A Geography of Opportunity: Maps from the Regional Equity Atlas

Published by the Coalition for a Livable Future
GIS Analysis and Maps by Ken Radin, Population Research Center, Portland State University
Text by Meg Merrick and Vivek Shandas

Of the three pillars of sustainability (ecology, economy, and equity), equity has been largely absent from regional development discussions in part because policymakers lack a shared understanding of what equity means. In launching its regional equity initiative, the Coalition for a Livable Future (CLF) convened 100 leaders from across the region to envision what an equitable region would look like. Together, they defined an equitable region as one where:

  • All residents have access to opportunities for meeting basic needs and advancing their health and well-being: good jobs, transportation choices, safe and stable housing, a good education, quality health care, parks and natural areas, vibrant public spaces, and healthful foods (CLF, 2007).
  • Communities share both the benefits and burdens of growth and change (CLF, 2007).
  • All residents and communities are fully involved as equal partners in public decision-making (CLF, 2007).

In partnership with Portland State University’s Population Research Center (PRC) and the Institute of Portland Metropolitan Studies (IMS), the CLF has created a regional equity atlas. The Regional Equity Atlas: Metropolitan Portland’s Geography of Opportunity was published in August, 2007 and is available at CLF’s website: http://www.equityatlas.org.

This edition of the Periodic Atlas features several maps from the Regional Equity Atlas that begin to tell a story of the existing challenges and the highly dynamic nature of the relationships among people, place, and opportunities in the metroscape. We would like to thank the CLF for this important work and for allowing us to share these maps with our readers.

A Snapshot of Income Inequality in the Region

free lunches
Figure 1
(click to enlarge)

The percentage of students qualifying for free or reduced price meals often serves as an indicator of low-income neighborhoods. Figure 1 depicts the percentage of students in 2003 qualifying for free or reduced price meals in public elementary schools in the 4-county region. For example, in 2003, to qualify for the free lunch program a family of four could earn no more than $23,920 annually according to the USDA Food and Nutrition Service. Dark blue squares represent schools with the lowest percentages (0-7.5%) of low income students. The red squares indicate schools with the highest percentages (75%-100%). This map shows that a large number of elementary schools exist at either extreme of this spectrum and that income disparity is an issue in both urban areas and in the suburbs.

The Dynamics of Income and Housing

child poverty
Figure 2
(click to enlarge)

Figures 2 and 3 display flip-sides of income and the dynamics of change over time. Figure 2 shows the change in the number of children in poverty by location between 1990 and 2000. The blue color range from light blue to dark blue indicates a decrease in the number of children in poverty during this decade. The red color range indicates an increase in the number of children in poverty. The tremendous decrease in the number of children in poverty in northeast and north Portland is striking. The pronounced increase in Gresham and outer eastside neighborhoods also stands out. However, significant increases also occurred in some Vancouver and Beaverton neighborhoods.

Figure 3
(click to enlarge)

Note the dramatic increase in the distribution of upper income households (households whose incomes were greater than $125,000 in 1989 and greater than $100,000 in 1999) in inner northeast Portland, where a decrease in children in poverty occurred during the same period (figure 3). Increases also occurred in upper income households throughout the inner eastside of Portland, also accompanied by decreases in the number of children in poverty. While some poor families might have acquired wealth during this period, it is much more likely that middle and upper income households displaced poor families in these areas.

Figure 4
(click to enlarge)

Given the shifting locations of households at both ends of the income spectrum that occurred between 1990 and 2000, the changes that occurred during the same period in the location of single-family rental housing are provocative (figure 4). We see losses in northeast and north Portland in the same areas where there was a decrease in children in poverty, losses in outer southeast Portland, and notable losses in Oregon City, Hillsboro, and Vancouver. The loss in single-family rental housing and the corresponding increase in upper income households in these areas may be due in part to gentrification. Large gains in rental single-family housing occurred in eastern Vancouver, Hillsboro near Intel, and McMinneville, areas that aren’t necessarily providing affordable housing for the poor.

african american change
Figure 5
(click to enlarge)

The distribution of African American residents changed significantly between 1990 and 2000 (figures 5 and 6). The large blue area in figure 5 in inner northeast Portland indicates the significant decrease of African Americans that took place during this decade, with increases in north Portland, outer northeast Portland, and Gresham. However, in 2000, the largest concentration of African Americans in our region remained in northeast Portland (figure 6). Since 1980 the percentage of African Americans in several neighborhoods in northeast Portland has changed dramatically. In 1980, one census that was 73% African American, and four others were over 50% African American. In 1990, six census tracts were over 50% African American. And by 2000, only one census tract that was 50% African American.

af am change
Figure 6

Homeownership often symbolizes the American Dream, but it also is one of the primary ways that U.S. residents can save and accumulate capital.

minority home ownership gap
Figure 7
(click to enlarge)

Figure 7 illustrates a profound inequity with regard to minority homeownership in the metroscape. Assuming that the percentage of minority households is the same as white households in any given census tract, this analysis also assumes that the percentage of home-owning households (white and minority) should be the same (or “no gap” on this map). Any census tract that is in the yellow to red color range indicates a discrepancy between the percentage of white household home-owners and minority household home-owners.

2000 hispanic
Figure 8
(click to enlarge)

Figures 8 and 9 show tremendous growth in the Hispanic population between 1990 and 2000. Their numbers tended to increase in already established Hispanic communities such as Hillsboro, Forest Grove, Cornelius, and the Cully neighborhood in northeast Portland. Significant increases also occurred in Gresham, Beaverton, and north Portland in areas proximate to transportation options such as light rail and lower cost housing.

1990 hispanic
Figure 9
(click to enlarge)

Access to Parks and Natural Habitat

As the metroscape continues to change in dynamic and unpredictable ways, the access to parks and green spaces may also change for many residents. Parks and green spaces in the metroscape provide numerous amenities and are part of a network of “green infrastructure.” This green infrastructure protects the water quality of our streams, rivers, and drinking water supplies; supports the region’s diverse plants and animals; protects air quality; and contributes to residents’ health and quality of life. Some studies even suggest that home values improve relative to proximity to urban parks and green spaces. As a result, residents’ access to green infrastructure is a critical issue in questions of equity and social justice.

Two maps illustrate green access. The walking distance to urban parks in Portland suggests that most neighborhoods are within ½ mile to a public park (figure 10). While this map doesn’t indicate the recreational opportunities in these parks, it does illustrate an extensive network of urban parks. Several areas in Portland are also greater than one mile from parks, including areas west of Happy Valley and east of Milwaukie, 102nd Ave. near the I-84/I-205 interchange, and Front Avenue in the northwest Portland.

Parkland Access
Figure 10
(click to enlarge)

Access to habitat is distinctly different than access to urban parks. While urban parks may provide recreational opportunities for citizens, habitat is essential for maintaining healthy urban ecosystems, including flood control, native biodiversity, and cleaning air pollutants, in addition to providing recreational opportunities (such as hiking, bird watching, and environmental education). Figure 11 takes a broader perspective by looking at the percent of habitat and distances from different parts of the metroscape. The figure suggests that most of the habitat is located outside central Portland. While Forest Park is a beacon of habitat close to Portland’s city center, large tracts of habitat can also be found near Cornelius, Damascus, King City, Lake Oswego, and in areas along the Columbia River.

Proximity to Natural Habitat
Figure 11
(click to enlarge)

The distribution of the green infrastructure in the metroscape raises questions about how changes in the region will impact the access to green spaces. Will increasing population reduce the amount of habitat? Will the loss of habitat mean an increase in parks and other recreational amenities? How much of the access to green infrastructure is determined by household income? Addressing these questions will require policymakers and planners to consider who is being affected and how we can improve the living conditions for the whole population.

The Coalition for a Livable Future initiated and managed the production of the atlas, including providing editorial leadership.The project was completed at the Portland State University Population Research Center by Ken Radin (analysis and cartography) and Irina V. Sharkova (methodology and project oversight). CLF developed the project design and funding in partnership with the Institute of Portland Metropolitan Studies. Detailed information about the methodologies used to create the maps included in this atlas are available in Appendix B of the Regional Equity Atlas: Metropolitan Portland’s Geography of Opportunity. The Regional Equity Atlas is available for purchase or for download at the Coalition for a Livable Future’s website: www.equityatlas.org.

The narrative for this edition of the Periodic Atlas was written by Meg Merrick, IMS, and Vivek Shandas, assistant professor at the Nohad A. Toulan School of Urban Studies and Planning at PSU, in cooperation with the Coalition for a Livable Future.

Geography in Laser-light: Using Lidar to Map the Metroscape

Chances are that at some point in your life, without even knowing it, you’ve been hit by a laser. It may have been mounted on an airplane, helicopter, or even a satellite. Your dwelling, your car or bike, perhaps even your pet may have also been hit. The fact that you’re still here to tell the tale is because the laser used was far too weak to damage you and was part of a system known as light imaging, detection, and ranging—“lidar” for short.

Whether orbiting the earth, circling the skies right above us, or just trundling down a road in the back of a pickup truck, lidar equipment has been put to use by engineers and scientists (and sometimes even artists) for projects ranging from the mundane to the monumental. Like almost all technologies, it’s hard to keep pace with the rate of improvements and changes to lidar, but every increase in its fidelity allows our region to know more about our resources, risks, and opportunities.

Figure 1. Source: BLM, Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community

This issue of the Periodic Atlas will look at the rising prominence and capabilities of lidar, and how local researchers are using the technology to change the way we see, measure, and manage our region (figure 1).

Figure 2. A 3D rendering of a lidar point cloud, here looking at the western end of the Marquam Bridge. Flat surfaces such as roadway and rooftops are rendered in red, while likely tree locations are in green.

First developed in the 1960s in conjunction with the invention of the laser, lidar is an active sensing system that functions on the same principle as its cousins, sonar and radar, firing a pulse of energy in the form of radar waves, sound, or light, and measuring the time it takes to bounce back. If you know the speed of that pulse of energy, then measuring the time it took to bounce back to you will give you the distance to your target of interest.

In lidar’s case, a laser operating in the infrared, visible, or ultraviolet spectrum is fired at the target. (In reality, thousands of beams are pulsed at the target.) A sensor unit mounted with the laser detects the reflected beams, measures their time of flight along with their energy intensity, and returns what is called a point cloud (figures 2 and 3).

This point cloud represents the first, rawest form of lidar-derived data, containing millions, or even billions, of points, each one representing the three-dimensional coordinates of laser reflections. With current technology, the accuracy of a given point is usually within fifteen centimeters vertically, and forty centimeters horizontally.

However, from their raw form, point clouds are often far too large and complex to be used by anyone but specialized analysts or engineers. Most often, the point cloud is simplified into a raster or pixelated image. Each pixel of this image represents an averaging of hundreds or even thousands of individual lidar points, depending on its resolution. The most common of these rasters are “digital ground models” used for measuring the elevation of the natural or built environment (figure 4).

Figure 3. A 3D point cloud rendering of Ladd’s Addition, with treetops in green and rooftops in red.

Given the high labor and material costs of collecting lidar data, public sector users have tended to pool their resources into consortiums to purchase large, high-resolution data sets. In Oregon, the Oregon Department of Geology and Mineral Industries (DOGAMI) has led the Oregon Lidar Consortium since 2007, managing procurement, establishing and maintaining quality standards for the data, and hosting the final products on the web for all members of the public to use.

Here in the region, lidar data has been an essential component of an ever-expanding spate of research projects, many of them focusing on sustainable solutions for managing climate change and new development

Figure 4. Lidar digital ground models (right) is a massive improvement over older, usually radar-derived elevation models (left). For years, the usual resolution for digital elevation models was 10 m. x 10 m. With lidar, that resolution is now improved to 3’ x 3’. That means being able to see ever more detailed features of the landscape, like being able to see individual oxbows of the Sandy River that give their name to Oxbow Regional Park.


One of lidar’s most common uses has been the study and prediction of landslides. According to DOGAMI, tip-offs include “scarps, tilted and bent (‘gun-stocked’) trees, wetlands and standing water, irregular and hummocky ground topography, and over-steepened slopes with a thick soil cover.” With finer resolutions and improvements in the ability to interpolate terrain beneath forest canopies, geologists and environmental engineers are using lidar to spot those tip-offs, as well as evidence of historic landslide activity that may not be immediately visible to the naked eye

Earlier in 2018, DOGAMI released a report and accompanying data sets detailing the landslide risks faced in western Multnomah County (Figure 5). Using lidar along with existing tax lot and census data, DOGAMI determined that $1.65 billion in land and buildings and almost 6,700 people are located on existing landslides, twenty-nine thousand residents are at direct risk of a shallow landslide, and eight thousand at risk of a major deep landslide. The majority of those at risk are located in and around Forest Park, where elevation, soil, runoff, and vegetation health all combine as determinants of landslide risk.

Figure 5. DOGAMI’s 2016 landslide risk assessment for central and western Multnomah County. DOGAMI estimates that 21 percent of the surveyed area is at moderate risk of a shallow landslide, while 16 percent is at high risk. Deep landslides (those likely to be triggered by an earthquake) are more damaging than shallow, but cover a smaller area—around 7 percent of the study area is at high risk of a deep landslide. (Data source: DOGAMI)


While the Portland region has long enjoyed the reputation and benefits of being one of the most verdant urban areas in the nation, measuring the health of urban tree canopies has either relied on aerial observation and a lot of guesswork, or tedious on-the-ground investigation. With the introduction of lidar, researchers have gained a powerful tool that opens the door to new ways of measuring the health and density of trees across the region.

Figure 6a. A high-resolution lidar raster of downtown Oregon City, with tree biomass colored green.

Figure 6b. The same section of Oregon City, but overlaid with tree-top points and building footprints. Spatial analysts can use advanced statistical algorithms to sift through lidar data and spot the abrupt changes in height other patterns that indicate a tree or a rooftop.

Researchers from PSU’s Sustaining Urban Places Research Lab and Metro’s Data Resource Center have used canopy height data from lidar in conjunction with spectrum data from aerial imagery to produce new datasets that can estimate the total biomass of the region’s trees (Figure 6). Going further, researchers used statistical analysis of the lidar data to identify individual tree crowns, which in turn allowed for the identification of particularly tall, old-growth trees around the region. Going forward, this data could allow local tree-preservation advocates and agencies to more accurately allocate their limited resources.


Ecoroof development has taken on greater importance in the region, especially in light of Portland’s recent inclusion of an ecoroof mandate and targets in the Central City 2035 Plan. While specific incentives have yet to be decided, the city plans to add 408 acres of ecoroofs to the city by 2035. Portland State researchers and faculty took part in analyzing current regional lidar data to determine which existing buildings may already be good candidates for adding ecoroofs (figure 7a and 7b). Using high-resolution lidar, researchers were able to identify candidate buildings across the region, analyzing not only the overall aspect and slope of roofs, but their flatness as well (e.g., building roofs without excessively bulky HVAC units on them).

Figure 7a. For an investment in a green roof to pencil out, let alone be feasible, the planting area needs to be big enough and free of obstacles like large HVAC units elevator winch housing that lidar is perfect for identifying. Above is a green roof suitability analysis of buildings in Portland’s city center. Suitability has been calculated using a combination of rooftop area, angle and evenness. (Data source: City of Portland, Lone Fir Labs)

The projects discussed in this issue of the Periodic Atlas represent just a glimpse of what lidar data has allowed researchers to do so far. Moreover, these projects exist in the growing area of overlap between lidar data and sustainable policies and investments. As the capabilities of lidar become more known among local decision-makers, entirely new ways of analyzing and planning for a sustainable region could be quick to follow.

Figure 7b. Oblique view of potential green roofs along the Willamette River near downtown Portland. (Data source: Metro, Lone Fir Labs)

Researchers within IMS and PSU are increasingly depending on lidar data to improve our understanding of the region, but the data still have limits. First among these is the static nature of the data—with the rapid growth and change occurring within the region, every day that passes means the most recent lidar survey from 2014 loses a little bit of its relevance. The scope of the data is also limited as it does not include complete coverage, often leaving out rural areas and small towns. While there is certainly interest among researchers in procuring a more exhaustive survey of the entire MSA region, and the benefits of lidar data are becoming more and more evident, local elected officials and agencies will have to find a way to share in the investment and management of future surveys.

Figure 8. A 3D point cloud rendering of the Tilikum Bridge (in blue) from a lidar survey taken during construction. The crane barge is visible in the lower left, at the foot of bridge’s western tower.

Justin Sherrill is a research assistant with the Institute of Portland Metropolitan Studies and a graduate student in the Masters of Urban & Regional Planning program at Portland State University, where his focus is on public transportation systems and data visualization.

The Geography of Health

Our ability to lead a fulfilling life and pursue our goals is largely shaped by our health.[1] Although we experience these conditions such as illness and disabilities at a very personal level, factors outside of our control are often what determines our health.[2] Known as the Social Determinants of Health, where we are born, work, live, and spend our lives is considered equally if not more important to our health status than medical care and personal health behaviors.[3] As a result, certain communities and populations disproportionately experience burdens. Identifying and increasing awareness of health disparities is an essential step toward improving the health status of all Metropolitan residents.

The 500 Cities Project, a partnership between The Robert Wood Johnson Foundation, CDC Foundation, and the Centers for Disease Control and Prevention
(CDC), provides surveillance data to better explore and visualize health disparities affecting urban residents within the United States. Using a multi-level statistical modeling framework, the project predicts individual disease risk and health behaviors, and estimates the geographic distributions of population disease burden and health behaviors.[4]

Here we examine six of twenty-seven available measures related to health inequity including two health outcomes, two unhealthy behaviors, and two preventative
measures. As the 500 Cities project only covers the largest US cities, the study area within the Portland-Vancouver Metropolitan Statistical Area will look at Portland, Vancouver, Gresham, Hillsboro, and Beaverton. Each of these six maps also includes a chart showing how the Portland-Vancouver area fares compared to a selection of cities across the country with similar economic and demographic characteristics,[5] as well as to the 500 Cities national average.

The Atlas article concludes with a Social Vulnerability Index (SVI) map, providing the reader an opportunity to compare the 500 Cities results with a map of composite scores based on demographic indicators. The SVI indicators show data at the Census tract level derived from the 2016 five-year American Community Survey, and include percent values for non-white population, unemployment, bachelor’s degree attainment, home cost burden, rent cost burden, dependency (ages zero to four, and sixty-five and over), and disability.

Health Outcomes

Health outcomes can be thought of as “the results that matter most to patients.”[6] That is, health outcomes are the diseases and conditions that reflect our
state of physical, mental, and social well-being.[7]

Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that is the third leading cause of death in the United States.[8] Nearly 80 percent of
COPD deaths are attributable to smoking,[9] while other risk factors include occupational exposure, ambient air pollution, and long-term severe asthma.[10]

High Blood Pressure

High blood pressure, or hypertension, is a leading  contributor to critical public health issues in the United States. Approximately 20 to 30 percent of coronary heart disease (leading cause of death in the United States) and 20 to 50 percent of strokes (fifth leading cause of death in the United States) are attributable to uncontrolled hypertension.[11] Leading causes of hypertension include smoking tobacco, eating foods with high sodium intake and/or low in potassium, physical inactivity, obesity, and excessive alcohol consumption.[12]

Unhealthy Behaviors

Unhealthy behaviors, or behavioral risk factors, are detrimental to an individual’s physical or mental health and can lead to poor health outcomes.[13] Unhealthy behaviors are implicated in up to 40 percent of premature deaths in the United States.[14]

No Leisure Time Physical

Activity Physical activity during leisure time includes any activity outside of work with physical movements that improve health and quality of life such as exercise, gardening, or walking to work.[15] An insufficient amount of physical activity is a leading risk factor for premature death due to diseases such as heart disease, cancer, stroke, and type 2 diabetes. [16] 

Sleeping Less than Seven Hour

As defined in the 500 Cities measure, individuals experiencing insufficient sleep report usually sleeping fewer than seven hours a night.[17] Insufficient sleep has been connected to reducing productivity (e.g., poor work, academics), can reduce an individual’s quality of life,[18] and has been associated with major chronic diseases and conditions, such as diabetes, cardiovascular disease, high blood pressure, obesity, and depression. [19]


Prevention is at the core of public health work.[20] Public health work is largely focused on preventing poor health outcomes and unhealthy behaviors before they lead to individuals becoming sick or injured.

Lack of Health Insurance

Lack of health insurance is a major barrier to accessing health services and preventative services.[21] Uninsured individuals are associated with poorer health status,4 are less likely to be hospitalized for preventable illnesses and conditions, and can be burdened with insurmountable debt from medical bills.[22]

Papanicolaou Test

The Papanicolaou test, or Pap smear, is a screening procedure for women to detect cervical cancer. It has been estimated that increased use of the Pap smear (recommended once every three years) could lead to timely and effective treatment and ultimately the prevention of approximately 40 to 60 percent of cervical cancer deaths.[23]

Social Vulnerability

The social vulnerability indicators are largely reflective of the social determinants of health–conditions in our social, economic, and physical environments that affect a wide range of health risks and outcomes.[24]


By all seven measures, the Metro area ranked healthier than the 500 Cities average. However, local tracts that are more likely to experience poor health outcomes, practice unhealthy behaviors, and are less likely to seek preventative services, are consistently concentrated in the same areas throughout the maps. The PortlandGresham border, North Beaverton, East Hillsboro, and the downtown Portland tracts all indicate higher potential for residents to experience health disparities. As illustrated in the social vulnerability map, this may be due to a number of factors such as less access to health services or experiencing poorer social, economic, or physical environmental conditions detrimental to residents’ well-being. 


The association between the concentrated health disparities and higher rates of social vulnerability are a strong indication of health inequities and warrant further exploration. Although there are limitations to the validity of spatial data, in combination with qualitative research, such as resident outreach, planners and officials can identify emerging health problems and develop targeted interventions to reduce health inequities experienced by the Metro area residents.

1. Website of the World Health Organization, “Health Systems: Equity,” http://www.who.int/healthsystems/topics/equity/en/

2. Centers for Disease Control and Prevention, “Introduction: CDC Health Disparities and Inequalities Report—United States, 2013,” MMWR (2013), 62 (Suppl. 3).

3. Theodor R. R. Marmor, Morris L. Barer, and Robert G. Evans, Why Are Some People Healthy and Others Not? The Determinants of Health of Populations (Social Institutions and Social Change). (New Jersey: Aldine Transaction, 1994). 4. Centers for Disease Control and Prevention, “Introduction: CDC Health Disparities and Inequalities Report—United States, 2013,” MMWR (2013), 62 (Suppl. 3).

4.  Website of the Federal Reserve Bank of Chicago, “Peer City Identification Tool.” (n.d.), https://www.chicagofed.org/region/ community-development/data/pcit

5. Website of ICHOM,“Mission,” (n.d.), http://www.ichom.org/

6. R. Gibson Parrish, MD, “Measuring Population Health Outcomes,” Preventing Chronic Disease, 7, no. 4, A71 (2010).

7. Website of the American Lung Association, “Lung Health & Diseases.” (n.d.), http://www.lung.org/lung-health-and-diseases/ lung-disease-lookup/copd/

8. B Adhikari et. al, “Smoking-Attributable Mortality, Years of Potential Life Lost, and Productivity Losses—United States, 2000-2004,” (2009), JAMA, 301, no. 6, 593–594.

9. Mannino, & Holguin. “Epidemiology and Global Impact of Chronic Obstructive Pulmonary Disease,” Respiratory Medicine: COPD Update, 1, no. 4, 114–120.

10. Go et al., “Heart Disease and Stroke Statistics—2013 Update: A Report From the American Heart Association.” Circulation, 127, no. 1, E6–E245.

11. Ibid.

12. “Unhealthy Habit.” McGraw-Hill Concise Dictionary of Modern Medicine, (2002), https://medical-dictionary.thefreedictionary.com/ Unhealthy+Habit 

13. Ali H. Mokdad et al., “Actual Causes of Death in the United States, 2000,”. JAMA, 291. no. 10, 1238–1245. 

14.  Website of the World Health Organization, “Physical Activity,” (2018), http://www.who.int/en/news-room/fact-sheets/detail/ physical-activity

15. United States. Department of Health and Human Services, “2008 Physical Activity Guidelines for Americans: Be Active, Healthy, and Happy!” (2018) ODPHP publication, no. U0036. 

16.  Website of the Centers for Disease Control and Prevention, “500 Cities: Local Data for Better Health,” (2017), https://www.cdc. gov/500cities/methodology.htm 

17.  H. R. Colten and Bruce M. Altevogt, & Institute of Medicine Committee on Sleep Medicine Research. “Sleep disorders and sleep deprivation an unmet public health problem,” (Washington, DC: National Academies Press, 2004).

18. Ibid.

19.  Website of the CDC Foundation, “What is Public Health?” (n.d.), https://www.cdcfoundation.org/what-public-health 

20.  J. Weissman et al, “Delayed Access to Health Care: Risk Factors, Reasons, and Consequences.” Annals of Internal Medicine, 114, no. 4, (1991), 325–31. 

21. Ibid.

22. Ibid.

23.  “Practice Bulletin No. 131: Screening for Cervical Cancer,” Obstetrics & Gynecology, 120(5), (2012) 1222–1238.

24. Website of the Office of Disease Prevention and Health Promotion, “Social Determinants of Health.” (2018) https://www. healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health

Regional Connections 2: Economy

by Jeremy Young, Sheila Martin, and Meg Merrick; cartography by Robert Smith, Jeremy Young, and Meg Merrick

Portland’s regional economy is integrated across the 7-county Metropolitan Statistical Area (MSA) by a workforce that commutes throughout the region. In last winter’s issue of Metroscape,we demonstrated these connections and others through a series of maps showing regional commuting and migration. As these data indicate, employers draw their work force from every county in the MSA; therefore, jobs created in one particular city or county benefit not just the residents of that city or county, but people region-wide and on both sides of the Columbia River.

The locations of industrial and manufacturing activities (and,therefore, jobs) are driven by a number of factors.Historically, access to transportation—primarily rivers, rail, and roads—determined the location of these industries.The legacy of that history is apparent in the region’s zoning maps that identify the locations of current industrial areas (figure 1). Despite the conversion of some formerly industrial and manufacturing sites close to downtown Portland to mixed-use residential zones (i.e. the Pearl District), the influence of history is apparent in the current location of these industries. Furthermore,as the importance of air transportation has increased, so has the amount of land dedicated to industrial and commercial uses increased surrounding the Portland International Airport. Land use zoning is fundamental to business location. 

But what other factors influence the spatial location of jobs? Factors other than transportation and traditional zoning patterns have become more important to the location of businesses as the economy has evolved. Rather than depending primarily on the extraction and processing of raw materials, the region’s economy now depends on the development of knowledge-based products and services and the application of advanced models of logistics and supply chain management. Microclimates that offer differing combinations of industrial land, office space, financial incentives,transportation options, worker amenities,and proximate workforce draw different types of companies to some areas within the metroscape. These microclimates become apparent as we examine the spatial patterns of employee location in specific industries.

Nevertheless, the maps in this edition of the Periodic Atlas show that although some regions may specialize in specific types of activities, our region’s key industries provide jobs throughout the region, revealing an economic connection among different cities and counties that may not be readily apparent.  


Figures 2 and 3 show the distribution of all primary jobs throughout the region in 2011. They reveal the densities of employment within key industrial and commercial corridors and scattered employment near to primarily residential areas.

Figures 4 and 5 indicate the distribution of total manufacturing employment in 2011. These maps show a different spatial pattern,with manufacturing employment concentrated on industrial land near freight corridors (including highways, railroads, airports, and port facilities).

This pattern differs from the locations of retail activity (figures 6 and 7), which, not surprisingly, are more accessible to residential areas. That said, the highest concentrations of retail employment are in large shopping centers and malls that are generally located at freeway interchanges.

It is notable, however, that throughout most of the region’s zoning history, commercial uses were restricted from residential zones.But, in recent decades, several cities in the region have adopted “mixed-use” zoning which permits a combination of commercial and residential uses in the same facilities. These tend to be located in “centers” and along “corridors”where higher residential densities are encouraged.

Patterns also vary by industry. The Portland Development Commission (PDC) has defined four traded-sector clusters that have significant presence in the Portland region. Advanced Manufacturing, Athletic & Outdoor, Clean Tech, and Software are high growth, high-wage industries in which the region has a competitive advantage. Examining the distribution of employment in these clusters demonstrates that they are truly regional; most include employment in every county. The tables below show total employment in these clusters for the7-county region for 2012.

The Advanced Manufacturing cluster differs from total manufacturing in that it focuses on metals, machinery,computer and electronic products, and transportation equipment—high-wage, knowledge-intensive industries that evolved from more traditional manufacturing and the region’s high tech beginnings at Tektronix. This industry incorporates advanced technology in each stage of the process, from design, to fabrication, to assembly. The region’s comparative advantage, according to PDC, is based on the industry’s execution of lean processes and resulting production cost efficiencies.

The Athletic & Outdoor industry is focused on apparel, footwear and sporting goods that take advantage of Portland’s design talent, strong local customer base, and the existence of heritage firms Jantzen, Nike, and Columbia Sportswear to provide a strong talent pool. According to economist Joe Cortright, the success of the industry relies on a young, creative workforce and a wide array of suppliers and services firms that provide advertising, public relations, marketing and merchandising.

Clean Tech includes companies that generate clean power, design equipment and programs to conserve power, design green buildings, and provide research and consulting services that support the generation and conservation of energy. The region’s strengths in manufacturing and semiconductors and its environmental ethic and policy contribute to its strengths in this sector. The strength of the Software cluster in Portland is rooted in its open source history and reputation, its comparatively low talent costs and its emphasis on small entrepreneurial firms.

The concentration of employment in these clusters is shown in figures 8-11. The squares represent one-square-mile areas and the shading indicates the density of jobs per square mile. As shown in the tables, each county in the region has employment in each of the region’s key clusters.

The advanced manufacturing cluster exhibits a spatial pattern similar to that of total manufacturing, with employment throughout the region and concentrations along the rivers, near the airports, and along major industrial corridors in northwest Clackamas County and Washington County. Thus,the legacy of location preferences along these freight transportation corridors remains in this industry, and the spatial patterns have not significantly altered despite the shift to advanced technology processes for design, fabrication, and production.

Athletic & Outdoor employment likewise is spread throughout the region with some obvious concentrations in the City of Portland and in Washington County. The central city concentration is probably explained by Adidas Group US Headquarters in North Portland, and the presence of design firms located near a young, highly educated and creative workforce located n or near the central city. The industry’s concentration in Washington County is the obvious influence of Nike and Columbia Sportswear.

Clean Tech has perhaps the most dispersed employment, reflecting the broad distribution of energy and waste management companies throughout the region. But a core of concentration in the central city reflects the city’s energy engineering and management strengths and a sub-cluster of green building companies. Software companies also locate throughout the metro area, but denser concentrations occur in the downtown Portland area and in the commercial corridors of Washington County.These clusters do exhibit differing patterns of location and we might think of them as specific to a certain part of the region:advanced manufacturing along the rivers and in Clackamas County; software downtown; and Athletic and outdoor in Washington County. Yet the maps demonstrate the region-wide presence of these clusters and underline the shared nature of their success. As they develop, these clusters provide jobs to people who live in the entire area and therefore offer economic benefits to each city and county in the metroscape.

Metroscape, Summer 2014

Regional Connections

by Sheila Martin and Jeremy Young

Dr. Nohad Toulan’s legacy has many facets and one is the development of institutions for regional decisionmaking. His establishment of the Institute of Portland Metropolitan Studies in 1991 was based on his assessment of the opportunity to develop an institution that could focus on issues that required cross-jurisdictional cooperation. At the time, this was revolutionary thinking. Although Metro had been established in 1979, it didn’t (and still doesn’t) officially include the Washington side of the metroscape. Myron Orfield’s Metropolitics wasn’t published until 1997 and Neil Pearce wouldn’t publish his Citystates until 1993. Nevertheless, Dr. Toulan recognized that progress on many important issues required that we think and act regionally, and that no formal institutions for accomplishing this yet existed.

This atlas provides evidence that the metropolitan region is indeed connected as people travel through the region to live and work. We provide two sets of maps that speak to the region’s connectedness through the movement of people. The first set of maps demonstrates how people move about the region on a daily basis to work; the second set shows how people move into and about the region as they change their place of residence.

The final set of maps shows some of the consequences of this mobility: the changing demographic diversity of our metropolitan region. As people migrate in and find their place, demographic patterns have changed. The result, which may be surprising for some, is that our communities share the experience of demographic change, although that change looks a little different in each neighborhood.

Commuting Patterns

The maps on the facing page show the volume of daily commuting into each county, in the Portland metropolitan region, from each of the other counties. These numbers are based on the location of someone’s primary job and the location of their residence. The county shown on the map in yellow is the county people are commuting to, and the size of the orange circles indicate the volume of commuting from each of the other counties.

The greatest volume of commuting occurs between Multnomah and Washington counties, with over 61,000 people commuting into Multnomah County each day from Washington County, and 42,000 each day commuting from Multnomah to Washington counties. Clackamas County also exchanges many workers across its borders, with over 56,000 people commuting to Multnomah County each day, and over 22,000 commuting into Washington County. Clackamas receives approximately 32,000 workers from Multnomah County and 19,000 from Washington County.

Commuting to and from the other counties is much smaller, but we do, perhaps surprisingly, see hundreds of people traveling from one edge of the region to the other – from Columbia to Clackamas and from Skamania to Yamhill. Clearly, the labor market within the region is connected by people willing to travel long distances to find the right fit for their skills and interests. This means labor market, housing market, and transportation issues require a regional approach.

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Migration Patterns

The metroscape is also connected by a pattern of intra-regional migration—people moving from one part of the metropolitan region to another—as their life circumstances, tastes, and housing needs change. Migration connects us because as we move around the region, we bring with us our experiences, perceptions, and points of view. As we interact with our neighbors, we expose them to ideas that may be new to them—and we learn about the challenges and benefits of living in our new community.

To quantify these patterns, we rely on the 5-year aggregate data from the American Community Survey for the years 2006 to 2010. The survey asks the question, “Did this person live in this house or apartment one year ago?” and if the answer is no, “Where did this person live one year ago?”

Based on the answers to these questions, we mapped the flow of migrants into and among the counties in the metroscape. The maps show that almost 41,000 people migrated to Washington County during this period. Thirty two percent of those were from within the metropolitan region. Forty eight percent came from out of state, and 9.4 percent came from abroad. Within the stream of regional migrants to Washington County, the highest number came from Multnomah County.

Fifty-five thousand people moved to Multnomah County during this period. About 29 percent of these, or 16,000 in-migrants, were from other counties in the Portland region. The highest number of regional in-migrants to Multnomah County came from Clackamas County, followed by Washington and Clark. However, Multnomah County attracted almost 28,000 people from outside of Oregon and almost 6,000 from abroad. 

Clackamas County also received over 13,000 in-migrants; most of these (51 percent) were from within the metropolitan region, with the highest number of migrants from Multnomah County. About one-third of migrants to Clackamas County came from a different state, and about 5 percent moved there from abroad.

Clark County, Washington received over 27,000 in-migrants, with only 23 percent of these coming from within the metropolitan region. Just over two-thirds (69 percent) came from other states, and about 43 percent of those (8,167) came from Oregon (3,859 coming from Multnomah County). This represented about 14 percent of Clark County’s total in-migration.

About 8,400 people moved to Yamhill County and the majority of these—53 percent—came from a different state. 29 percent moved from within the region, with the highest flows being from Washington County.

Columbia County received the fewest number of in migrants – only 3,750—and most of these came from within the metropolitan region. 897 Washington County residents moved to Columbia County and 665 people moved there from Multnomah County.

This continuous change in the amalgam of residents in each neighborhood in the metroscape means that we are constantly challenged to question our assumptions about who we are as a region and how to approach our important public policy challenges.

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Regional Diversity

A final factor that connects us is the changing racial and ethnic demographics of our region. As previously explained, each county in our region had in-migrants from other states and other countries, leading to a changing regional demographic profile. Specifically, over the past decade, our region has become much more diverse as the percentage of individuals who are White and non-Hispanic has declined. But the patterns of change across the region are somewhat different depending on each community’s economic drivers, changes in its housing market, and its historic ethnic communities.

Each map shows for each census tract in the region the change in the percentage of the population within a specific ethnic group (Asian alone or in combination, Black alone or in combination, Hispanic of any race, and White alone, non-Hispanic) between 2000 and 2010. The maps show how the population share of these ethnic groups has changed over those ten years.

The percentage of people who are Asian has increased in many suburban areas of the metropolitan region. While a few areas within Portland, Beaverton, and Vancouver have experienced a relative decline in the Asian population, many areas in northern and eastern Clark County, western Washington County, and eastern Multnomah and Clackamas counties have experienced a relative increase in their Asian populations.

The maps showing changes in the Black population show a somewhat different pattern, with large decreases in the percentage of the Black population in the historically Black neighborhoods of North and Northeast Portland and consistent increases in East Portland, Gresham, and in parts of Clark County. 

The percentage of the population that is Hispanic has increased almost everywhere in the region, with a few decreases for Census tracts in close-in neighborhoods of Portland where increases in the cost of housing likely prompted some Hispanics to move to other areas.

The percentage of the White alone, non-Hispanic population has declined almost everywhere in the region, mirroring increasing diversity throughout the region with a few exceptions. The most notable exception is in close-in northeast Portland neighborhoods where the increase in the White population has been over 20 percent in several Census tracts. This trend appears to reflect the decline in the Black population in these neighborhoods.

As the region’s racial and ethnic diversity increases and the demographic mosaics of our neighborhoods shift, we wonder whether the changes are increasing or decreasing our opportunities to connect with people who don’t look like us or share our cultural backgrounds. Evidence suggests that cultural diversity contributes to economic growth by introducing new ideas and cultural experiences into society and workplaces, resulting in more creative problem solving. Our increasing diversity is an asset to be embraced and an important ingredient in our connective tissue.

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The Power of the Pyramid


Population pyramids have long been used to visualize the age and gender structure of societies. The shapes of these pyramids are largely determined by fertility, mortality, and migration. By dividing the population by sex (by convention, males are on the left of the vertical axis and females on the right) and stacking the age statistics (typically in 5-year cohorts with the youngest at the bottom and the oldest at the top), we get a snapshot of the relative age and sex balance of the population as well as a glimpse into the future — if people stay where they are. Indeed, before advances in public sanitation and medicine in the late 19th century, the globe’s population was the classic pyramidal in shape. In a growing number of countries, that is no longer the case.

In its 1870 Statistical Atlas, the U.S. Census Bureau created population pyramids for each state and territory for both the native and foreign born populations to paint an often dramatic picture of the rapidly growing nation. The population of Oregon, like the global population, was markedly young but what is particularly striking during this period, is the imbalance of young males to females in the resource extraction states of the West: Wyoming, Arizona, Montana, Idaho, Dakota, and Colorado (figure 1). This gender imbalance is even more striking in certain foreign-born populations. The Census Bureau made special note of the Chinese who were, among other activities, heavily involved in building railroads across the American West (figure 2).

Today, population pyramids are most widely used to visualize the contrast between the age distributions of underdeveloped and many of the world’s most highly developed countries. Zambia’s 2010 population (figure 3), which is dominated by those under 20 years of age is typical of population pyramids for nations before the 20th century and like many African countries has been significantly impacted by the HIV/AIDS epidemic. 

In contrast, the populations of Japan, Korea, and western European countries are markedly older. In 2012, over 24 percent of Japan’s population was over 65 years of age (figure 4). According to the 2010 Census, 12.7 percent of the U.S. population was elderly.

In fact, aging nations face significant challenges because there are fewer people of working age (typically considered 15 to 64 years old) relative to those 65 and older. A 2012 United Nations study states that Japan is projected to have the highest old age dependency in the world by 2050 with 72 elderly (aged 65 and older) for 100 working age people. The same study projects that there will be 36 elderly per 100 working age people in the U.S.

The age and sex composition of the population in the U.S. in 2010 (figure 5) is described by demographers as “stationary” which means that both the fertility and mortality rates are low. Oregon’s population pyramid (figure 6) reflects the national trend although Oregon’s population is slightly older. If fertility and mortality rates continue to trend down, without the in-migration of young families with higher fertility rates, these populations will age.

Population pyramids may also be used to visualize the age and gender characteristics of cities. In comparison with the nation and the state of Oregon, Portland and Beaverton (figures 7 and 8) have noticeable bulges in the 25 to 34 year old age cohorts with populations in those age groups at 19.6 percent of the total for Portland and 18.3 percent of the total for Beaverton as opposed to 13.6 percent of the total for Oregon and the U.S. 

Portland and Beaverton’s elderly populations are considerably lower than Oregon’s. In 2010, nearly 14 percent of Oregon’s population was 65 and older but in Portland and Beaverton the elderly populations were 10.3 percent and 10.5 percent respectively. For comparison, Miami (figure 9) which is considered a mecca for seniors had an elderly population of 16 percent in 2010.

Visualizing Neighborhood Character

Because the decennial Census data are available in very small geographic areas (Census blocks are about the size of a city block), they can be reaggregated into new geographies. The  population Research Center at PSU has done this for neighborhoods in Portland and Beaverton using the 2010 Census data. And because these data are broken out by age and sex, we can create population pyramids from them that have the ability to tell us something about the character of these communities.

The map on page 16 displays population pyramids for Portland’s neighborhoods. It is interesting to consider the shapes of the population pyramids relative to each other. These  neighborhood pyramids represent the full gamut of population distributions from those that are distinctly youthful to those that are aging.

At opposite ends of the spectrum are several neighborhoods in the inner northwest and southwest of Portland including Hillside, Arlington Heights, and Bridlemile that are clearly aging, and many neighborhoods in East Portland such as Powellhurst-Gilbert, Mill Park, and Centennial that are distinctly young (figure 10). These East Portland neighborhoods are some of the region’s most ethnically and racially diverse with relatively large populations of recent immigrants, many of whom have larger families.

Alameda’s population pyramid (figure 10), which is sparsely populated by those aged 20 to 35, is characteristic of several of the historic, elite streetcar neighborhoods on Portland’s inner eastside. These neighborhoods, known for their high quality residential architecture and arbored streetscapes, are seen as highly desirable places to live. In addition to Alameda, they include Irvington, Laurelhurst, and Eastmoreland, neighborhoods that over the last decade have become unaffordable for most young families.

Sunnyside (figure 10), on the other hand, is typical of many of Portland’s hipper, more central neighborhoods with population bulges for the 25 to 35 year old cohorts that are absent in Alameda. These include Buckman, Richmond, Hosford-Abernethy, Eliot, King, Brooklyn, Sellwood-Moreland, and Woodstock among others.

Clearly, neighborhoods that are home to institutions of higher education such as Downtown (PSU), Homestead (OHSU), Reed (Reed College), Collins View (Lewis and Clark), and University Park (University of Portland) have large college-aged populations.

And, while the overall population of the city tends to break out evenly between men and women except in the most elderly cohorts (which tend to have larger numbers of females), we can see that in Old Town/Chinatown (which has the highest number of single-occupancy residential facilities in the region) and Sunderland (near the airport and home to the city-recognized
homeless community, Dignity Village) the populations are overwhelmingly male.

Like Portland, Beaverton’s neighborhoods are markedly different from each other in terms of their age distributions (figure 12). Vose, Central Beaverton, and Five Oaks/ Triple Creek have the youngest populations. Vose, in particular, is notable with nearly 9 percent of its population under the age of five. And for each of these neighborhoods the largest adult cohorts are between 25 and 34 years of age. Central Beaverton is remarkable in that 46 percent of its population is in these cohorts.

Sexton Mountain’s population pyramid, and to a lesser extent South Beaverton and West Slope, contracts where the first set of neighborhoods described above bulges. The 25 to 34 year old cohort makes up 11.5 percent of Sexton Mountain’s population while nearly 27 percent of its population is between 40 and 54 years of age.

Highland’s population pyramid is nearly columnar and with its largest age cohorts (at nearly 15 percent of its population) between 50 and 60 years of age. What is remarkable about Denney Whitford/Raleigh West is its large percentage of extremely elderly residents. Over 11 percent of its population is 85 or older. In fact, 37.5 percent of its population in 2010 was 65 and older. This sort of distribution at the neighborhood level is indicative of the presence of assisted living, continuing care, and nursing home facilities.

Some Final Observations

There are a host of factors that play into where people live including the cost of housing and housing type, the quality of schools, the availability of jobs, and the proximity to parks, nature, services, cultural activities, major arterials, and transit. These visualizations not only tell us something about the community members who live there and help us to anticipate what their needs may be, but also help to remind us of the reality that neighborhoods across the region are very different with regard to the ages of the people who live there and sometimes even the male to female balance within them.

Meg Merrick, coordinated the Greater Portland Pulse regional indicator project as well as the Community Geography project (renamed Neighborhood Pulse) of the Institute of Portland Metropolitan Studies at Portland State University (PSU).  

The Geography of Publicly Subsidized Affordable Housing

…the public dialogue around housing in Portland has reached critical mass and placed a renewed focus on the public resources available for affordable housing development.

This is how the State of Housing in Portland report published last fall begins. While much of the press, both locally and nationally, has focused on the cost of housing in Portland, the availability of housing that is affordable to all Oregonians is being called into question throughout the state. Over the past year, a group of people representing Housing and Urban Development (HUD), Oregon Housing and Community Services (OHCS), Metro, Catholic Charities, Network for Affordable Housing (NOAH), Transition Projects, and other housing and social service agencies met to discuss the creation of a statewide, comprehensive affordable housing database which could be used by case workers, program administrators, and policymakers.

As a result, the OHCS, with the assistance of a variety of partners, is in the process of finalizing the state’s first-ever affordable housing dataset. While the statewide data are not yet ready for public use, Metro, in concert with this effort, has developed a similar database for Clackamas, Multnomah, and Washington counties in Oregon and Clark County in Washington. In this edition of the Periodic Atlas we highlight Metro’s recently published inventory of publicly subsidized affordable housing. We put the locations of this housing stock in the context of  Portland’s market rents; we look at the significance of the sponsor types (government, nonprofits, and for-profits) in each county; we then take a closer look at the top providers in
downtown and northeast Portland where this type of housing is most concentrated; and we close by looking at where this housing is in relationship to high performing schools and public libraries.

Figure 1

Source: Metro

Figure 2

Figure 3

Figure 4

Figure 5


Source: Metro

Some Context

In a tight housing market with rapidly increasing housing costs, renters are particularly vulnerable to often unpredictable rent hikes. This is especially a problem for households who are already paying rents at the maximum of what they can afford. Figure 2 uses data from Portland’s State of Housing Report (2015) showing the average rents for a two-bedroom apartment (a preferred size for households with children) per “neighborhood” (groups of neighborhoods) in 2015. The neighborhoods in shades of gray were considered unaffordable for the average household in Portland. What is apparent from the map is that the least affordable neighborhoods are those that are centrally located while the most affordable neighborhoods tend to be located farthest from the downtown core in North Portland and East Portland.

Figure 3 overlays Metro’s affordable housing data over these areas. The size of the circles indicate the number of units at each site. What is clear here is how important this housing (much of which was either developed or purchased decades ago) is in terms of its access to services, jobs, and transit. Without these publicly subsidized affordable units, given the average
market rents, there would be few opportunities for low-income households to live in these neighborhoods.

According to the State of Housing Report, since the third quarter of 2014, Portland’s rents increased an average of 8–9 percent or approximately $100 per month over the prior year. But this increase has not been felt everywhere. In figure 4, the red areas experienced the largest rates of increase while the blue areas saw decreases. In the yellow areas, rents were relatively
stable during this period. Again, we can see how important the existing stock of publicly subsidized affordable housing is, especially in areas where the rates of increase are highest.

The Geography of Sponsor Type

Sponsors of affordable housing can be classified into three types: government, nonprofit, and forprofit enterprises. The for-profit GSL Properties, Inc. is the second largest provider of publicly subsidized affordable housing in the region. With 3,297 units at 18 sites (according to Metro), only Portland’s housing authority, Home Forward, with 5,743 units in 101 sites, offers more. GSL Properties’ sites are located across the region from Troutdale and Gresham, to downtown Portland, to Hillsboro (figure 5).

Government sponsored affordable housing is present in all counties in the region and dominates in Clackamas and Clark counties. The Vancouver Housing Authority ranks fourth in the region, after Home Forward, GSL Properties, Inc., and all of the unknown sponsors (see figure 6). The Housing Authority of Clackamas County is ninth in the region while the Housing Authority of Washington County places 10th. In Clackamas County, its housing authority is the single largest provider of publicly subsidized affordable housing—there, the nonprofit sector has a much smaller presence than in either Multnomah or Washington counties.

Figure 7 displays the geographic distribution of the 11 largest providers (not including the unknown providers). Quatama Housing LP’s apart ment complex in Hillsboro (largest gray circle) is the outlier. This single site contains 711 units making it the 12th largest publicly subsidized affordable housing provider in the region. It is also interesting to note that some providers have crossed the Columbia to serve both Oregon and Washington. This is the case for REACH CDC and Innovative Housing Inc. 

Figure 7

Clearly, the most intense clusters of these units are in downtown (including Old Town/ Chinatown) and inner northeast Portland. Figures 8 and 9 provide a closer look. While the providers
in downtown serve a variety of clientele (including the aged and disabled) and housing types (including single-room occupancy buildings) what stands out in northeast Portland is the large
number of widely dispersed low density units (predominantly single-family) that are principally owned and managed by PCRI (Portland Community Reinvestment Initiatives). PCRI was born out of the housing crisis of the 1980s recession when unscrupulous lending practices forced many homeowners out of their homes. City and county leaders and community members decided to form a nonprofit that would acquire endangered homes, help affected families secure conventional mortgages to buy back their homes, and purchase other defaulted properties for long-term affordable rentals. Many of the properties that remain in PCRI’s portfolio are in some of the most rapidly gentrifying neighborhoods in Portland abutting affluent Irvington and Alameda. This housing stock has been instrumental in allowing some of the original residents to remain in these neighborhoods that have become unaffordable.

However, widely dispersed singlefamily houses, duplexes and four-plexes are more costly to manage and maintain than more concentrated housing options. Home Forward, over the years, has moved away from dispersed low density housing in favor of more concentrated approaches.

Figure 10

Source: Metro; ODE

Figure 11


Source: Metro; Regional Equity Atlas 2.0

Access to Opportunities

One of the most important drivers, in terms of housing choice for households with school-aged children, is access to good schools. Statewide test scores, which provide a snapshot of how schools are doing relative to others in the state, are often used by parents to identify good schools.

Figure 10 shows the percentage of students in every public school in the region who have reached levels 3 or 4 (level 3 is the benchmark for proficiency) Smarter Balanced scores for  English/Language Arts for the 2014/2015 academic year. Green points represent schools where 75 percent or more of the students reached proficiency. The yellow to red points, indicate
schools where the percentage of students attaining proficiency in English and Language Arts was 74.9 percent or less. The map also includes a quarter-mile street grid from each of the publicly subsidized affordable housing sites to show their proximity to schools (not the catchment areas).

 While it appears that some households living in these units may be able to take advantage of high performing schools by this metric, many cannot. The number of high performing schools (green points), especially in Washington and Clackamas counties that stand alone, far from the locations of this housing is notable.

 Finally, many low income households lack computers and internet access. Access to the internet has all but become a requirement for people to stay informed, remain connected, complete schoolwork, and find and apply for jobs. Public libraries offer free access to computers and the internet, but access to libraries may pose a barrier. Using data from the Regional Equity Atlas 2.0 mapping tool, figure 11 shows the relationship between the locations of publicly subsidized affordable housing sites and public libraries (census tracts identified as having very good access are those in which there is a library within a quarter mile; people living in census tracts with extremely poor access must travel a mile or more to a public library). While library access is good in many of Portland’s central neighborhoods and other downtown areas in the region, access is quite poor in many areas, especially in East Portland, unincorporated areas of Washington County, and in Clark County. Many residents of these affordable housing units must travel considerable distances to get to a library. As land becomes more costly in  well connected neighborhoods, access to important services and opportunities will become more difficult. 

Meg Merrick, coordinated the Greater Portland Pulse regional indicator project as well as the Community Geography project (renamed Neighborhood Pulse) of the Institute of Portland Metropolitan Studies at Portland State University (PSU).  

An Emerging Contradiction: Non-Farm Activity within Exclusive Farm Use Zones


Oregon’s land use policy plan has been lauded nationally as one of the most successful conservation strategies for agricultural and forest lands.((Kline, “Forest and Farmland Conservation Effects of Oregon’s (USA) Land-Use Planning Program”; Nelson, “Preserving Prime Farmland in the Face of Urbanization”; Tulloch et al., “Integrating GIS into Farmland Preservation Policy and Decision Making.”)) Urban growth boundaries (UGB), which limit urban development within the UGB area, are a key component of this statewide land use system to mitigate sprawl. In combination with UGBs, exclusive farm use (EFU) zones facilitate and protect farm production by restricting development that may potentially conflict with agricultural practices and offering tax incentives for farming. However, this restriction is not absolute, as a variety of non-farm-related uses and dwellings are legally allowed within EFU zones. The allowed non-farm activities are diverse, and delineating their impact on farm operations has been difficult due to the lack of data to measure these phenomena. In this edition of the Atlas, we mapped the locations of non-farm permits collected and maintained by the Department of Land Conservation and Development (DLCD), from 1993 to 2015 in the northern Willamette Valley. We hope this work will contribute to a dialogue among various actors and researchers interested in the growth management of Oregon.


While Oregon’s Statewide Planning Goal 3 explicitly states “agricultural lands shall be preserved and maintained for farm use,” it also allows counties to “authorize farm uses and nonfarm uses defined by commission rule that will not have significant adverse effects on accepted farm or forest practices.”((DLCD, “Goal 3: Agricultural Lands.”)) It seems contradictory that non-farm activities are permitted to function within EFU zones, but there are a variety of reasons for their existence. Some non-farm operations, including processing plants, storage facilities, agri-tourist events, and other accessory uses, sustain the agglomerative properties of the local agriculture industry and serve as complementary, if not essential, elements to farming practices.((Lynch, Economics and Contemporary Land Use Policy; Lynch and Carpenter, “Is There Evidence of a Critical Mass in the Mid-Atlantic Agriculture Sector between 1949 and 1997?”; Nelson, “Preserving Prime Farmland in the Face of Urbanization.”)) Another reason is that some activities, such as solar farms and wind turbines, require open space and thus, contend with farming demand for EFU lands.((DLCD, “2014-2015 Oregon Farm & Forest Report.”)) Lastly, it’s worth noting that not all land within EFU zones are conducive to farming because of soil quality or gradient. Ideally, non-farm uses and dwellings are relegated to non-productive farmlands as long as they don’t conflict with nearby farms.((DLCD.)) However since the creation of the first EFU zone in 1963, the number of allowed non-farm uses has increased from six to over fifty uses today.((DLCD.)) The gradual addition of uses over the decades has been a political process and a compromise with farmers and landowners, who want to increase the economic return of their land. Nonetheless, there is concern that the growing number of non-farm uses and dwellings may eventually undermine the critical mass of agricultural land, or the minimum inventory of land needed for farming to remain sustainable. Isolated operations may have little to no impact on farming practices individually, but concerns focus on the cumulative impacts of these activities.

Farm operations require open space to function because some farm activities (e.g. late and early work hours, farm machines on streets, animal noises and smells, and weed and pest management) may conflict with the day-to-day activities of neighboring, non-farm businesses and residents. Conversely, in addition to converting farmland to other uses, nearby dwellings and non-farm operations can produce traffic, pollution, and complaints of their own that negatively impact the longevity and production of nearby farms.

While these processes have been discussed at length by researchers and farm advocates, we know relatively little about how they function on the ground. Questions surrounding the extent of these operations, their locations, and their overall impact on farming practices have not been thoroughly addressed. By analyzing the spatial distribution at a local scale, this work takes an important step towards deepening our understanding of the cumulative impacts of non-farm development. Using administrative data maintained by DLCD, we’ve geocoded permits for dwellings and uses from 1993 to 2015 in the northern Willamette Valley, Oregon’s agricultural heartland. An important note is that the permit data in their current form do not capture the entire history of non-farm development in the region, as illegal operations and structures are not recorded. By their nature, these permits can only inform us of approved development at specific points in time, not what is currently operational. Furthermore, we are not arguing that these phenomena produce a net negative or positive impact on farming practices, nor is it the intent of these maps to illustrate such. The purpose is to highlight the presence of these activities, identify broad areas where they have clustered, and generate questions for future research and practices.



Permitted activities vary from county to county and are not codified in a standardized method, making it difficult to measure and track. Therefore, we recoded and geocoded 622 cases into four broad categories: accessory use, utility and communication facility, other use, and agritourism and events, as illustrated in Figure 2.

Accessory uses represent activities that complement or are necessary for farming production and made up roughly 16 percent, or 97 cases, of permitted uses. Utility and communication facilities include wind turbines, power plants, and cell towers, making up roughly 20 percent, or 126 cases, of permitted uses. We’ve isolated these activities from “other uses” because they are generally public amenities that require open space to operate, opposed to private commercial activities. Other uses was the broadest category including private parks, home businesses, personal airports, and many other activities not related to farming. A plurality of permits fell under other uses and made up roughly 39 percent, or 242 cases, of permitted uses. A large number of these cases clustered outside the city of Woodburn (Figure 4).

Finally, agri-tourism and events represent farm stands, viticulture operations, bed and breakfast establishments, and wedding venues. We chose to isolate agri-tourism because of its unclear relationship to farming. While agri-tourism may help individual operations, there is not a consensus on its impact on farming practices as a whole. On the one hand, agri-tourism, such as u-pick stations, farm stands, and wine tasting stations, produces supplementary income streams for farm operations.((DLCD; Haugen and Vik, “Farmers as Entrepreneurs”;
Searle, “A Comprehensive Valuation of Agricultural Lands: A Perpetual Investment in Oregon’s Economy and Environment.”)) On the other hand, it can also create residential traffic and development that can negatively affect other farm operations that have not adopted these practices. By codifying these cases into a different category, we hope to highlight areas where these events are occurring. Agri-tourism made up 25 percent, or 157 cases, of permitted uses with a large majority concentrating in Yamhill County near Dundee (Figure 3).


In 1993, the Oregon legislature permitted non-farm dwellings to be built on less productive land within EFU zones.((DLCD, “2014-2015 Oregon Farm & Forest Report.”)) Permitted dwellings fall into seven categories, some defined more clearly than others:

  • Accessory farm dwellings: Residences for farm workers not related to the operator.
  • Dwelling replacements: Residences that replace dwellings that have been removed. It is not clear what type of residence is removed and what is being built.
  • Lot of record: Residences that can be built under the condition that the land has been under the same ownership prior to 1985.
  • Non-farm dwellings: Residences, unrelated to farming, which are approved on less agriculturally productive lands.
  • Primary dwelling: Residences for farm operators. 
  • Relative farm assistance: Residences for the operators’ relatives who will work on the farm. However, there is no requirement that a relative occupy the residence or that the residence be used for farm-related purposes once built.
  • Temporary hardship: Residences constructed concurrently with a primary dwelling for a family member enduring a medical hardship. The state does not track the removal of these temporary dwellings.

With the exception of accessory farm and primary farm dwellings, which make up 300 out of the 2,400 dwelling permits (13%), most of the dwellings are either unrelated to farming or are not explicitly farm related, with a large concentration located near the Yamhill and Washington County border (Figure 6). Our binary classification is deliberate, and highlights a broader issue that the official dwelling types in their current form don’t tell us enough about the nature of development within Oregon’s agricultural lands. 


With permit data as our main source to spatially track these phenomena, the information we’ve presented is only a glimpse of non-farm development that has occurred in the northern Willamette Valley. It is likely that the maps we have presented are the most conservative scenario of non-farm development, since they only include permitted uses within a certain time frame. One take-away from our research is that we need more tools and better data to track the extent and spatial distribution of non-farm uses and to evaluate their cumulative impacts. Better-detailed data with standardized classifications for non-farm development is necessary for better monitoring and evaluation. Site visits would also help us understand more about the varying impacts of different uses. We are hopeful that this work will contribute to a more informed dialogue about the cumulative impact of non-farm uses.

Figure 3. Dundee area use cases
Figure 4. Woodburn area use cases
Figure 6. Dwelling cases along the Washington-Yamhill County boundary
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Nick Chun earned a Master of Urban Studies degree at Portland State University, where he serves as Forecast Program Manager for PSU’s Population Research Center.

The Geography of Future Housing


Over the past few years, the housing market has responded to strong demand driven by population growth; new multifamily housing is coming online rapidly.

A strong regional economy, rising wages, and rapid population growth throughout the Portland region have driven rapidly increasing demand for housing. Housing development lagged the region’s economic recovery, as permitting activity fell to a 20-year low in 2009 and 2010, leading to acute shortages and rapidly rising rents and home prices as the economy recovered. Over the past few years, the housing market has responded and new housing, particularly multifamily housing, is coming online rapidly.

In this edition of the Periodic Atlas, we provide a glimpse of housing construction that has been permitted over the past few years, with a focus on multifamily housing. Focusing on recently-permitted units provides a perspective on how the newest housing is and will be different from existing housing. The type of housing that will be built, where it is built, its characteristics, and its price will shape the Portland region in the years to come.

To provide additional perspective, we examine where multifamily housing is being permitted in relation to fast growing population areas, and areas with the greatest concentration of people of color. Finally, we explore a few recently completed multifamily projects to provide a better understanding of the characteristics of multifamily projects coming on line.

We are drawing from a number of data sources in this Atlas, including Metro’s multifamily housing inventory (RLIS, November 2016), multifamily unit data gleaned from Clark County’s taxlot dataset, demographic data from the Census Bureau, and permit data from Construction Monitor. Although there are different sources of permit data with different strengths and weaknesses, they draw from the same source: the permitting offices of local jurisdictions. The Portland State University Population Research Center and the Institute of Portland Metropolitan Studies cleaned, checked, augmented, and geocoded the Construction Monitor raw data and joined it with the other data sources to create the maps in this version of Atlas.

Historical data show that over 90 percent of permitted projects are eventually completed, although the lag time varies from six to 18 months. Thus, many of the permitted units shown in these maps, which are from 2014 through October of 2016, have been completed; but at least 7,500 units in Clackamas, Washington, and Multnomah counties that have been permitted since 2014 had not been completed as of the date of the last multifamily inventory.

Figure 1: Building Permit Densities, January 2014-October 2016

Source: Construction Monitor, Inc., Metro

L Hawkins apartments at 1510 NW 21st Avenue were completed in 2015. This market-rate apartment development in the Slabtown neighborhood of Northwest Portland contains 113 units, with studio, one- and two-bedroom units. A two-bedroom two-bath unit rents for about $3,000 per month. Thus, a couple or family would need to earn a combined income of about $120,000 per year to consider this apartment affordable.

© 2015 Google Inc, used with permission. Google and the Google logo are registered trademarks of Google Inc.

Density Downtown

Figure 1 shows all permits issued by city development departments for housing in January 2014 through October of 2016 in Clackamas, Multnomah, and Washington counties in Oregon, as well as Clark County in Washington. The dark blue areas indicate the most dense areas of multifamily permitting (150 units per square mile or greater). Blue rings show areas with less dense multifamily permitting. Yellow areas show dense single-family permitting, and the yellow rings show areas of less dense single family permitting. Scattered multifamily units are shown by red squares; scattered single family units are shown with grey squares.

The permitting pattern is as expected by any casual observer watching construction cranes rise in the region. The densest clusters of multifamily permits appear in close-in areas of Portland, in Washington County near Intel and Rock Creek, and in Bethany. There are also several clusters of multifamily permitting in Clark County, including Vancouver’s central city, and in the Bennington neighborhood near SE Mill Plain Blvd. and SE 164th Ave.

Dense permitting for single family units overlap the areas with the greatest multifamily density in areas such as Bethany, Hillsboro, and Happy Valley. Less dense clusters of single family units occur throughout the region, mostly outside of the urban core, where scattered infill is the only source of single family housing.

Figure 2: Single and multifamily units permitted, 2014-2015

Source: Construction Monitor, Metro Multifamily Inventory

Figure 2 shows the number of permitted multifamily units by county and year. Keep in mind that 2016 is only partial-year data (January to October). The boom in permitting that began in 2014 followed a crash in newly-permitted units during the recession that left the housing pipeline nearly empty. Our comparison of these permit data with Metro’s latest multi-unit inventory shows that more than half of the housing units permitted since 2014 still have not yet been completed. Over 7,000 of the new units in the three counties that have been permitted since 2014 have not yet become available, with the greatest number in Multnomah County, where over 6,500 units have yet to be delivered to the market. 

How does the location of these units compare with areas that have experienced strong population growth? And what do these permits tell us about where population growth will occur in the future?

Figure 3: 2006-2010 and 2011-2015 population change

Source: Construction Monitor, Inc., Metro

The D Street Salal apartments at 17101 SE Division were completed in 2015. The development by PHC Northwest was designed to fill the vast need for units affordable for families and individuals with lower incomes. “Mini” units rent for $495, and are therefore affordable to people with incomes of about $20,000 per year. The one-bedroom units rent for $719, and two- bedroom units are $859 per month. Thus, the two-bedroom units are affordable to families that earn about $34,000 per year.

© 2015 Google Inc, used with permission. Google and the Google logo are registered trademarks of Google Inc.

A Growing City

Figure 3 shows the census tracts with the highest rates of population growth from 2010 to 2015 in relation to the permitted units. The areas with the greatest population growth are dark green. Some of these areas, Northwest Portland, the South Waterfront, Bethany, and Hillsboro show strong growth in the past as well as a concentration of permits that will lead to continued growth. Other areas with significant permitting activity but no significant population growth in the past five years will likely see significant increases in population as those units come on line and fill. 

Race to the Periphery

Figure 4 shows the permitted units in relation to geographic areas with high concentrations of people in three racial or ethnic identities according to the U.S. Census: Asian, African American, and Hispanic/Latino. The Portland region as a whole is diversifying; from 2000 to 2010 the Hispanic population nearly doubled, the Asian population grew by 50 percent, and the African American population grew by about 35 percent. But the region in 2015 was still 74 percent white, and the map shows that people of color are concentrated in specific areas of the region, many of them on its periphery.

Some areas of racial or ethnic concentration – in particular the Asian areas in Washington County and the Latino areas of Beaverton –are experiencing significant new multifamily projects. There are also many new multifamily projects in the North Williams corridor, a traditionally African American area of Northeast Portland that has already experienced rapid and significant changes in its racial makeup. But most of the recent multifamily activity has occurred in areas that are mostly white. 

As these new units come online, the region’s housing market may experience some relief from the rapid rent increases we’ve experienced over the past few years. According to Co-Star, vacancy rates in the region have returned to normal as the inventory has climbed. Whether the increased supply improves affordability depends on population and household growth in the region. We will continue to monitor changes in our population, housing, and economy to understand how these forces will shape our region.

In the Bethany area, one of the newest developments is West Parc at Bethany Village. The 149 units completed in 2015 include studio, one-bedroom, two-bedroom, and three-bedroom one-bedroom, two-bedroom and three-bedroom units, making them appropriate even for larger families. The three3-bedroom units rent for from $1,700 to $2,700 per month, meaning that a family could afford the apartment with an income of between $68,000 and $108,000 per year.

© 2015 Google Inc, used with permission. Google and the Google logo are registered trademarks of Google Inc.

The 15 West Apartments in Vancouver is a five-story affordable housing project with 120 one-, two-, or three-bedroom units. The income-restricted units are designed to be affordable to residents making no more than 60 percent of the area’s median income. Studio units cost $714; the largest one-bedroom unit unis is $754, a two-bedroom is $903, and a three-bedroom unit is $1,040. Thus, a three-bedroom unit is affordable to families making $41,600 per year.

© 2015 Google Inc, used with permission. Google and the Google logo are registered trademarks of Google Inc.

Sheila Martin is Director of the Institute of Portland Metropolitan Studies and the Population Research Center at Portland State University. She directs the Institute’s community-oriented research and service activities.

Randy is the Community GIS Project Leader at Portland State University’s Institute of Metropolitan Studies, and was most recently published as lead cartographer in Portlandness: a Cultural Atlas.  Currently, he heads the IMS Neighborhood Pulse project.

The Geography of the Commute


Transit use is fraught with perils . . . one delay of a connection can cause a worked to be . . . fired.

It is a common misperception that low-income populations are transit-dependent or typically do without a car because it is too expensive. While much larger proportions of low-income populations use a mode of transportation other than a personal automobile to commute to work, a majority of them still use a personal automobile. In this edition of the Periodic Atlas, we looked at commuting as it relates to people of color and low-wage workers using the most recent reliable Census data as well as data from the Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LEHD-LODES).

Commute Mode by Race and Income 

Generally, commute mode shares in the Portland metropolitan area are similar to most other regions in the United States where commuting by automobile (driving alone or carpooling) forms the dominant share of commuting. Across all races and ethnicities, populations in poverty commute by transit and walking to a greater extent than their peers not in poverty. White populations not in poverty have the lowest share of commuting by transit. Meanwhile, African-American populations have the highest share using transit (19%) with African-American populations in poverty using transit to get to work at a rate (33%) twice that of any other population group. The differences in commute mode by race/ethnicity and poverty status are important for understanding commute patterns considering the level of racial/ethnic and class segregation in the Portland region.

Figure 1: Commute mode by race, Hispanic origin and poverty level, Portland-Vancouver-Hillsboro MSA

Source: IPUMS-USA, University of Minnesota, www.ipums.org: American Community Survey 2015 5-year estimates: Clackamas, Multnomah, and Washington Counties, workers 16 and older, and employed outside of the home.

Notes: Dashed line = moderate reliability. Solid line = low reliability. Poverty defined by 150% of federal.

Employment and Commuting

Commuting is also impacted by where employers locate. Employers make location decisions for many reasons. Some employers prioritize their location for their employees while others put the priority on transportation access for freight or customers. Inevitably though, employees will commute to a job from all over the region regardless of the transportation amenities available.

To examine this effect, we used LODES data to identify the Census tracts with the highest density of employment with high proportions of low-wage workers employed there. To have comparison cases, we selected employment tracts based on their dominance for a particular kind of work. The following analysis focuses on the Census tracts that contain the airport, the Central Eastside Industrial District (CEID), the Fred Meyer Distribution Center (FMDC) in Milwaukie, Washington Square Mall (WSM), and Vancouver Mall (VM). For each location, we analyzed the proportion of low-wage (earning less than $1250/month) workers located within three, six and ten miles (straight-line distance) of the Census tract as well as the various transportation options leading to and from these locations.

Table 1. Proportion of Low-Wage Workers by Distance

Source: U.S. Census Bureau. 2016. LODES Data. LongitudinalEmployer
Household Dynamics Program. http://lehd.ces.census.


Washington Square Mall and Vancouver Mall

WSM’s tract contains nearly 12,000 employees while VM’s tract contains around 6,700. That the tract is retail-centered was reflected in nearly 30% of employees being classified as low-wage. Nearly half of low-wage WSM workers live farther than ten miles away compared to 40% of VM workers. However, a much larger proportion of low-wage workers live close to VM. This is likely an effect of the Columbia River presenting a barrier to workers in Oregon seeking employment in Washington. Both malls act as transit hubs presenting employees with relatively easier access to jobs from nearly any direction. However, for WSM, transit options diminish precipitously south and west of the mall while the same is true north and east of VM.


Thirteen percent of the nearly 30,000 jobs in the airport tract were classified as low-wage. Table 1 shows that, generally, low-wage workers live about the same distance from work as all other workers with only a slightly higher proportion living beyond ten miles.

Transportation to and from this tract is extremely limited. Only the MAX Red Line goes to the airport and the closest bus lines are along NE Killingsworth Street west of NE Eighty-Second Avenue and on Columbia Boulevard west of NE Forty-Seventh Avenue. This leaves many of the businesses in the tract completely inaccessible by transit, and walking from transit to any of the employers requires traversing streets laden with freight traffic and often lacking sidewalks.


Central Eastside Industrial District

The CEID contains nearly 17,000 jobs. This tract is a highly exceptional case in that over three-quarters of low-wage workers employed in this tract live within ten miles of work. This is likely due to its centralized location in the region even when considering housing costs in nearby neighborhoods would make it very difficult for such workers to live nearby. A third of the workers employed in the CEID live within three miles, the most expensive housing locations in the city.

Transportation to the CEID is convenient for most travel modes. Most cross-town bus lines in the city go through the tract or at least stop in downtown leaving riders a relatively easy walk from work. Parking, however, is in very short supply here.

Fred Meyer Distribution Center

The FMDC is another industry-heavy tract with around 10,000 jobs. This tract had the highest proportion of low-wage workers living more than ten miles away. Like the airport tract, transit options to this location are sparse with one bus line going through it. However, this bus line has half-hour waits between buses during peak morning hours heading toward downtown and then changes to one-hour waits. Headed away from downtown, the shorter waits occur in the evening. Workers south and west of the tract have virtually no transit connections to work. Walking is also difficult as the tract is surrounded by highways and the Clackamas River.

Figure 9. A comparison of two commutes from SE 136th Ave and SE Division St

Highlighting Two Scenarios

We also looked at possible transit commutes that workers living in the area of SE 136th Avenue and SE Division Street may face when trying to get to their jobs in the airport or Fred Meyer Distribution Center tracts. This location had a concentration of workers for both work places.

Workers trying to get to jobs around NE Eighty-Second Avenue and NE Alderwood Road, face a nearly ninety-minute commute that requires them to transfer twice on transit. Around twenty minutes of their travel time is spent waiting at the transfer points. At the end of their journey, they are faced with a three-quarter-mile walk from the Target in Cascade Station along a road where the sidewalks disappear before they reach their destination. If they miss a transfer, they have a fifteen-minute wait for the next train. This trip is similar if done at 8:00 a.m. or 8:00 p.m. While this area is near the airport, it is not well served by the Airport Max line.

Workers starting their commute to the FMDC at 8:00 a.m. also face a trip that will take over ninety minutes and two transfers. Unlike their airport tract peers, nearly half of their travel time is spent waiting for buses (forty-five minutes). If they tried to get there at 8:00 p.m., though, the bus from Clackamas Town Center would no longer be running.

Figure 10. Commuters to many of the commercial and industrial jobs in the PDX tract face heavy-traffic streets and long walks wherever they disembark.

Understanding the Cases

Transit use is fraught with perils. As workers live farther from work, their chances of having to transfer buses or trains increases. One delay of a connection can cause a worker to be late to work. This can lead to them being fired since low-wage industries are often less amenable to tardiness among their employees. Transit also takes time. A low-wage worker living close to work may not experience much time difference between transit and driving but living ten miles or more from work creates considerable time differences. Low-wage industries also tend to use night-shift labor more than other industries. Transit schedules often do not coincide with night-shift schedules leaving workers to either find a different mode or wait hours for a bus.

Walking to work or walking from a transit stop can present many difficulties. Many locations around Portland lack sidewalks or convenient and safe places to cross busy streets. This presents a higher possibility of a person walking to work getting injured or killed by a driver.

Driving can severely strain a low-wage worker’s budget, but it also provides them a quicker and more convenient way to get to and from work and any other obligations they need to get to. As we see from the cases we presented here, driving may be the only reasonable way for low-wage workers to get to work.

Race and ethnicity also matter. Many populations of color and low-income populations are concentrating east of NE Eighty-Second Avenue, but low-income African-American’s dependence on transit presents a much larger problem when they move east of NE Eighty-Second Avenue. In this area of the region, their high rates of transit usage will be difficult to maintain, but so will their budgets if they switch to driving. Racial discrimination in the labor market may also mean they face a greater likelihood of being fired over tardiness related to missing a bus, and a more difficult time finding a job after such an instance. We must always remember there is an intimate linkage between housing, jobs, and transportation and we must always consider what that linkage means as economic and racial segregation still exist in the region.

Steven Howland is a Ph.D. candidate in the Nohad A. Toulan School of Urban Studies and Planning at Portland State University with a research focus on poverty, low-income labor, and transportation equity.