Transcript Permitting Public Health: Are Mixed Land Use Zones Improving Walkability? Carol Cannon, MA; Sue Thomas, PhD; Ryan Treffers, JD; Mallie J.

Permitting Public Health:
Are Mixed Land Use Zones Improving Walkability?
Carol Cannon, MA; Sue Thomas, PhD; Ryan Treffers, JD; Mallie J. Paschall, PhD; Lauren Heumann, BS; Gregory Mann, BA; Dashiell Dunkell, MS; Saskia Nauenberg, BA
HBSA: A Supporting Organization of Pacific Institute for Research and Evaluation
DISCUSSION
CDC’s Healthy Places Program identifies zoning and land use as
critical for healthy communities;
Zoning that segregates work, home, commercial, public and civic daily
use activities perpetuates automobile use as primary transportation
mode and discourages daily physical activities such as walking or
biking;
Public health compromised by diminished physical activity, leading to
poor health indicators--obesity, diabetes, cancer, etc.
LEGAL DATA: INDEPENDENT VARIABLE
Variable: Comprehensiveness of each MUZ ordinance (adherence to APA guidelines)
Legal Score (Comprehensiveness of Ordinance) and
Walkability Score (Percentage of Daily Use Activities Within Zones)*
Zoning ordinances from 22 California cities (populations of ≥ 50,000) collected and
reviewed to identify mixed use zoning. Ordinances coded as allowing mixed use if:
Independent Variable: Legal Final Score
Dependent Variable: Total Percent of Walkability Use Categories Present In Zone
One or more residential use(s) and
One or more commercial use(s) permitted or conditionally permitted.
Unstandardized
Coefficients
If ordinance only permitted public/civic uses and either ≥ one commercial OR ≥ one
residential uses, ordinance not coded as mixed use.
B
(Constant)
Strategy: Implement Mixed Land Use Zones (MUZs) to shorten
distance between home and daily activities.
Is this strategy working? Does mixed use zoning result in greater
walkable proximity to daily use activities?
* Defined as being pedestrian-friendly, pedestrian-accessible or pedestrian-oriented.
METHODOLOGICAL GOALS
1) To determine the feasibility of using legal zoning data to predict
neighborhood walkability. This effort was designed to augment the
case studies prevalent in the field which use tools such as audits of
community features, public opinion and public behavior surveys, and
archival data to produce comparative, cross-sectional investigations.
2) To determine if the research design and tools available for this research
produce interesting and important findings about the relationship
between governmental policy and the walkability of mixed use zones
within cities.
SCORING AND ANALYSIS
Walkability scoring: Each zone measured by number and percentage of
use categories present, as well as total number of businesses found.
Bivariate and multivariate analysis of relationships among legal and
walkability data, including:
Correlation of ordinance’s final legal score to percentage of
walkability use categories present in zone created by ordinance;
Control variables:
City population size
Socio-economic Status (SES)
Size (area in square kilometers) of each zone
.204
.026
46.065
Median Household Income
t
Sig.
7.799
.000
2.980
.662
15.458
.000
.000
.000
-.221
-1.843
.067
Race/Ethnicity: Black only
-153.537
51.560
-.302
-2.978
.003
Race/Ethnicity: White only
-1.798
.921
-.101
-1.954
.052
Race/Ethnicity: Hispanic/Latino/a of any race
-87.769
31.929
-.698
-2.749
.006
Race/Ethnicity: American Indian/Alaskan only
330.184
77.563
.260
4.257
.000
Race/Ethnicity: Pacific Islander /Hawaiian only
796.752
543.497
.099
1.466
.144
-245.727
59.675
-1.094
-4.118
.000
Graduate/Professional Degree (% age 25+)
208.839
37.778
.751
5.528
.000
Conditional mixed use (residential and/or commercial uses conditionally permitted)
Age: 19 and under
118.262
64.367
.378
1.837
.067
Overlay (zone that creates mixed use by combining more than one zone type)
Age: 35 to 59
455.097
138.444
.718
3.287
.001
Other (zones that did not fit into above categories but still facilitated the mixing of uses)
Age: 60 and over
-166.273
99.453
-.260
-1.672
.096
168 Mixed Land Use Zone Ordinances identified. Each ordinance coded for 39 use
measures;
Area of Zone in Square Kilometers
Four general use categories: Residential, Public and Civic, Commercial, and Industrial.
Ordinances were categorized by type of mixed use zoning mandated:
Explicitly mixed use (defined as such in wording of ordinance)
Mixed use by right (residential and commercial uses permitted without conditions)
High School graduates (% age 25+)
* Controlling for city SES characteristics and area of zone in square kilometers.
Legal Coding
Ordinance Wording
Commercial
Redwood City, CA
CN (Neighborhood Commercial District)
Zoning Code, art. 13 (2010)
13.1 Purpose
To provide centers for convenience shopping in the
residential neighborhood planned and controlled to the
extent that such centers will perform a vital service to the
neighborhoods and become integral parts thereof.
(Ord.
1130, eff. 7-10-64)
13.2 Permitted Uses.
The following uses are permitted in the CN District if
conducted entirely within a building:
A. Grocery, retail bakery sales, drug, variety or hardware
stores;
B. Beauty, barber, shoe, gift, stationery, record, toy or flower
shops;
C. Neighborhood serving, ground floor dependent offices,
financial services & medical offices, subject, however, to
Section 13.10 provisions;
D. Soda fountains & restaurants, not including live
entertainment, dancing, or sale of liquor, beer, or other
alcoholic beverages for consumption on the premises;
E. Studios for arts, crafts, photography & dance;
F. Parking garages for customer & employee parking only;
Permitted
Animal services (including sales, grooming, veterinary)
x
Artist work or sales spaces
Drive-through facilities
Unstandardized
Coefficients
Bars, taverns
Entertainment and spectator sports venues
Financial services
x
Food and beverage retail sales
x
Gas stations
Offices
Independent Variable
(Legal)
Equation #1:
Entertainment Score
Parking, commercial (non-accessory)
Personal services including health clubs, gyms, laundry/dry
cleaners, etc.
x
Repair services, consumer, including bicycles
Equation #2:
Financial Score
Residential storage warehouses
x
Retail sales, general
Vehicle sales, service, repair, auto parts & repair stores
WALKABILITY DATA: DEPENDENT VARIABLE
Variable: Number and type of daily activity uses in MUZ neighborhoods
Google Earth used (key word searches, layers) to identify 43 locations and businesses
constituting daily activity destinations.
Name of businesses/destinations and total number in each category recorded.
* Two geographical zones for each ordinance were not always present, hence the lower n.
Interviews with City Planners: 15 City Planners interviewed about the history and
implementation of mixed use zoning in their cities.
Zoning Map
OLS Regression Analyses:
Key Results Across Individual Equations for Subsets of Walkability*
x
Restaurants
Google Earth Map: “Grocery Store” Layer
Key Markets
Correlation of individual legal use scores to presence of analogous
walkability use activities and number of businesses in zone.
59.628
Beta
.362
For each MUZ type, two geographical zones randomly selected for analysis. (n=265).*
Legal Scoring: Uses scored from 0 to 6 in each ordinance for closeness
to, or distance from, APA model, taking into consideration both
presence of use in ordinance and level of permission (explicitly
permitted, conditionally permitted, or not permitted). Each ordinance
given a final cumulative score.
69.921
Legal Final Score
Hypothesis:
Controlling for city population size and SES, the higher the MUZ
comprehensiveness, the higher its walkability.
Std. Error
.242
RESEARCH QUESTIONS
2) What is the relationship between MUZ comprehensiveness (defined as
greater adherence to the American Planning Association’s model MUZ)
and walkability* of the resulting neighborhoods?
Standardized
Coefficients
1.173
Coded for whether use is explicitly permitted, conditionally permitted, or not permitted.
1) Can land use zoning create conditions for improved public health? Are
municipal mixed use zone (MUZ) ordinances effective tools to increase
walkable proximity to businesses and services, and ultimately, to
improve public health?
PRELIMINARY CONCLUSIONS
PRELIMINARY RESULTS
Equation #3:
Food Score
Equation #4:
Medical Services Score
Equation #5:
Office Score
Equation #6:
Personal Services Score
Equation #7:
Repair Score
Equation #8:
Restaurant Score
Equation #9:
Retail Score
Dependent Variable
(Walkability)
Presence of Entertainment (movie
theaters, performing arts, sports
arenas, other) in zone
Presence of Financial Services
(banks, ATMs, insurance, tax, and
other related businesses) in zone
Presence of Food Sales (groceries,
markets, convenience stores) in
zone
Presence of Medical Offices
(doctors, dentists, clinics, other
healthcare services) in zone
Presence of Offices (attorneys,
computer services, government,
other non-retail businesses/offices)
in zone
Presence of Personal Services
(health and fitness, beauty, laundry,
other) in zone
Presence of Repair Businesses in
zone
Presence of Restaurants in zone
Presence of Retail Businesses
(books, clothing, department and
thrift stores, pharmacies,
electronics, home improvement,
office supplies, ancillary, other) in
zone
Standardized
Coefficients
B
Std.
Error
Beta**
Sig.
.017
.004
.258
.000
3.594
.719
.269
.000
3.794
.808
.261
.000
5.451
.905
.328
.000
4.300
.600
.331
.000
3.999
.661
.327
.000
.037
.009
.241
.000
.071
.010
.376
.000
Relationship between the comprehensiveness of municipal
ordinances and the presence of daily use activities in the
mixed use zones was significant.
The more comprehensive and stringent the legal scores for
specific use categories, the greater the presence of these
uses in the MUZs.
Analysis of legal zoning data is a feasible method for
predicting neighborhood walkability. Comparative, crosssectional research designs can complement case study and
archival approaches.
This study raised the question: Under what conditions can
the laws make a difference in offering walkable
neighborhoods? City planners cited the economy as a major
limiting factor for the development of new mixed use zones,
resulting in numerous undeveloped or partially developed
zones.
LIMITATIONS
The existence of a walkable neighborhood or zone is a
separate question from whether people avail them of
opportunities for physical activity. Further research is
needed to determine the extent to which opportunity and its
use intersect.
Effective dates of ordinances are not consistently available.
Some variation among results may be attributable to time
between effective dates of original ordinances, effective
dates of amendments to original ordinances, and/or timing of
zone development.
Despite legal designations of mixed used zones, some zones
identified on city zoning maps were partially or completely
undeveloped at the time of data collection.
Some cities are in transition from land use-based zoning
codes to form-based zoning. Form-based codes emphasize
form and scale over individual use categories, and therefore
score lower using this methodology.
Regional differences may also affect the results. Cities in
more rural areas tend to favor horizontal over vertical mixed
use development. This leads to codes that emphasize single
family housing over multiple-unit or other high density
residential uses.
ACKNOWLEDGEMENT
3.137
.499
.314
.000
This research was funded by the Robert Wood Johnson Foundation’s
Public Health Law Research Program.
Walkability Coding
* Backwards stepwise regression used to determine best overall model for combined data
(see previous table). The independent variables that emerged from that model [Area of
Zone in Square Kilometers; Median Household Income; Race/Ethnicity: Black only;
Race/Ethnicity: White only; Race/Ethnicity: Hispanic/Latino/a of any race; Race/Ethnicity:
American Indian/Alaskan only; Race/Ethnicity: Pacific Islander /Hawaiian only; High School
graduates (% age 25+); Graduate/Professional Degree (% age 25+); Age: 19 and under;
Age: 35 to 59; Age: 60 and over] included in each of the equations pertaining to each
walkability subset.
** The beta weights are included for ease of interpretation within each of these categories,
not for comparison across-equations.
Sue Thomas, PhD, [email protected], Carol Cannon, MA, [email protected],
Ryan Treffers, JD, [email protected]
Pacific Institute for Research and Evaluation, PIRE-Santa Cruz Office,
P.O. Box 7042, Santa Cruz, CA 95061, 831-621-7937