Planning Portfolio - Luis Quintanilla

LUIS QUINTANILLA SUSTAINABLE PLANNING

LUIS QUINTANILLA 24 Grove Street #5, Arlington, MA 02476 Luis.Quintanilla@gmail.com I 857.316.8645

TABLE OF CONTENTS 1. General information

5

2. Transportation investment plan for the Commonwealth of Massachusetts

6

3. Urban placemaking in Jamaica Plain and Roxbury

10

4. Household income inequality: Analysis of Suffolk County, MA

14

5. Maximizing transit oriented development along MBTA’s Fitchburg commuter rail line

20

6. Next generation mobility challenge: The Smart Cane Program

24

7. Contact

29

3

Luis Quintanilla Master of City Planning

AWARDS / AFFILIATIONS May 2017

Boston University Award for Excellence in Graduate Study Master of City Planning

2016 – 2017

Boston University Urban Planning Association – President

2016 – 2017

Member of the American Planning Association

Boston University 2017

Master of Business Administration Monterrey Institute of Technology 2015

B.S. Industrial and Systems Engineering Monterrey Institute of Technology 2006

Nov 2017

Member of winning team in IXL Summer Innovation Olympics

Feb 2017

Member of winning team in the Toyota Next Generation Mobility Challenge at Boston University

2015 – 2016

WORK EXPERIENCE 2016 – Today Lincoln Institute of Land Policy – Cambridge, MA Junior Fellow I International & Institute-wide Initiatives

2015 – 2016

Nemak – Monterrey, MEX Program Manager I New Products Development

2008 – 2010

Vitro – Monterrey, MEX Materials Planner I Supply Chain & Logistics

2005 – 2007

2006 & 2015 Graduated with Honors B.S. Industrial and Systems Engineering, and M.B.A. 2006 – 2008

Member of the Institute of Industrial Engineers

Boston University – Boston, MA Graduate Assistant I City Planning and Urban Affairs Program

2011 – 2015

Graduate Assistantship at Boston University

Prolec GE – Monterrey, MEX Engineer I Operations

SKILLS Analysis:

Quantitative + Qualitative Analysis, Problem-solving methods

Tech:

ArcGIS, SPSS, SketchUp, AutoCAD, MS Project, Qualtrics

Engineering:

Lean Manufacturing, APQP, PPAP, PFMEA

Languages:

Spanish (native), English (proficient), German (basic knowledge) 5

TRANSPORTATION INVESTMENT PLAN for the Commonwealth of Massachusetts

2016

Strategies for a robust, efficient, well-functioning, interconnected and flexible transportation system THE

BOSTON AND SURROUNDING CITIES AND TOWNS IS CONNECTED by a variety of roads, biking paths, and public transit services, including rapid transit, commuter rail, bus routes, and ferry services. Nonetheless, as moving away from the central business district, several cities and towns become less served by public transit and less equipped with biking infrastructure, especially those outside of the Route 128 corridor. For instance, places like Peabody in the north, Marlborough in the west, or Plymouth in the south are populous cities where public transit options are scarce or null. This situation forces citizens living or working in these areas to use the automobile as their main, and perhaps only, mode of transportation. As a result, traffic congestion exacerbates in the whole metro area.

URBAN CORE COMPRISED BY THE CITY OF

Figure 1.

Population Density and transportation networks across Massachusetts

PROJECT FEATURES: Quantitative Research ArcGIS Spatial Analysis

Financial Analysis Public Policy Discussion Map produced by Luis Quintanilla Leonard P. Zakim Bunker Hill Memorial Bridge. Boston, MA. Photograph by Luis Quintanilla

TRANSPORTATION INVESTMENT PLAN FOR THE COMMONWEALTH OF MASSACHUSETTS

Goal 2. Diminish the system’s impact to the natural environment  Policy 3. Implement a gas taxation scheme  Policy 4. Prioritize the implementation of charging stations for electric vehicles Goal 3. Mitigate congestion  Policy 5. Implement a congestion charge scheme  Policy 6. Adopt a carpooling incentive program Goal 4. Maintain and modernize the system’s current infrastructure  Policy 7. Keep a comprehensive maintenance program for public transit  Policy 8. Adopt a revamping program for roads and bridges

Primary investments and revenue sources

Figure 2.

Revenue Collection Plan New Policy Charges (USD)

2018

2020

2021

2022

$

0.05

$

0.05

$

0.05

$

0.05

$

0.05

Congestion charge (USD)

$

0.65

$

0.66

$

0.67

$

0.68

$

0.69

Policy

Concept Estimated Gas consumption change per year due to new policies (%) Estimated Gas consumption by year in Massachusetts (gallons)

2018

2019

2020

2021

2022

-2%

-2%

-2%

-2%

-2%

3,136,000,000

3,073,280,000

3,011,814,400

2,951,578,112

2,892,546,550

Inflation rate (%)

2.3%

1.3%

1.7%

1.8%

1.8%

Gas Tax additional charge (US Dollars)

0.05

0.05

0.05

0.05

Gas Tax Gross Estimated Revenue (USD Millions)

$ 156,800,000

Estimated Traffic Volume change per year due to new policies (%) Estimated Traffic Volume in entire Central Artery Tunnel per weekday

$ 155,661,632

$ 155,141,722

$ 154,805,995

-1%

-1%

-1%

-1%

-1%

530,640

525,334

520,080

514,879

509,731

60%

60%

60%

60%

60%

Weekdays per year (48 weeks, Monday to Friday)

240

240

240

240

240

76,412,160

75,648,038

74,891,558

74,142,642

73,401,216

Estimated Traffic Volume in Cong. Charge Zone on peak hours per year Estimated Carpooling change due to new policies (%) Estimated Carpool Traffic Volume per year (Cong. Charge exemptions) Estimated Traffic Volume to be charged per year

9.5%

10.0%

10.5%

11.0%

11.5%

7,259,155

7,564,804

7,863,614

8,155,691

8,441,140

69,153,005

68,083,235

67,027,944

65,986,952

64,960,076

Inflation rate (%)

2.3%

1.3%

1.7%

1.8%

1.8%

Congestion charge (USD)

0.65

0.66

0.67

0.68

Congestion Charge Gross Estimated Revenue (USD Millions)

$ 44,949,453

$ 44,829,406

2018

$ 44,884,837

2019

$ 44,991,960

2020

Total

0.05 $ 154,440,653

Estimated Traffic Volume on peak hours 7:00 - 18:00 (%)

Summary of Estimated Revenues (Millions USD)

The main investment areas lay in bus services, bike infrastructure, information technology systems, EV charging stations, and maintenance programs. As far as sources of revenue is concerned, two strategies would be the key drivers to raise an objective of $1 billion dollars in a period of 5 years: 1) an additional gas tax of 5 cents per gallon, and 2) a congestion charge of 65 cents in all main accesses to the Central Artery tunnel in downtown Boston, the most auto-congested zone of the Commonwealth with a traffic volume of more than 530,000 vehicles per weekday.

2019

Gas Tax additional charge (USD)

Gas Tax

Goal 1. Improve the current system’s connectivity  Policy 1. Prioritize the expansion of bus services and biking infrastructure  Policy 2. Prioritize the adoption of new information technology systems (ITS)

A gas tax scheme and a congestion charge policy would discourage extensive automobile use, but it is equally important to offer alternatives travel modes to citizens.

These initiatives are consistent and aligned with the goals of increasing the region’s connectivity, reducing congestion, and lessening environmental impact.

Congestion Charge

The proposed investment plan seeks to stimulate economic development and enhance quality of life across the Commonwealth of Massachusetts through a set of four goals and eight policies:

$ 776,850,003

0.69 $ 45,089,057

2021

$ 224,744,713

2022

Total

Gas Tax additional Revenue (USD Millions)

$

156.8

$

155.7

$

155.1

$

154.8

$

154.4

$

Congestion Charge Revenue (USD Millions)

$

44.9

$

44.8

$

44.9

$

45.0

$

45.1

$

Grand Total (USD Millions)

$

201.7

$

200.5

$

200.0

$

199.8

$

199.5

$

776.9 224.7

1,001.6

A gas tax scheme and a congestion charge policy would discourage extensive automobile use.

7

TRANSPORTATION INVESTMENT PLAN FOR THE COMMONWEALTH OF MASSACHUSETTS Figure 4.

Regions in the Greater Boston area with low connectivity to public transit (at least 2 miles away)

Where and why should we invest?

The most efficient and sustainable way to connect suburban towns in Eastern Massachusetts to the public transportation network is by expanding the MBTA’s bus service. Buses are more flexible, easier to implement, less contaminating, and require less capital investment than trains or rapid transit infrastructure. In the MBTA’s 2013-2017 Capital Investment Plan only 11.3% of funds were allocated to enhance the bus metropolitan bus service, even though its ridership share was estimated at 29.7% in 2010 (MassDOT, MBTA, 2012).

Rapid Transit Figure 3.

MBTA Capital Investment Plan 2013-2017 per transportation mode compared to ridership

Capital Investment 2013-2017

Ridership FY2010

Commuter rail Bus service

11.3% 29.7%

Rapid Transit Commuter rail Bus service

26.1%

62.3%

60.0% 10.0% Map produced by Luis Quintanilla

Regions with low connectivity to the public transit network

The incorporation of 440 new buses to the current MBTA’s fleet and 4 new bus facilities is recommended as part of the new investment plan. The new bus system would serve regions that currently have low connectivity to the public transit system (Figure 4), and would have the potential to add 35,200 new passengers to the daily ridership cipher. This proposed investment aims to connect auto-oriented towns such as Framingham, Marlborough, or Peabody to the existing public transit system and, consequently, to job centers like Boston, Worcester, Lowell or Lawrence. The proposed bus service expansion would cost $351 million.

8

T

2-mile radial distance from train stations

More than 60,000 persons live and work within the regions with low connectivity to public transit that are shown in Figure 4 (U.S. Census Bureau, 2014). Bike paths can offer a healthy and less polluting commuting option for these citizens. In that regard, the investment proposal is to allocate $35 million for the construction of 350 miles of new bike lanes and 1,500 bike racks in these regions. When fully implemented, it is estimated that the new bus services and the new biking infrastructure will reduce the automobile’s travel mode share by 8% in the Greater Boston Area.

TRANSPORTATION INVESTMENT PLAN FOR THE COMMONWEALTH OF MASSACHUSETTS Figure 5.

Five-year Transportation Investment Plan for Massachusetts

Program

Bus System Expansion

Buses (40 ft. diesel)

$

Bus facility (Southampton St.) Full Time

$ 48,000,000

Maintenance and Modernization Programs

Quantity

361,204

$92,000

2018

2019

2020

2021

2022

Total Estimated Cost (MUSD)

440

$

-

$

39.7

$

39.7

$

39.7

$

39.7

$

159

4

$

-

$

48.0

$

48.0

$

48.0

$

48.0

$

192

$

-

$

87.7

$

87.7

$

87.7

$

87.7

$

351

Total Bike path per mile, 10 foot wide:

Bicycle Infrastructure Expansion

Unit Cost (USD)

Concept

$

92,000

350

$

-

$

8.1

$

8.1

$

8.1

$

8.1

$

32

Bike lane stripe per mile, four inch line

$

3,168

350

$

-

$

0.3

$

0.3

$

0.3

$

0.3

$

1

Bike locker (for 2 bikes)

$

930

1000

$

-

$

0.2

$

0.2

$

0.2

$

0.2

$

1

Bike rack (10-12 bikes)

$

730

500

$

-

$

0.1

$

0.1

$

0.1

$

0.1

$

0

Total

$

-

$

8.7

$

8.7

$

8.7

$

8.7

$

35

Highway / Bridges

$

60.4

$

60.4

$

60.4

$

60.4

$

60.4

$

302

Revenue Vehicles (Commuter rail, rapid transit, buses)

$

36.4

$

36.4

$

36.4

$

36.4

$

36.4

$

182

Public Transit Stations

$

7.6

$

7.6

$

7.6

$

7.6

$

7.6

$

38

$

7.6

$

7.6

$

7.6

$

7.6

$

7.6

$

38

$

7.5

$

7.5

$

7.5

$

7.5

$

7.5

$

38

Maintenance Facilities (rail car houses and bus garages)

$

2.6

$

2.6

$

2.6

$

2.6

$

2.6

$

13

Technological and informational infrastructure / ITS

$

1.2

$

1.2

$

1.2

$

1.2

$

1.2

$

6

Total

$

123

$

123

$

123

$

123

$

123

$

616

$

123

$

220

$

220

$

220

$

220

$

1,002

Public Transit Bridges

$

5,000,000

Electric vehicles Parking and Curbside Charging Stations

$

7,500.00

Grand Total

Another fundamental component of this investment plan relates to preserving, and modernizing the existing regional transportation system, particularly highways and bridges, public transit stations, electric vehicles charging stations, maintenance facilities, and technological infrastructure.

Additional maintenance on rail tracks, boarding stations, and power upgrades are necessary as well. Moreover, aligned with the goal of reducing environmental impact, parking and curbside charging stations are contemplated to promote the

5000

massive use of electric vehicles. To properly address all these latent needs, an investment of $616 million dollars is herein proposed. The cost estimation and a year-by-year plan for allocation of resources is shown in Figure 5.

With this investment plan, taxpayers and car users living in suburbs are likely to be provided with additional travel mode choices: bike lanes and buses. Current and following generations will benefit from improved connectivity and mobility, especially those living in urban areas.

It is estimated that the new bus services and the new biking infrastructure will reduce the automobile’s travel mode share by 8% in the Greater Boston Area.

Information sources MassDOT / MBTA (2012). Capital Investment Plan 2013-2017. Boston, Massachusetts.

MassDOT (2016) Our Customers: System usage. Retrieved from: http://www.massdot.state.ma.us/planning/Main/MapsDataandReports/Data/Transporta tionFacts.asp U.S. Census Bureau (2013). Analysis of the state of Massachusetts. Census Explorer Commuting Edition. Retrieved from: http://www.census.gov/censusexplorer/censusexplorer-commuting.html U.S. Census Bureau (2014). Analysis of county subdivisions in Eastern Massachusetts. OnTheMap. Retrieved from: http://onthemap.ces.census.gov/ Stewart-Wilson, Graeme; Millar, Caitlin; McLaren, Christine; Millar, Erin; Rockafella, Josli; Poulos George (2015). What is the full cost of your commute? Moving Forward Journal. Vancouver, British Columbia.

Renski, Henry; Strate, Susan (2015). Long-term population projections for Massachusetts regions and municipalities. University of Massachusetts Donahue Institute.

9

URBAN PLACEMAKING Jamaica Plan and Roxbury, Boston, MA

Holistic visions for sustainable placemaking, streetscape functionality and opportunities for beautification Figure 1.

Median Household Income and boundaries of the JP/ROX Plan

PROJECT FEATURES: Qualitative Research ArcGIS Spatial Analysis Urban Design Public Policy Discussion

2016

“Keepin’ it local” mural at Washington St. and Glen Rd. Boston, MA. Photograph by Luis Quintanilla

STRONG COMMUNITY INVOLVEMENT IS A KEY INGREDIENT IN THE PLANNING PROCESS to improve the overall livability of a neighborhood, and such has been the case with the JP/ROX Plan: a set of guidelines to enhance open space, mobility, and business development along the transportation corridor between the Bostonian neighborhoods of Jamaica Plan and Roxbury. Starting in July 2015, the Boston Planning and Development Agency conducted several public meetings to assess the community’s vision for the area bounded by Washington St., Columbus Ave., and Amory St. (Figure 1).

Perceived already as a rapidly gentrifying area, marked socioeconomic differences across block groups (observed in Figure 1) urge for policies seeking to preserve the character of the neighborhood, make sense of place, foster human connections, and unify the community.

Median Household Income (USD)

JP/ROX Plan Boundary

Data source for MHI: American Community Survey (2014). US Census Bureau Map produced by Luis Quintanilla

URBAN PLACEMAKING IN JAMAICA PLAN AND ROXBURY, BOSTON, MA

What does the community want to see?

Figure 3.

Holistic vision for Sustainable Placemaking: physical and social aspects

Through an extensive planning process of two and half years, the JP/ROX Plan identifies a wide range of elements for the neighborhood that are envisioned by the community, including inclusionary zoning ordinances, mixed use development, maximum building height regulations, late night public transit services, support to locally-owned businesses, among many others.

Within the scope of community placemaking, the JP/ROX Plan recognizes the need to address two key community aspirations: 1. Preserve ethnic and cultural diversity, and multi-generational community connections  More outdoor events for people from all ages  Back allies as pedestrian zones and retail clusters  Artist studios and housing  Integration of marginalized groups (i.e. Latinos, elders) into new urban development projects 2. Preserve and enhance current green open space  Parkways and setbacks, places for kids, youth, adults, elderly to congregate outdoors  Eliminate requirement of parking to lower commercial space cost  Larger parks on Washington St.  Community garden spaces

Short Term Proposals – Phase I

The ultimate objective is to preserve cultural diversity and foster human connections.

Figure 2.

Community aspirations for a vacant local at the corner of Washington St. and Glen Rd., Boston, MA

With this preamble, three urban design intervention proposals (two short-term, one long-term) are presented based on the holistic vision of sustainable placemaking illustrated in Figure 3 above.

1

SW Corridor Park: Walking path and amenities

2

Brookside Avenue: Pedestrianization

Long Term Proposal – Phase II

3

Green Street: Pedestrianization Desirable for Pedestrianization:

Photograph by Luis Quintanilla

•

McBride Street

•

Cornwall Street

•

Boylston Street

Figure 4.

Proposed interventions map

11

URBAN PLACEMAKING IN JAMAICA PLAN AND ROXBURY, BOSTON, MA

Proposal

More places to sit, walk, bike, eat, and play The main features in all three intervention proposals are more places to sit, pedestrian and bike paths, tree plantings that provide shade, and new amenities such as mini plazas, art and food locals. These simple, inexpensive public assets have the potential to make urban spaces more inviting and attractive for people’s use and enjoyment.

Short-term proposals 1 and 2 are considered low-hanging fruits since there would be no major disruption to the current car traffic flow. Proposed interventions at the Southwest Corridor Park are all within the green space area, while modifications in the underutilized Brookside Avenue (between Green St. and Williams St.) would have no major impacts on car traffic.

On the other hand, proposal 3 implies impacting a more transited road by closing Green Street to automobiles. This bold yet feasible idea would turn Green Street into a pedestrian pathway that connects the Jamaica Plain and Roxbury neighborhoods. If successful, further pedestrianization projects like Green Street’s proposal would be recommended for McBride, Cornwall, and Boylston Streets. Implementing all these proposals would result in a continuous parkway system (Figure 5), capable of congregating neighbors from all ages, cultural backgrounds, and socioeconomic statuses.

Proposed

Current State

Amenities Shade

1

Sitting places

Path

2

Food Greenery Sitting places

Walk

Figure 5.

Bird’s eye view of urban design intervention proposals for the JP/ROX area Art

Shade

3

N

12

Sitting places Bike paths

Photographs and editing by Luis Quintanilla

URBAN PLACEMAKING IN JAMAICA PLAN AND ROXBURY, BOSTON, MA

Summary of policy recommendations for placemaking in the JP/ROX area 1.

2.

Figure 6.

Underused industrial land in Brookside Ave. and Green St. could be turned into parks and pedestrian corridors

Encourage parks and open space improvements: SW Corridor Park and William F. Flaherty Park 

Pedestrian paths and places to sit



Trees and greeneries



Places for live performance



Restrooms



Street lightning

Allow the pedestrianization of Brookside Avenue. Learn and move forward with Green Street 

Pedestrian and bike paths



Places to sit



Mixed use development in Green St., as currently planned



Vendors, food trucks, and cafes



Reduced parking requirements for new development on Green St.

3. Also recommended for pedestrianization: McBride Street, Cornwall Street, and Boylston Street 

This would connect the neighborhood transversely with the SW Corridor Park, from Washington Avenue to both Amory Street and Lamartine Street Photograph by Luis Quintanilla

Implementing all these proposals would result in a continuous parkway system, capable of congregating neighbors from all ages, cultural backgrounds, and socioeconomic statuses.

Information sources Boston Planning and Development Agency (2016). PLAN: JP/Rox. Retrieved from: www.bostonplans.org/planning/planning-initiatives/plan-jp-rox American Community Survey (2014). Median Household Income in the past 12 months – All County Subdivisions within Massachusetts. 2010-2014 American Community Survey 5-Year Estimates. U.S. Census Bureau database Lynch, Kevin (1960). The Image of the City

13

PROJECT FEATURES: Quantitative Research

Data-driven policy recommendations to mitigate the income gap in Massachusetts’ fourth-most populous shire

Descriptive Statistics Correlation Analysis Multiple Linear Regression Public Policy Discussion

2016 HOUSEHOLD INCOME INEQUALITY Analysis of Suffolk County, MA

SINCE 2010, ECONOMIC INEQUALITY HAS BEEN GROWING IN THE UNITED STATES LIKE IN NO OTHER DEVELOPED COUNTRY. At the beginning of the decade, the gap between the 90th percentile and the 10th percentile of the household income range was represented by a ratio of 6.1, the highest among developed nations (Dreier, 2014). The effects of economic disparity are reflected in a distressed and less functional society. With such challenging preamble in mind, this study investigates if socioeconomic factors, such as educational attainment, race, and family composition, affect the median household income in different census tracts of Suffolk County, Massachusetts. Which social variables have a significant impact on median income?

How do variables interact among each other? Is there a statistically significant relationship between them? These are research questions of interest that the present study responds in order to further public policy supported with statistical analyses.

Based on literature in the subject, the research hypothesis is that median household income (MHI) per census tract in Suffolk County has a significant positive relationship with the level of educational attainment, the percentage of white population, the percentage of male population, and the percentage of households whose primary language is English. Similarly, it is initially presumed that MHI per census tract in the county may have a significant negative relationship with the percentage of African-American and other minorities’ populations, the percentage of households headed by a single female, and the percentage of population with disabilities.

Panoramic view of the South End, South Boston, and Dorchester neighborhoods in Suffolk County, MA Photograph by Luis Quintanilla

ANALYSIS OF HOUSEHOLD INCOME INEQUALITY IN SUFFOLK COUNTY, MA

Literature review and relevant facts about income inequality in the U.S. Household income is defined as the total sum of income for members of the household over age 15 (Hartman, 2014). Educational attainment, gender, language and race are highly associated with the level of household income in the United States according to the literature:

Research question, hypotheses, and study variables Research question

 Bachelor's degrees earn $2.27 million over their lifetime. Masters, doctoral, and professional degrees earn $2.67 million, $3.25 million, and $3.65 million respectively (Carnevale, 2014).  American women make 82 cents to every dollar a man makes, and 80 cents to every dollar made by a white man (Ashton, 2014).

Hypotheses

 In 2010 the household income gap between the 90th percentile and the 10th percentile in the U.S. is represented by a ratio of 6.1 (Dreier, 2014).

The gap between the 90th percentile and the 10th percentile of the household income range was represented by a ratio of 6.1 in the United States.

1) There is a significant, positive relationship between MHI and:  Graduate / Professional educational attainment (%)  White population (%)  Male population (%)  Households whose primary language is English (%).

2) There is a significant, negative relationship between MHI and:  African-American population (%)  Other minorities population (%)  Female-headed households (%)  Population with disabilities (%)

 Regardless of gender, African-Americans and Hispanics earn less income than white and Asian people. (Ashton, 2014)  Educational attainment among African-Americans and Hispanics is lower in comparison to whites (Ashton, 2014).

What affects median household income in Suffolk County’s census tracts?

Dependent Variable

Independent Variables

Median Household Income (USD)           

Professional degree or higher - Population over 25 (%) White population (%) Black or African American population (%) Asian population (%) Other race population (%) Married couple households (%) Female householder, no husband present (%) Male householder, no wife present (%) Male population - Pop 18 and over (%) Disability of non-institutionalized population (%) English language spoken at home (%)

15

ANALYSIS OF HOUSEHOLD INCOME INEQUALITY IN SUFFOLK COUNTY, MA

Data and Methodology Data

Methods

 American Community Survey 2014 (ACS 5-Year Estimates 2014) from the U.S. Census Bureau: available data for Suffolk County census tracts  Units of observation and units of analysis: Census tracts from Suffolk County  Univariate Descriptive Analysis, sample size: 193 census tracts  Bivariate Analysis: Correlation between dependent and independent variables  Multiple Linear Regression Model

Findings From the univariate analysis, the MHI data has a left-skewed distribution (See left histogram in Figure 1). This is also detected by comparing the independent variable’s mean ($58,401) and median ($53,065) from the summary statistics in Table 1. To correct such deviation the log10 of the median income values is computed, centering the data’s distribution for the dependent variable, as shown by the right-hand side chart in Figure 1.

For the bivariate analysis, a cross calculation of Pearson’s correlation coefficients (r) among all the variables of study is computed, using the logged values of MHI as dependent variable. As demonstrated in Table 2, all independent variables have a significant relationship with the dependent variable at the 0.01 alpha level (99% confidence level). As per the Pearson’s correlation coefficients signs, it is confirmed that there is a positive relationship between MHI and education, white population, married-couple households, male population, and English spoken at home. Similarly, there is a negative relationship between MHI and African American, Asian and other race populations, female-headed households, male-headed households, and population with disabilities.

16

Table 1.

Data summary – Descriptive statistics Variable Median household income Log - Median household income Graduate or professional degree (%) White (%) Black or African American (%) Asian (%) Some other race (%) Family Married-couple (%) Family Female householder, no husband present (%) Family Male householder, no wife present (%) Male - Pop 18 and over - (%) Disability of noninstitutionalized population (%) English language spoken at home (%)

Mean

Median

Std. Dev.

Min

Max

58,401.68 4.71 19.14 59.42 21.36 7.81 6.60 27.38 17.91 4.81 47.62 11.83 62.96

53,065.00 4.72 13.60 66.50 8.20 4.30 4.00 27.30 14.70 4.00 47.20 11.30 64.50

29,386.09 0.23 15.19 29.19 26.01 9.87 7.78 11.13 14.20 4.03 6.04 5.42 18.61

12,813.00 4.11 0.90 4.50 0.00 0.00 0.00 6.70 0.00 0.00 32.90 0.00 12.90

180,417.00 5.26 72.50 100.00 92.90 73.90 38.40 62.80 84.80 21.20 68.70 30.10 100.00

Figure 1.

Histograms for Suffolk County’s Median Household Income in USD (left) and the log10 values (right)

ANALYSIS OF HOUSEHOLD INCOME INEQUALITY IN SUFFOLK COUNTY, MA

The independent variables that show a weak relationship (0 < r < 0.30) with the dependent variable are Asian population, male-headed households, and male population; those showing a moderate relationship (0.30 < r < 0.60) are education, African-American population, other race populations, married-couple households, femaleheaded households, population with disability, and English language spoken at home; while the only independent variable with a strong relationship (r > 0.60) with MHI is the percentage of white population.

Table 2. Pearson r correlation coefficients for the Log10 of Median Household Income

Table 3. Initial Multiple Regression Model for the Log10 of Median Household Income in Suffolk County, MA Unstandardized

Variable

Log Median Sig. household (2 Tailed) income

Standardized

B

Std. Error

Beta

(Constant)

3.968

.132

Graduate or professional degree (%)

.005

.001

.313

T

Sig.

30.023

.000

5.100

.000

Graduate or professional degree (%)

.468

**

.000

White (%)

.001

.001

.133

1.839

.068

White (%)

.659 **

.000

Asian (%)

-.006

.001

-.236

-4.983

.000

Black or African American (%)

-.464 **

.000

Some other race (%)

.000

.001

-.008

-.162

.871

Asian (%)

-.253 **

.000

Family Married-couple (%)

.009

.001

.451

11.814

.000

Some other race (%)

-.463 **

.000

Family Female householder, (%)

-.001

.001

-.045

-.582

.561

Family Married-couple (%)

.585 **

.000

Family Male householder, (%)

.008

.003

.147

2.862

.005

Family Female householder (%)

-.542 **

.000

Male - Pop 18 and over - (%)

.005

.001

.128

3.318

.001

Family Male householder (%)

-.222 **

.002

-.006

.002

-.145

-3.001

.003

Male - Pop 18 and over - (%)

.239 **

Disability of population (%)

.001

Disability of noninst. population (%)

-.535 **

.000

English language spoken at home (%)

.003

.001

.237

4.481

.000

English language spoken at home (%)

.520 **

.000

R2 = 0.785, F = 66.37

p > 0.05

**level Significant at the 0.01 level (2-tailed) **. Correlation is significant at the 0.01 (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

All the independent variables proved to have a significant relationship with median household income.

Since all the independent variables proved to have a significant relationship with median household income, all of them were included in the multiple linear regression analysis, except for the percentage of African-American population due to its multicollinearity effect with the percentage of white population variable.

In the initial multiple regression exercise the ANOVA test shows that the model would be good fit for the data (F = 66.37, sig. 0.000). However, beta coefficients resulted not significant (p > 0.05) for three independent variables: other race population, female headed-households, and white population, as highlighted with blue rectangles in Table 3.

Table 4. Refined Multiple Regression Model for the Log10 of Median Household Income in Suffolk County, MA Unstandardized

Standardized

B

Std. Error

Beta

t

Sig.

(Constant)

3.916

.098

Graduate or professional degree (%)

.005

.001

.326

40.113

.000

5.714

.000

White (%)

.001

.000

.165

3.273

.001

Asian (%)

-.005

.001

Family Married-couple (%)

.009

.001

-.221

-5.524

.000

.452

11.928

.000

Family Male householder (%)

.009

Male - Pop 18 and over - (%)

.005

.003

.154

3.112

.002

.001

.133

3.526

.001

Disability of population (%)

-.006

.002

-.146

-3.053

.003

English language spoken at home (%)

.003

.001

.244

5.053

.000

R2 = 0.784, F = 83.65

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ANALYSIS OF HOUSEHOLD INCOME INEQUALITY IN SUFFOLK COUNTY, MA

Following the “backwards variable selection” approach, the regression model was refined by removing the variable with the highest p-value in every iteration until all beta regression coefficients became significant. This resulted in the elimination of two independent variables in the following sequence: 1) Other race, and 2) Female-headed households. The resulting refined model’s R2 coefficient value is 0.784, which means that 78.4% of the observed variation in Suffolk County’s median household income is explained by the combined linear influence of the independent variables of study. When testing the statistical significance of the regression coefficients (H0: βn = 0) for the refined model, all p-values are less than the alpha level of 0.01, hence all the model’s coefficients are statistically significant at a 99% confidence level. The refined model’s overall fit for the data is also good (F=83.65, sig. 0.000). The model’s equation, able to predict 78.4% of the variation of median household income in Suffolk County’s census tracts, can be expressed as:

Table 5.

Summary of results from the bivariate correlation and the multiple linear regression analyses

Log10 I = 3.916 + .005g + .001w – .005a + .009c + .009h + .005m – .006d + .003e + ɛ Significant at the 0.01 alpha level Not significant at the 0.01 alpha level

where I is median household income, g is the percentage of population with graduate or professional degree, w is the percentage of white population, a is the percentage of Asian population, c is the percentage of married couple households, h is the percentage of family households headed by a single male, m is the percentage of male population, d is the percentage of population with some disability, e is the percentage of households where English is spoken at home, and ɛ is the residual standard error. It is important to note that, since the dependent variable is logged and all independent variables are measured in percentages, a 1-unit percent increase in an independent variable will impact the dependent variable by 100 times the amount of its coefficient. For example: the coefficient of the variable g is 0.005, so for every percent unit increase in the population with a graduate / professional degree the MHI in that census tract would increase by 100 x 0.005 = 0.5%. The same logic applies for the rest of the independent variables.

Moreover, based on the standardized coefficients’ individual magnitudes, the most important explanatory variable is the percentage of married-couple households (β=0.452), followed by the percentage of graduate or professional degree (β=0.326), the percentage of family households 18

The more family-integrated and the more educated the population of a census tract is, the higher the median household income will be in that census tract. were English is spoken at home (β=0.244), and also the percentage of white population (β=0.165). All this suggests that the more family-integrated and the more educated the population of a census tract is, the higher the MHI will be in that census tract. The ethnicity factor suggests that the higher percentage of white population in the census tract, the higher the MHI will be. In other words: the greater minority population percentage in a census tract, the lower the MHI in such census tract. The analysis also suggests that there is a significant negative relationship between the population with disability and MHI.

ANALYSIS OF HOUSEHOLD INCOME INEQUALITY IN SUFFOLK COUNTY, MA

Limitations of the analysis Potential Limitations

 Accuracy of reported income on the American Community Survey  Access to information from individuals with very high income

Public policy implications Policy implications from the research include utilizing resources to establish or increase the following programs for people specifically in census tracts with lower household income:

 Programs to maintain and promote family cohesion. Focus on reaching families for marriage counseling, child care, and family recreation.

Key takeaways The three most important variables positively affecting median household incomes in Suffolk County are: 1. Married couple households 2. Graduate or professional degree 3. English language spoken at home The two most important variables negatively affecting median household income in Suffolk County are: 1. Population with some kind of physical disability 2. Asian population Photograph by Luis Quintanilla

 Programs for awareness of secondary / graduate / professional degrees. Focus on reaching high school students to demonstrate employment opportunities available to those with graduate degrees and the path to obtaining graduate and professional degrees.  Programs to increase English as a Second Language (ESL) for adult populations. Focus on reaching working adults with courses and curriculum to fit their schedules and connecting English language learners to mentors who can help them associate with greater community, so that they are not isolated in a non-English speaking neighborhood/environment.  Programs for employment of disabled (Social inclusion). Focus on connecting disabled with organizations and businesses that employ disabled as well as incentivizing businesses to employ the disabled. Promote involvement, inclusion and purpose for disabled individuals.

Information sources American Community Survey (2014). 2010-2014 American Community Survey 5-Year Estimates. Available from U.S. Census Bureau database.

Ashton, D. (2014). Does Race or Gender Matter More to Your Paycheck? Harvard Business Review. Retrieved in March 31, 2016 from https://hbr.org/2014/06/does-race-or-gender-matter-more-to-your-paycheck Carnevale, A. P., Rose, S. J., & Cheah, B. (2014). The College Payoff. The College Payoff, 1-36. Retrieved in March 31, 2016 from: https://cew.georgetown.edu/wp-content/uploads/2014/11/collegepayoff-complete.pdf Dreier, Peter; Mollenkopf, John; Swanstrom, Todd (2014). Place Matters: Metropolitics for the Twenty-First Century. Third Edition. University Press of Kansas.

Hartman, D. (2014). What Is the Definition of Median Household Income? Zacks Finance, 1-4. Retrieved in March 31, 2016 from: http://finance.zacks.com/definition-median-household-income-9856.html

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MAXIMAZING TRANSIT ORIENTED DEVELOPMENT along MBTA’s Fitchburg Commuter Rail Line

2017

GIS mapping tools to identify sustainable development opportunities around transit nodes AS POPULATION IN CITIES CONTINUES TO GROW, THE CONCEPT OF MEGAREGIONS INTEGRATED BY CLUSTERS OF INTERCONNECTED URBAN CENTERS HAS BECOME MORE RELEVANT as a sustainable way to foster growth, economic development, and better quality of life. The eastern Massachusetts region counts with an extensive network of public transportation in its MBTA commuter rail system.

This infrastructure is without a question an extremely valuable asset for the region’s connectivity, offering opportunities for residential and commercial development suitable to the current housing demand. However, how optimal is land use around this valuable public transit nodes? How well utilized is the commuter rail infrastructure that is already in place?

The impetus behind this research is to identify opportunities to increase the region’s housing stock by disclosing potential zones that would be apt for transit oriented development (TOD).

By means of a spatial analysis of population density and land use patterns along the Fitchburg branch of the MBTA commuter rail system, the ultimate objective in this investigation is to detect possibilities for new housing and commercial development.

Figure 1.

Population density (2015) in eastern Massachusetts by block groups across MBTA Commuter Rail System

PROJECT FEATURES: Quantitative Research ArcGIS Spatial Analysis

Fitchburg Line

Land Use Analysis Public Policy Discussion

MBTA Commuter Rail. Boston, MA Photograph by Luis Quintanilla

Map produced by Luis Quintanilla

MAXIMIZING TRANSIT ORIENTED DEVELOPMENT ALONG FITCHBURG COMMUTER RAIL LINE

Research questions The scope of the study is centered on questions regarding population density, socioeconomic level, and land use. It is through spatial analysis and GIS maps that clarification is sought on the following interrogations with regards to Fitchburg commuter rail line:

Exhibit 1. Classification of land uses selected for the study (left) and customized color code (right) Business and amenities

• • •

Commercial Industrial Urban public / institutional

• High-density (less than ¼ acre lots) • Medium-density (¼ – ½ acre lots) • than Low-density (½ - 1 acre lots) High-density (less ¼ acre lots)

Residential

 How is population density in areas surrounding commuter rail stations?  What types of socioeconomic levels are found near commuter rail stations?  What types of land uses are more prominent around commuter rail stations?

Data and methodology Density is measured in population per square mile at the block group level (2015 ACS Survey), socioeconomic level is illustrated as the median household income at the county subdivision level (2015 ACS Survey), while a color code is used to depict existing land uses (2005 MassGIS data layers).

A demographic analysis performed with ArcMap shows choropleth maps for population density and median household income on all corresponding areas surrounding the commuter rail stations of the Fitchburg line.

A proximity analysis using ½-mile and 1-mile buffers from the commuter rail stations is performed to exhibit areas with potential for development. Land uses selected for the analysis are classified in three main categories: business and amenities, residential, and new development potential. A color code (Exhibit 1) is assigned to identify those land uses that would be relevant in a re-zoning discussion to promote TOD. Note: This customized color coded is adopted exclusively in this project to visually identify opportunity areas more easily.

• • Medium-density (¼ – ½ acre lots) New• development vacant land Low-density (½ •- 1 Open acre lots) potential • Transitional / Urban open

Findings From the spatial analysis on population density, it is observed that the Fitchburg line serves the least dense commuter rail corridor in the eastern Massachusetts region given that it counts with 19 boarding stations in total. This reinforces the importance of studying land use patterns that surround stations along this transit line. The population density analysis is done at the block group level (Figure 1) with the intention of being as precise as possible when recommending low-dense areas that would be suitable for TOD. Zooming into the commuter line pathway, almost all of its stations lie in areas where population density is less than 2,500 persons per square mile, a very low figure compared to places like Cambridge, Somerville, northern Brookline, and Boston. In that sense, the Fitchburg line potential to increase the region’s housing stock is relevant given market pressures currently experienced in Boston’s metro area due to its economic and population growth. Hence, this is a great opportunity for the eastern Massachusetts region to tap into its existing transportation infrastructure to expand the economic prosperity and wealth creation to those cities and towns situated along the Fitchburg line. Figure 2 shows that all the boarding stations in Fitchburg’s commuter rail line from Kendal Green up to North Leominster could be better utilized, as the areas they serve are low dense. The purple circle surrounding each train stop represents a half-mile radial distance from each stations.

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MAXIMIZING TRANSIT ORIENTED DEVELOPMENT ALONG FITCHBURG COMMUTER RAIL LINE Figure 2. Population density (2015) by block group along the Fitchburg commuter rail line

Fitchburg Line

Urban sprawl did not occur organically, but mainly due to codes and standards that impede a smart, compact, and sustainable growth.

Map produced by Luis Quintanilla

Figure 3. Median household income (2015) by county subdivision along the Fitchburg commuter rail line

Fitchburg Line

Map produced by Luis Quintanilla

22

Median household income (MHI) is shown in Figure 3 at the county subdivision level because municipal zoning regulations in the state of Massachusetts are established by local governments, hence it makes greater sense to gauge the political viability for zoning reforms and TOD at that scale. The MHI map reveals the wealthiest (green) and poorest (red) areas along the Fitchburg line corridor. This is relevant for the study because the wealthier the county subdivision is, the more politically unfeasible it might be to reform its zoning by-law. High-income residents are associated with greater political power, thus the permitting process to allow more compact, transit-oriented development could become more challenging. Within the line of study, it can be observed that the town of Weston has the wealthiest residents (MHI > $135,000), while Lincoln, Concord, Acton and Littleton fall within a second category ($110,000 < MHI < $135,000). The towns of Ayer, Shirley and Leominster are the least wealthy ($85,000 < MHI < $110,000).

As far as land use regulations is concerned, it can be perceived in Figure 4 how land used for residential purposes goes from high-density or multifamily (green) to medium-density (yellow) in the regionâ&#x20AC;&#x2122;s second ring, to more a scattered low-density (red) in the third ring. But why does residential land use has to vary this way? Why canâ&#x20AC;&#x2122;t there be a more efficient use of land overall?

Urban sprawl did not occur organically, but mainly due to codes and standards that impede a smart, compact, and sustainable growth (Duany, 2010). Marked differences can be observed from a transit oriented development standpoint, as the extensive high-density, mixed-use type of development (green) in the city of Waltham is depicted against the extremely low-density, residential development (red) that characterizes the three stations from the wealthy town of Weston. This is a clear example of the political challenges that the eastern region of the state of Massachusetts faces towards a more integral, sustainable growth based on mass transit.

MAXIMIZING TRANSIT ORIENTED DEVELOPMENT ALONG FITCHBURG COMMUTER RAIL LINE Figure 4. Land use (2005) along the Fitchburg commuter rail line.

2. Aim for urban infill development whenever feasible. Select areas declared as transitional or open (vacant) land. If there are no opportunities for revitalizing current buildings, target new development in vacant or transitional land within dense, urban settings that are unsuitable for agriculture or any type of natural vegetation. The best stations for this type of development are: West Concord and Littleton / Route 495.

Fitchburg Line

Map produced by Luis Quintanilla

Policy recommendations Within the scope of this analysis, and based on the land use patterns along the Fitchburg line’s transit nodes, three types of development are recommended in the following order:

1. Make urban revitalization in urban settings. Restore, retrofit or revitalize high and medium-density residential areas first. This includes refurbishing high and medium density housing structures, even low-density housing if this would help increase the number of people living near transit stations. The best stations in the Fitchburg branch for this type of development are: West Concord, Concord, Ayer, and Shirley.

3. Adopt a suburban retrofit approach. Target low-dense industrial or commercial areas first, leave low-density residential areas for last. When neither urban revitalization nor urban infill are possible, go for retrofitting commercial and residential land in low-dense areas. The idea is to get the most out of already taken land; for instance: turning a light-industry site or a big parking lot near a transit station into a mixed-use complex. The best stations for this type of development are: West Concord, Concord, Ayer, South Acton, Littleton / Route 495, and North Leominster.

Information sources Duany, Andres; Speck, Jeff; Lydon, Mike (2010). The Smart Growth Manual. McGraw-Hill.

American Community Survey (2015). Total Population – All block groups within Massachusetts. 2011-2015 American Community Survey 5-Year Estimates. Available from U.S. Census Bureau database American Community Survey (2015). Median Household Income in the past 12 months – All County Subdivisions within Massachusetts. 2011-2015 American Community Survey 5-Year Estimates. Available from U.S. Census Bureau database 2016 shapefiles for Massachusetts’ county subdivisions, and block groups. Retrieved from US Census Bureau TIGER / Line at: https://www.census.gov/cgi-bin/geo/shapefiles/index.php MBTA’s commuter rail (Active and Proposed) datalayer. Retrieved from MassGIS website at: http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-informationmassgis/datalayers/trains.html Land Use (2005) datalayers for Suffolk County, Middlesex County, and North Worcester County. Retrieved from MassGIS website at: http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-ofgeographic-information-massgis/datalayers/lus2005.html

23

NEXT GENERATION MOBILITY CHALLENGE The Smart Cane Program

Creative Thinking

Product Design New Products Development

HOW

CAN WE HELP SENIORS TO NAVIGATE SAFELY IN CITIES AND ENGAGE MORE ACTIVELY WITH THEIR

COMMUNITIES?

More specifically, how can we address the last-mile problem for them? These were prompt questions given at the 2017 Next Generation Mobility Challenge from Toyota and Net Impact, where teams of students from different universities and distinct academic backgrounds share ideas and develop proposals to solve the most vexing mobility issues of today. The winning team gets an internship in Toyota and the chance to incubate its proposal.

In this project, I was fortunate to work with a diverse group of students and designed The Smart Cane, an intelligent device that works with GPS, voice recognition technology, and dedicated short range communications (DSRC) to connect users to transportation networks and guide them throughout their journeys. Our main goal in this project is to enhance mobility and confidence for the elderly and individuals with physical limitations by connecting them to people with similar interests and destinations, making their travels secure and fun. The Smart Cane Program promotes autonomy, safety and community building by helping people to navigate in cities with the assistance of artificial intelligence and DSRC technologies, as well as facilitating social encounters, new relationships and outdoor activities. Our teamâ&#x20AC;&#x2122;s pitch was selected as the best proposal in the contest hosted at Boston University on Spring 2017.

Figure 1. The Smart Cane Program Team, Spring 2017

PROJECT FEATURES:

Multidisciplinary Teamwork

Innovative solutions to build mobility and social inclusion for people in the third age

2017 Accessibility facility for physically disabled people at MBTAâ&#x20AC;&#x2122;s light rail Green Line in Boston, MA Photograph by Luis Quintanilla

NEXT GENERATION MOBILITY CHALLENGE: THE SMART CANE PROGRAM

Problem definition and proposed solution Elder population willing to go places but vulnerable when navigating in cities due to:

Human-centered product design Figure 2. End user persona



Simple, easy to use



Various modes of use and accessories



Physical disabilities: motor, eyesight, hearing



Voice recognition and GPS technology



Disorientation while walking or using transit



Dedicated Short Range Communications (DSRC)



Fear to go out alone, insecurity



Plus: An App for volunteer signups to assist



Lack of companion

Smart Cane users

Opportunity

Volunteers participate by matching their daily journeys with users of The Smart Cane Program, getting rewards like frequent flyer miles, supermarket points, or discounts from enterprises and foundations with social responsibility programs.

Figure 3. The Smart Cane: Product Design

Elder population (65+ years old) will grow significantly:

2 billion worldwide by 2050 = 20% of population Source: United Nations: World Population Ageing.

Our proposed solution would assist the elderly to navigate through public transportation systems in their cities while concurrently connecting them to more people with similar interests and destinations. The latter would help users to socialize and feel safer during their journeys. While designing our product, the team gave a lot of thought to the particular needs of our end user persona (Figure 2): Val, an 85-year-old lady who is gradually losing eyesight and has no family able to take care of her, but she is still willing to do her errands and meet new people to feel accompanied. To that end, we designed a product that is user-friendly, and works with current GPS, voice recognition, and dedicated short range communications technologies that Toyota is investing in. During the prototyping stage, the team conceived a smart device with the form of a cane in order to be handy for senior citizens. Additional variants to the product could be belts, watches or necklaces, so more people would find it convenient.

Main device unattached

Design by Alejandro Delgado and Luis Quintanilla Images by Rubén Cerón

25

NEXT GENENRATION MOBILITY CHALLENGE: THE SMART CANE PROGRAM

How does the Smart Cane Program work? Process, product features, and implementation schedule Figure 4. Exemplification of a journey using the Smart Cane

Background image source: National Association of City Transportation Officials Additional image editing by Luis Quintanilla

26

NEXT GENERATION MOBILITY CHALLENGE: THE SMART CANE PROGRAM Figure 5. Product features and process sequence

Key audiences, monitoring mechanisms, and expected outcomes Target Audiences

Primary Beneficiaries  Senior citizens (65+ years)  People with physical limitations  Youth (Volunteer Reward Program)

Monitoring & Evaluation

   

Automatic feedback mechanism in case of malfunctions Continuous improvements in hardware and software Qualitative feedback from users Data analytics to improve service and measure effects on society

Expected Outcomes

  

Ability to travel autonomously and conveniently Community engagement: connections via smart devices Simple directions and volunteer assistance = safe journeys

Image by Rubén Cerón and Luis Quintanilla

Figure 6. Program implementation schedule

Stakeholders  Local communities  Municipalities  Enterprises and Foundations

My main contributions in the Smart Cane Program proposal for the Next Generation Mobility Challenge at Boston University included: design of the product and its features, exemplification of the end user experience, definition of the implementation process and schedule, identification of potential challenges, and collaboration in the creation of all visual aids.

Information source United Nations: World Population Ageing. Retrieved from: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pd

27

LUIS QUINTANILLA SUSTAINABLE PLANNING

All photographs, maps, charts, visual aids, tables, writing and graphic design in this document were produced by Luis Quintanilla.

CONTACT Luis.Quintanilla@gmail.com linkedin.com/in/luisfelipequintanillatamez @luisfelipeqt 857.316.8645

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