhoods with low to moderate NYRP investment than in non-NYRP

neighborhoods. In a neighborhood of 40,000 people, this would translate into 61.6 fewer major

felonies per year. The NYRP effect was also negative when we compared high coverage

neighborhoods to no coverage areas, though this difference was not statistically significant.

• Neighborhoods with exposure to NYRP parks/gardens show larger drops in non-major felonies

after 2003, compared to neighborhoods with no exposure to NYRP parks/gardens. The difference

between neighborhoods with high coverage and neighborhoods with no coverage is nearly

statistically significant (p = .093).

• The misdemeanor rate in neighborhoods with NYRP parks/gardens was higher after 2003,

compared to neighborhoods with no exposure to NYRP parks/gardens, though the difference was

not statistically significant. (It is unclear why the effect size is positive. It is inconsistent with the

other findings.)


Solutions

15

Table 5. Summary of findings on NYRP’s impact on neighborhood-level crime rates.

RQ1 Analysis* RQ2 Analysis *

NYRP "effect"

Low to moderate

NYRP coverage

High NYRP

coverage

7 major felonies -0.818 -1.541 -1.028

7 non-major felonies -3.759 -1.151 -2.742

Misdemeanors -5.046 5.987 2.003

*Statistically significant effects (p<.05 level) are bold-faced when the effect is in the expected

direction. Effect sizes approaching significance (p<.10 level) are italicized. Models also include

controls for % female in NTA, % black or Hispanic in NTA, % unemployed in NTA, % poor in NTA, %

aged 18-24 in NTA, % aged 65 or above in NTA, NTA population density, and borough fixed effects.

We also account for random effects at the NTA level since the NTAs are clustered over time.

Business activity

Data on business activity was available for 1998 to 2015; thus, the baseline trend includes data from 1998 to

2002 and the post-treatment period is 2003 to 2015. If NYRP is having an impact, we would expect the

number of businesses to be greater in the neighborhoods in which NYRP invests more heavily than in similar

neighborhoods with no NYRP investment. This would mean we would expect the NYRP effect size to be

positive. Positive numbers indicate that the NYRP investment is associated increased business activity,

relative to the comparison group. The results are presented in Table 6. Results in black, bold font are

statistically significant in the expected direction; black italicized font indicates the effect was nearly significant.

Results in red, bold font are statistically significant but in the opposite direction (i.e., indicating negative

growth); red, italicized font indicates the effect was nearly significant.

The results for RQ1 and RQ2 are as follows:

• Across each analysis, the data indicate that NYRP neighborhoods have roughly 40 additional

businesses, on average, but the effect is not statistically significant.

• In some specific industries, the effect size is positive and statistically significant; however, it is

negative and significant in other cases.

• Overall, the data do not support the claim that NYRP has a significant impact on the number of

businesses in an NTA.


Solutions

16

Table 6: Summary of findings on NYRP’s impact on neighborhood-level business activity.


NYRP "effect"

Low to moderate

NYRP coverage

High NYRP

coverage

All 42.005 41.820 39.887

Construction 0.205 -3.885 -4.860

Manufacturing 0.189 3.883 4.920

Wholesale trade -1.449 -6.216 -7.093

Retail trade -1.913 8.061 3.335

Transportation and Warehousing -1.920 -2.295 -4.944

Information 0.424 0.386 -0.556

Finance and Insurance -0.926 -13.699 -3.225

Real estate 1.123 8.892 11.225

Professional, scientific, and tech. services 8.052 8.509 5.727

Mgmt. of companies and enterprises -0.238 0.377 0.707

Administrative and support and waste

management and remediation services 0.582 0.643 0.801

Educational services 0.526 4.280 3.710

Health care and social assistance 1.433 3.140 2.470

Arts, entertainment, and recreation 0.377 0.210 -1.492

Accommodation and food services 6.640 0.852 -0.953

Other services 5.397 1.950 5.805

*Statistically significant effects (p<.05 level) are bold-faced when the effect is positive direction, and are

bold-faced in red when the effect is negative. Italicized numbers indicate the effect was nearly significant

(p<.10). Models also include controls for crime rates in year 2000, % female in NTA, % black or Hispanic in

NTA, % unemployed in NTA, % poor in NTA, % aged 18-24 in NTA, % aged 65 and above in NTA, NTA

population density, and borough fixed effects. We also account for random effects at the NTA level since

the NTAs are clustered over time.

Health

CHS data was available for 2002 to 2014, though not all questions are asked in all years (see Table 2 for a

description of the variables and the years for which they are available). The results are presented in Table 7

and summarized here:

• Perceived neighborhood safety. We would expect the NYRP effect to be negative, indicating that

people who live near NYRP parks/gardens feel their neighborhood is safer in 2010 than they did in

2002, compared to residents who live in neighborhoods with no exposure to NYRP parks or

gardens. In the analysis for RQ1, the NYRP effect on the perceived neighborhood safety measure


Solutions

17

was -0.093, suggesting that people with greater exposure to NYRP parks/gardens report feeling that

their neighborhood is safer in 2010 than in 2002, compared to people with no exposure to NYRP

parks/gardens. This effect is statistically significant (p = .000). We did not find a significant effect in

the RQ2 analysis.

• Mood scale. On this scale, higher numbers indicate more symptoms, so we would see a negative

effect size if our hypothesis is supported (i.e., if people who live near NYRP parks/gardens report

better mental health after 2003 vs. before 2003, compared to residents who did not live near any

NYRP parks or gardens). We find negative effect sizes in both the RQ1 and RQ2 analyses, though

neither is statistically significant.

• Good physical health. Since this reports the percentage of people reporting good physical health,

we would expect to see positive effect sizes. We see a positive but statistically insignificant effect size

in the RQ1 analysis. We see negative (but insignificant) effects in the RQ2 analysis.

• Number of days poor physical health. We would expect the NYRP effect on the number of days

residents reported poor physical health to be negative. In RQ1, the effect size is negative but

statistically insignificant. The results for RQ2 indicate that, relative to people who do not live near

any NYRP parks or gardens, people with low-to-moderate exposure to NYRP parks/gardens report

significantly fewer "poor physical health" days after 2003. Specifically, compared with 2002, in

2005/2006, people living in low-to-moderate NYRP neighborhoods report 2.1 fewer sick days in the

last month, on average, than people with no exposure to NYRP parks/gardens.

• Body max index (BMI). We would expect NYRP to have a negative effect on BMI, since

hypothetically greater exposure to NYRP parks and gardens would be associated with more outdoor

activity and therefore lower BMIs. In the RQ1 analysis, the results indicate that the NYRP effect was

positive but statistically insignificant. The results for the RQ2 analysis were negative, but also

statistically insignificant.

Table 7: Summary of findings on NYRP’s impact on neighborhood health outcomes.


Outcome

Hypothesized

direction of effect if

NYRP has an impact

NYRP "effect" Low to moderate

NYRP coverage

High NYRP

coverage

Perceived neighborhood safety Negative -0.093 0.027 0.039

Mood scale Negative -0.058 -0.011 -0.020

Good physical health Positive 0.101 -0.058 -0.038

# days poor physical health Negative -0.390 -1.689 -2.016

Body Mass Index (BMI) Negative 0.621 -0.441 -0.149

*Statistically significant effects (p<.05 level) are bold-faced when the effect is in the expected direction. Effect

sizes approaching significance (p<.10 level) are italicized. Models also include controls for % female in NTA, %

black or Hispanic in NTA, % unemployed in NTA, % poor in NTA, % aged 18-24 in NTA, % aged 65 or above in

NTA, NTA population density, and borough fixed effects. We also account for random effects at the NTA level

since the NTAs are clustered over time.


Solutions

18

CONCLUSION

To summarize, the goal of this project was to estimate the impact of NYRP’s renovation of vacant and

distressed lots on three indicators of neighborhood well-being: crime, business activity, and health outcomes.

We used a comparative interrupted time series to answer two specific questions:



(RQ1)



The results can be summarized as follows:

• Our analyses support the hypothesis that neighborhoods in which NYRP has invested more heavily

have experienced lower crime rates than similar neighborhoods with no NYRP investment. To

contextualize the effect sizes:

o In one analysis, the non-major felony rate was significantly lower post-2003 versus pre-2003

for neighborhoods with high exposure to NYRP parks/gardens, relative to the comparison

neighborhoods. The effect size was -3.759 (p = .001). Since this is a crime rate, we can

interpret the effect as: on average, post-2003, there were annually 3.8 fewer non-major

felonies per 1,000 people in NYRP neighborhoods than in non-NYRP neighborhoods. In a

neighborhood of 40,000 people, this would translate into 152 fewer non-major felonies per

year.

o In another analysis, neighborhoods exposed to NYRP parks/gardens showed greater drops

in major felonies after 2003, compared to neighborhoods with no exposure to NYRP

parks/gardens. The difference between neighborhoods with low to moderate coverage and

neighborhoods with no coverage was statistically significant (p = .032). On average, post-

2003, there were annually 1.5 fewer major felonies per 1,000 people in neighborhoods with

low to moderate NYRP investment than in non-NYRP neighborhoods. In a neighborhood

of 40,000 people, this would translate into 61.6 fewer major felonies per year. The NYRP

effect was also negative when we compared high coverage neighborhoods to no coverage

areas, though this difference was not statistically significant.

• Overall, the data do not support the claim that NYRP has a significant impact on business activity in

an NTA.

• Our analyses suggest that NYRP may have the desired impact on residents’ perceived neighborhood

safety, as respondents living in neighborhoods with high NYRP presence reported feeling safer after

2003 than people in similar neighborhoods with no NYRP presence. Additional research is required

to understand the true nature of the relationship, since the sample size and years for which data was

available was limited in this analysis.

• We did not find convincing evidence that high exposure to NYRP gardens/parks is associated with

improved mental or physical health. In one analysis, survey respondents in high exposure

neighborhoods reported fewer sick days than survey respondents in neighborhoods with no exposure

to NYRP parks and gardens, though additional research is required, given the results for related

outcomes were mixed.

A GREENER NEW YORK IS A SAFER NEW YORK · trash, planting trees, and renovating parks and gardens....

Documents

Transcript of A GREENER NEW YORK IS A SAFER NEW YORK · trash, planting trees, and renovating parks and gardens....