15
Do cities require too much parking for affordable housing in Santa Clara County? Darryl Yip UP 206A Final Project 12.15.2014

GIS final writeup

Embed Size (px)

Citation preview

Page 1: GIS final writeup

Do  cities  require  too  much  parking  for  affordable  housing  in  Santa  Clara  County?  Darryl  Yip  

UP  206A  Final  Project   12.15.2014  

08 Fall  

Page 2: GIS final writeup

Yip 2 Do cities require too much parking for affordable housing in Santa Clara County?

Research from UCLA Professor Donald Shoup found that parking increases sprawl, encourages automobile use over other forms of transportation, increases carbon emissions from automobiles, and decreases walkability (Shoup, 2011). However, fearing the lack of available on-street parking, cities require developers to build a minimum number of off-street parking spaces (Shoup, 2011). Cities often require upwards of one or two spaces per unit in multifamily housing developments. When parking can average $45,000 per space in land and construction costs, costs of constructing parking can be a significant portion of total development costs (Shoup, 2011). The table below shows minimum parking requirements for multifamily housing developments in three cities in Santa Clara County.

Current Parking Requirements for Multifamily Housing

City Bedrooms Car Parking Bicycle Parking

San Jose 0-1 1.25/unit 0.25/unit

2 1.7/unit 0.25/unit

3 2/unit 0.25/unit

San Jose Pedestrian Oriented Zones

Any Minimum 1.25/unit, Maximum 2/unit 0.25/unit

Sunnyvale 1 1 assigned covered space + 0.5 unassigned spaces/unit

None

2-3 1 assigned covered space + 1 unassigned space

Mountain View 0-1 1.5 spaces/unit (including 1 covered) + 15% of spaces for guests

None

2+ 2 spaces/unit (including 1 covered) + 15% of spaces for guests

Sources: City of San Jose, City of Sunnyvale, City of Mountain View

Meanwhile, the recent 2008 economic recession exacerbated the need for affordable housing at the same time that funders and lenders of housing pulled back (Zwick, 2014). In 2011, Governor Brown cut the Redevelopment Funds, the largest source of funding for affordable housing development, with an annual cut of $65,00,000 for Santa Clara County (Zwick, 2014). This drastically slowed down affordable housing development.

Background and Research Question

In 2013, and 2014, the nonprofit Transform surveyed parking for 68 affordable and market-rate housing developments in the San Francisco Bay Area (Transform, 2014). Of those, 30 are located in Santa Clara County. Transform (2014) found that although, under current regulations, current transportation models predict that future residents in an affordable housing development will drive only 4% less than a market rate development, better planning and policymaking can reduce driving by 30-80%.

Page 3: GIS final writeup

Yip 3 This study looks at Transform’s database and asks two important questions. First, are cities requiring too much parking for affordable and market-rate housing in Santa Clara County? Second, if so, what strategies can cities and developers do to ensure they use money efficiently and effectively?

Research

Using primary data from the Transform GreenTrip Parking Database and the United States Census, I looked at parking data for affordable and market-rate housing developments (which included parking occupancy rate, whether or not parking is bundled with rent, and number of parking spaces per unit) and demographic data for the census tracts (which included percent nonmotorists to work, median household income, and percent nonwhite). I also used University of Minnesota’s Transit Accessibility Maps to inform my research. I found my basemaps through UCLA Mapshare and the Metropolitan Transportation Commission.

Study Area: Santa Clara County

Study area is Santa Clara County, California, a county of about 1.9 million people just south of the San Francisco Bay and home to Silicon Valley. I focused on the densest part of the county, which includes the cities of Mountain View, Sunnyvale, Santa Clara, Campbell, and San Jose.

Study&Area&

Page 4: GIS final writeup

Yip 4 Recent Affordable and Market-rHousing Developments

I geocoded the housing developments, input transit lines and highlighted the VTA Light Rail and Caltrain commuter rail routes. I used geoprocessing to create a ½-mile buffer around the rail stations in addition to a 1-mile buffer around the route in order to highlight the focus area. I used the base layer from ESRI online. To show parking occupancy rates, I used a point-graduated symbol and divided it by quartiles, which represented the data quite accurately.

This map shows that most affordable housing developments are within the ½-mile radius from a rail station and all are near transit. The bigger the purple circle, the more occupied the parking spot. Additionally, it shows that all but one of the 30 affordable housing developments have empty parking spaces—some even show that about half of the parking spaces are never occupied.

Page 5: GIS final writeup

Yip 5 Bundled vs. Unbundled Parking

Using the same map, I separated bundled and unbundled parking in the data with the idea that bundled parking increases rent costs and hides parking costs into the cost of rent. I sorted this data by selecting by attribute and creating a new shapefile based on certain characteristics. The map shows that out of the 30 affordable housing developments, 26 have bundled parking and give parking away for “free,” yet most of the developments still have many unoccupied parking spaces at the peak.

Page 6: GIS final writeup

Yip 6 Number of Parking Spaces Built per Unit

I also mapped number of parking spaces per unit, which affects the denominator of parking occupancy rate (parking spaces occupied / total parking spaces). I sorted this data by selecting by attribute and creating a new shapefile based on certain characteristics. I found that 20 of the 30 developments have between 1 and 2 parking spaces per unit. A surprising 8 of the 30 developments have less than 1 space per unit and 2 of the 30 have more than 2 spaces per unit, and almost all still have unoccupied spaces at the peak.

Page 7: GIS final writeup

Yip 7 Percent Non-motorists by Census Tract

Next, I mapped demographic data from the United States Census. I mapped out percent non-motorists to work. I calculated this field by subtracting those who drive or carpool to work, leaving modes such as walking, transit, bicycling, and working from home under this field. I divided it into quartiles. The data show that census tracts in downtown areas, such as Downtown San Jose or Downtown Mountain View, have the highest percent Non-motorists to work, likely because people who live in those districts have excellent transit and job accessibility.

Percent'Non*motorists'

Page 8: GIS final writeup

Yip 8 Median Household Income by Census Tract

I mapped median household income by census tract. Santa Clara County has the highest overall median household incomes in the country, of about $93,500 from the latest United States Census. However, income distribution by census tract is highly uneven, with a high population of people below low-income in the central city. These are the same areas where the affordable housing developments are concentrated around, and the areas that have the greatest transit accessibility.

Median'Household'Income''

Page 9: GIS final writeup

Yip 9 Percent Nonwhite by Census Tract

I mapped percent nonwhite by census tract. I aggregated attribute fields by subtracting the total percent of the population who identified as “White only” from 100%, leaving all other races and two or more races to equal the percent nonwhite. I separated it into quantiles because it showed the wide distribution of whites and nonwhites across the county. The map shows that although Santa Clara is a majority nonwhite county, almost all of the nonwhites are concentrated in the densest part of the county where the affordable housing and highest transit accessibility tend to be.

Percent'Nonwhite'

Page 10: GIS final writeup

Yip 10 Number of Jobs Accessible within 30 Minutes by Transit by City Block

I found 2014 data from the University of Minnesota that shows job accessibility within 30 minutes by transit on a city block scale. The data was already a shapefile, so I just sorted it into quantiles to show the distribution of job accessibility in the county. The greener the map city block, the greater the job access 30 minutes by transit. To no surprise, the areas with the highest job accessibility by transit are all in the central city, because the central city has the most jobs to begin with. Those areas are also the areas where the recent affordable housing was built.

Number'of'Jobs'Accessible'within'30'Min.'by'Transit'

Page 11: GIS final writeup

Yip 11 Housing developments that are ½-mile from a Rail Station and are in a Census Tract with Greater than 15% Nonmotorists

Next, I wanted to extract information from a buffer for more complex data and map analysis. I did this by finding what housing locations are within ½-mile from a rail station and are in a census tract with greater than 15% nonmotorists. These two demographics are usually correlated (people who live near transit are more likely to take transit or other alternative transportation), but I wanted to see which housing locations qualify for both (some people live near transit but do not take transit, some areas have high transit usage but are not near rail). Because most of the housing tracts fit one of those criteria, I hypothesized that affordable housing locations that fit both have lower parking occupancy rates since they have higher transit accessibility and usage. However, the data do not show a clear difference, likely because more than just two variables come to play.

Page 12: GIS final writeup

Yip 12 Google Mashup

Link to Google Mashup File: https://www.dropbox.com/s/1ph0qwhaakqpg2b/Mashup.html?dl=0

I created a Google Mashup file by copying code into an HTML file and uploading it to Dropbox. I put a market in the center of Downtown San Jose by taking the coordinates of the Tech Museum, which is right in the middle of Downtown San Jose. Now, anyone with the link can view the affordable housing locations that I studied!

Obstacles of GIS research

Much of my project involved a lot of trial and error. For example, geocoding the affordable housing locations involved finding the correct address locator, which I spent several hours trying to find online and playing with the data. It also involved me remembering to clean up the data before I import it into GIS, since GIS does not handle editing as well as Excel does. Additionally, learning to create a model required several more hours of trial and error, because I had to know exactly what I wanted to do and what fields and tools I needed to input and output. Lastly, I learned that aesthetics are important. Although the most challenging part of mapping in GIS is data collection, importing, and making sure all the fields match, the most important part is presenting the data in a clear and concise way that can support the research conclusion. Thus, I dedicated a lot of time in my final to aesthetics.

Benefits of GIS research

Urban planning is inherently a spatial field, so I was happy to research a topic that is also inherently spatial. GIS enabled me to study not only mere parking occupancy of affordable housing developments, but also how spatial factors affect parking occupancy. For example, without the spatial component of GIS, there would be no easy representation to show that the affordable housing locations are mostly located near transit.

Page 13: GIS final writeup

Yip 13 Conclusion and Recommendations

My research supports my hypothesis that cities are requiring too much parking in affordable housing developments, and thus increasing the cost and challenges of developing affordable housing. Here are my conclusions:

ì Affordable housing developments in Santa Clara County tend to be concentrated near transit, in areas that have a high transit ridership, and high job access where people are more likely to be lower income, rent as opposed to own, and of color.

ì Of the 30 affordable housing developments surveyed in Santa Clara County, 1,377 of the 4,637 (29.7%) parking spaces were never used.

ì At ~350 sq. ft./space and ~$45,000/space, that’s 481,950 square feet or $62,000,000 that could have gone to more housing or better transit.

ì That’s about the same dollar amount that Governor Brown cut in annual County Redevelopment Funds ($65,000,000) in 2011 (Zwick et al, 2014).

ì Planners, policymakers, communities, developers, and other stakeholders have an important role in shaping the future of our cities.

ì While politically challenging, cities should eliminate or at least lower minimum parking requirements, especially in affordable housing developments, which tend to get built in areas that have high transit access.

Metadata and Model

I created the metadata from the addresses of GreenTrip’s Parking Database.

Page 14: GIS final writeup

Yip 14

I used the model above in all my layouts to create a buffer around the VTA Light Rail and Caltrain lines in order to emphasize the study area and for aesthetics. I also created a buffer around a half mile from the stations, to emphasize housing developments within a typical walking distance of a rail stations.

Skills Used Skill Data

Original Data GreenTrip Database Housing Locations

Seven Layer Map Statistics from Housing, Rail Lines, etc.

Modeling Modeled buffer around rail line (slide 16 shows model)

Extracting Data from a Buffer Housing ½ mile from rail station and are in Census Tracts with >15% nonmotorists = places less likely to need parking

Metadata GreenTrip Database Housing and Parking Data

Inset Map Focus Area, Santa Clara County, California

Point or Line Graduated Symbol

Parking Occupancy Point Graduated Symbols

Aggregating Attribute Fields % Non-White = 100% - % White Only

Attribute Subset Selections Rail Transit from all Transit lines

Boundary Subsets Selections Santa Clara County Census Tracts from California Census Tracts

Buffering ½ mile buffer from rail station and 1 mile buffer from rail line

Geoprocessing Buffered and clipped shapefiles

Geocoding Housing Locations

Page 15: GIS final writeup

Yip 15 Works Cited

City of Mountain View. Zoning Ordinance. http://www.mountainview.gov/depts/comdev/planning/regulations/zoning/default.asp

City of San Jose. Zoning Ordinance. https://sanjoseca.gov/index.aspx?nid=1751

City of Sunnyvale. Zoning Code. http://qcode.us/codes/sunnyvale/

Metropolitan Transportation Commission (2014). MTC Data Portal. http://dataportal.mtc.ca.gov/spatial-library.aspx

TransForm GreenTRIP Parking Database. (2014, October). Retrieved October 2014 from http://database.greentrip.org/

UCLA Mapshare (2014). 2014 Transit Accessibility Maps. http://gis.ats.ucla.edu/Mapshare/

United States Census (2012). American Community Survey 2012 5-year estimates. http://www.census.gov/acs/www/

University of Minnesota (2014). http://access.umn.edu/research/america/transit2014/maps/

Shoup, D. (2011). The High Cost of Free Parking (2nd ed.). Chicago: Planners Press, American Planning Association.

Zwick, K., & Ballard, S. (2014, July 22). Affordable housing: Santa Clara County and some cities are stepping up. Retrieved December 13, 2014, from http://www.mercurynews.com/opinion/ci_26189727/affordable-housing-santa-clara-county-and-some-cities