Moudon, Hess, Matlick, Pergakes
Pedestrian Location Identification Tools: Identifying Suburban Areas with Potentially High Latent
Demand for Pedestrian Travel
REVISED November 15,2001
Dr. Anne Vernez Moudon (corresponding author)
Professor of Urban Design and Planning, Architecture, and Landscape Architecture
University of Washington
Department of Urban Design and Planning, Box 355740
Seattle, WA 98195
Tel: 206-685-4057 Fax: 206-685-9597
Email: moudon@u.washington.edu
Dr. Paul Mitchell Hess
Research Associate, Department of Urban Design and Planning
University of Washington
Department of Urban Design and Planning, Box 355740
Seattle, WA 98195
Tel: 206-280-6321 Fax: 206-685-9597
Email: pmhess@u.washington.edu
Ms. Julie M. Matlick,
Pedestrian and Community Design Planner
Community Partnerships Program
Washington State Department of Transportation
Tel 360-705-7505 Fax 360.706.6822
Email: matlicj@wsdot.wa.gov
Mr. Nicholas Pergakes
Research Assistant, Department of Urban Design and Planning
University of Washington
Department of Urban Design and Planning, Box 355740
Seattle, WA 98195
Tel: 206-616-7308 Fax: 206-685-9597
Email: npergake@u.washington.edu
Word Count: 6,538
Keywords: Suburban pedestrian zones, latent demand for pedestrian travel, complementarity of
land uses, dominant land uses
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ABSTRACT
This paper describes planning tools developed for local and state agencies to identify locations that have
latent demand for pedestrian travel and are currently under-served with pedestrian infrastructure. Prior
research in the Puget Sound showed that approximately 20 percent of the suburban population lives in
dense, compact areas with latent demand for pedestrian travel. The tools are designed to enable agencies
to target capital investments in non-motorized-infrastructure to areas with the highest potential pedestrian
trips. They are a first step toward delineating suburban pedestrian zones.
A review of existing methodologies to identify areas with pedestrian travel demand precedes a
description of the two tools developed in this work. Both tools use Geographic Information Systems
(GIS) software. One of the tools benefits from high resolution parcel level data with specified land use
attributes. The other, however, relies on commonly available census block data and aerial photography,
which make it more labor intensive than the first tool and requires familiarity with reading urban form
and development patterns. The tools identify locations with potential for pedestrian travel based on two
attributes. First, the locations contain land uses that are functionally complementary, i.e. they are
commonly linked by travel. The land uses are dense residential development (travel generators) and retail
areas and schools (travel attractors). Second, the land uses are also spatially complementary, i.e. they are
close enough to each other to be linked by walking.
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INTRODUCTION
This paper reviews methods and presents tools developed to identify suburban areas that have potential
for pedestrian travel. It is based on previous research showing that approximately 20 percent of the
suburban population in the Puget Sound live in areas that, although designed for car travel, are dense,
mixed in use, and compact enough to support pedestrian travel (1, 2). The tools are intended to identify
these types of areas in order to maximize the benefits of new capital investments in pedestrian
infrastructure.
BACKGROUND
Pedestrian travel comes second only to automobiles as a mode of travel. Yet its share of total travel is
very small and continues to decrease—from 10 to less than 6 percent of all trips in the U.S. over the past
25 years (3). Other industrialized countries have a far larger share of non-motorized trips compared to the
U.S. — 11 percent in Canada, 20 percent in the UK, and 49 percent in Sweden. Yet despite low trip rates,
interest in supporting pedestrian travel in U.S. metropolitan areas has recently increased for several
reasons. First, transportation policies now seek to increase modes of travel other than the Single Occupant
Vehicle (SOV) to alleviate traffic congestion and to improve environmental quality. Pedestrian travel
serves these objectives because, given proper conditions, it can be used to substitute for short vehicle
trips. Second, the promotion of pedestrian travel may have broad impacts on people’s choice of travel
mode because walking trips are commonly used to access motorized modes, especially transit. Therefore,
if walking environments are poor, this may deter people from using transit. Third, increasing evidence
exists that pedestrian-friendly places are also desirable and safe environments that add to quality of life.
Finally, growing concern for high levels of physical inactivity point to walking and biking as healthy
activities with popular support (4).
Given proper support, walking appears to remain a strong alternative to driving. Distances driven
continue to be short—27 percent of all trips are under 1.6 kilometers (one mile) and an additional 13
percent are under 3.2 kilometers (two miles) (5, 6). Second, public transit trips are increasing in several
metropolitan areas where supporting infrastructure investments have been made (7).
At the same time, much work needs to be done to reverse the impact of policies and practices that
have supported auto travel at the expense of other modes. Fifty years of producing environments that lack
pedestrian infrastructure mean that, today, more than 60 percent of the U.S. population in large metro
areas live in environments that discourage or even prevent people from walking. These environments
have been termed pedestrian-hostile (8). Policies have also lead to limited budgets for infrastructure to
non-motorized travel at all levels of government. For example, only one percent of the State of
Washington’s transportation safety budget is directed to pedestrian infrastructure improvements—a
disproportionately low figure considering that pedestrian fatalities consist of 12-15 percent of all vehicle
related fatalities. Similarly, the share of federal transportation funds targeted to pedestrian travel remains
minuscule (9).
Yet policies have begun to shift. For example, federal policy statements on transportation system
design have directed state and local agencies to incorporate pedestrian facilities into all projects “unless
exceptional circumstances exist.” (10) Also, budgets for non-motorized infrastructure have consistently
increased at many levels of government over the past decade. yet these budgets will remain insufficient
Given the magnitude of the existing problem, however, these budgets remain insufficient to support
significant increases in non-motorized travel. Effective resource allocation at all levels of government is
essential, requiring, strategic planning and sophisticated tools. Specifically, investments in non-motorized
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transport should be carefully targeted and prioritized to areas that have the highest potential for
substantial increases in non-motorized travel demand. The tools presented in this paper are intended to
aid in this process.
CHARACTERISTICS OF ACTUAL DEMAND FOR PEDESTRIAN TRAVEL
Data on pedestrian travel in the U.S. are limited, making it difficult to target facility investments to where
they may be most needed (11). For example, Antonakos (12) reports on data from the Nationwide
Personal Transportation Survey and finds that “non-motorists” are likely to have the following
characteristics: to be under 21 or over 65 years of age, unemployed and without a driver’s license; to live
in a Central City and in a household with no car. National level data therefore indicate that people who
walk only do so by necessity. In contrast, local level data suggest that walking is common for people of
many age groups, with different types of personal characteristics, and in many types of urban and
suburban settings. On one hand, differences in mode share exist between different metropolitan areas—
the result of different periods of development, transportation infrastructure, transit service, and local
behaviors (13, 14, 15). On the other hand, marked differences in behavior also exist within metropolitan
areas. For example, walking trips account for about 20 percent of trip making by residents in some
medium-density, upper-income neighborhoods of Seattle—against a low 2.5 percent as the average of
King County which includes Seattle (16). In general, therefore, the few studies available that use
disaggregated data have shown that people other than the poor, the young, and the old walk, and they do
so in places other than the densest urban centers.
Our own research found substantial numbers of people walking in suburban areas that had not
been identified or recognized as areas with non-motorized travel (1). The areas, termed suburban clusters,
do not correspond to recognized suburban downtowns or centers (17). Instead, they are concentrations of
medium-density multifamily development in close proximity to medium and small-size retail areas (2).
Suburban clusters have land use patterns that can support walking, but they have poor pedestrian
infrastructure. Infrastructure deficiencies, including few sidewalks, difficult road crossings, and indirect
pedestrian routes likely limit the number of people that walk in these places. Neighborhoods with the
same density and basic land use mix, but with extensive pedestrian infrastructure were shown to have
three times as many pedestrians (18, 19). These differences in walking rates are an indicator of latent
demand for pedestrian travel in the suburban clusters.
Suburban clusters are common and suggest that a large latent demand exists for pedestrian travel
in suburban areas. Twenty percent of the suburban Puget Sound population (the 3 million-people
metropolitan area surrounding Seattle) lives in them, an unexpectedly high number considering recent
growth and the prevalence of sprawling land use patterns. The result of common planning and
development practices, suburban clusters are also likely to be common in the post-war areas of other large
metropolitan regions (2). Applying Puget Sound figures to metropolitan regions of over one million
people, as many as 15 to 20 million people may already be living in areas with land use patterns where
walking could be used as an alternative mode of travel for some trips. These are also likely to be places
with little pedestrian infrastructure and high latent demand for pedestrian trips.
These studies suggest that funding for non-motorized travel must look beyond the limited
population of pedestrians living in established older Central Cities and identify suburban populations
living in areas with potentially high rates of walking. The following two sections review the state-of-theart in theory and methods to locate and estimate demand for pedestrian travel.
DETERMINANTS OF WALKING
The choice to walk results from three determining factors (4):
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 Personal factors such as income, age, health, ethnicity, education, social and environmental
attitudes toward the different means of transportation, types of employment, all affect transportation mode
choice.
 Environmental factors, such as pedestrian-supportive land use types, intensity, and mix;
transportation facility characteristics; climate and weather; and topographical conditions contribute to
shaping decisions about mode choice.
 Trip characteristics, including trip length and purpose, also influence mode choice.
These determinants of travel also interact in often-complex ways to affect mode choice. For
example, a wealthy person may chose to drive a short distance to work in downtown Manhattan, but walk
the two kilometers to a friend’s house in the country.
The steps required to identify the environmental conditions that support walking and to locate
latent demand for pedestrian travel are reasonably well understood. First, walking as a mode of transport
is limited by the characteristics of the trip. Today, walking is practical as a mode of transport for short
and very short distances. This means that the distance between trip origin and destination must be small.
The value given to walkable distances are typically derived from the perceived cost of alternative modes
of transport in terms of functionality (cost and time), safety, or comfort. A walking distance that is widely
applied is 0.4 to 0.8 kilometers (0.25-0.5 miles), though it has little empirical verification and has not
been refined to include variations based on location or personal determinants.
Second, pedestrian trips require complementary origin and destination. Land uses at trip origin
and destination must functionally complement each other as generators and attractors of travel.
Appropriate generators and attractors include dense residential or employment areas, as well as specific
land uses such as shops, parks, schools, etc. As will be discussed later, many of the existing approaches
used to measure the relationships between origins and destinations are imprecise or even erroneous.
A third criterion for locating areas with potential for pedestrian travel demand addresses the
infrastructure or the facilities needed for safe and comfortable walking. Continuous sidewalks, trails, and
street crossings are readily used indicators of pedestrian-supportive facilities. While research has shown
that the presence of appropriate facilities seems necessary to support significant volumes of pedestrians
(1), they are never sufficient to generate significant volumes of pedestrian trips. In other words, the lack of
a sidewalk system will deter many people from walking, but the presence of a sidewalk system will not
attract pedestrians unless it connects appropriate origins and destinations. This suggests that capital
budgets for pedestrian infrastructure should be targeted exclusively to places where such origins and
destinations are found. (The single exception to this rule is in cases where new vehicle infrastructure is
being developed, then non-motorized facilities should always be included “unless exceptional
circumstances exist”).
METHODOLOGICAL CONSIDERATIONS
A wide range of methods and tools have been developed to locate and estimate pedestrian travel demand
(4). Most of these methods need to be empirically verified. One barrier to verification is the lack of local
data on actual pedestrian trip making mentioned earlier (11). Another problem is that few estimation
methods address the issue of the latent demand for walking. Many estimate pedestrian volumes based on
projections from existing mode share and do not consider the potential for latent demand. Others use an
estimated percentage of auto trips that may be substituted, depending on the distance for biking or
walking between origin and destination. Finally, several of the methods are based on the concept of level
of service (LOS) and relate demand estimation to infrastructure supply or pedestrian delay. As seen
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earlier, however, the assumption that “they’ll come if we build them,”(4, p. 3) is valid only in those
locations where there are short distances between functionally complementary origins and destinations.
The Pedestrian Potential and Deficiency Indices in use in the Portland, Oregon metropolitan area
are some of the most sophisticated methods developed to date to identify and rank areas with potential
demand for pedestrian travel (19). The development of these indices follows earlier work done on the
Pedestrian Environment Factor (PEF), which was developed for use in modeling regional travel (8). The
PEF was based on an adapted Delphi method of ranking Forecast Analysis Zones (FAZs) for sidewalk
availability, ease of street crossing, connectivity of street/sidewalk systems, and terrain. The Pedestrian
Potential Index (PPI), on the other hand, first attempts to locate areas that have the appropriate land uses
to support pedestrian travel. A Pedestrian Deficiency Index (PDI) then complements the index. PDI
identifies and ranks deficiencies in the pedestrian infrastructure that likely decrease the potential demand
for pedestrian travel. The PPI and PDI therefore address both the potential of an area and its shortcomings
in supporting pedestrian travel. Significantly, they also make a distinction between land use conditions
that cater to pedestrian travel and infrastructure that either does or does not. The tools serve to prioritize
infrastructure improvements in Portland.
The Pedestrian Potential Index (PPI) is a tool similar in purpose to those described in this paper.
The discussion of PPI that follows serves to introduce the approach we take to structure our tools. PPI has
two applications. One is relatively easy to apply and meant to be used in local planning applications. It
continues to rely the adapted Delphi method used in PEF. The other requires land use and activity data
and comes closest to the methods and tools presented in this paper.
The first simple application of PPI starts by identifying areas where schools, parks, and
neighborhood retail are present and assigns points or weights to these land uses. This technique, also used
in LOS approaches (20, 21), is referred to as that of “proximity factors.” It assumes that these land uses
attract pedestrian travel. While the identification of attractors is an important element of any method, PPI
does not identify the generators that may or may not be located around the attractors. Yet this is
important. For example, an elementary school surrounded by 5,000 residents living within a half
kilometer will likely generate many more pedestrian trips than a elementary school surrounded by 1,000
residents within a half kilometer, all else being equal. Our methods attempt to address this issue.
The second application of PPI relies on land use data to measure land use intensity, mix, street
connectivity, average parcel size, and the slope of terrain. This application of PPI measures environmental
factors more accurately than the first application, but still has some problems. First, PPI relies on spatial
units of data that are too large to capture actual development patterns, especially in suburban areas (2, 23).
Second, it is not clear that PPI adequately captures the land use mix that supports pedestrian travel. It
measures land use in terms of residential or employment density without examining the particular mixes
of activity that may support walking. Employment density alone, for example, is not a good proxy for
retail and service uses. While they may be attractors of pedestrian travel, these uses also tend to have low
employment densities. Further, the model assumes that pedestrian travel will increase as both
employment and residential densities increase. The reality of relationships between the mix of uses is
considerably more complex. High levels of pedestrian activity do necessarily occur in areas that have both
medium or high employment and housing. Usually, such activity takes place in areas that have either
medium to high employment densities or residential densities. This is because in most U.S. cities,
residential and employment densities do not co-vary within areas that are small enough to be walkable.
For example, high employment densities are found in areas of office development, which typically do not
have high-or even medium-density residential areas within walkable distances (there are exceptions such
as Manhattan, and the city centers of San Francisco, Boston, and perhaps Philadelphia). U.S. cities do
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have many areas of medium to high-density residential development, which are found in close proximity
to retail, but these neighborhood retail uses have comparatively low employment densities. Conversely,
retail uses with high employment densities such as suburban shopping malls are typically isolated (in
pedestrian terms) from residential areas. Thus “real-world” areas of mixed use typically include what we
call a dominant land use—residential or employment—and other land uses. For areas to be supportive of
pedestrian travel these “other” land uses must be functionally and spatially complementary with the
dominant land use. Whether residential or employment, the dominant land use is the prime generator of
travel. Functionally complementary land uses are those that are likely to be linked by a trip from the
dominant land use. For example, schools and industrial uses are not complementary, as they are unlikely
to generate many pedestrian trips between them, but residential and retail, or employment and retail, are
complementary. Functionally complementary land uses are attractors of pedestrian travel from the
dominant land use if they are spatially complementary—that is falling within a walking distance of the
dominant land use. These concepts are at the core of the tools we developed.
Street connectivity and parcel size are two other factors used in PPI to measure potential demand
for pedestrian travel. The use of these variables is important but need refinement to capture more
accurately the characteristics of environments with latent demand for pedestrian travel. In older urban
areas—typically fully developed before the Second World War—small street-blocks and parcels in
mixed-use areas are indeed associated with complete non-motorized infrastructure and some level of
transit service, and usually command medium to high actual demand for pedestrian travel. Yet, these
same relationships cannot be assumed to hold in newer, suburban areas. Previous research shows that low
actual and high latent demand exists in newer areas that have large and very large street-blocks (1, 2).
This is because dense and mixed development of apartments, retail facilities, offices, or schools built in
the postwar period has taken place in large parcels that are in turn served by large street-blocks. In
contrast, small parcels and small street-blocks in areas developed after the Second World War correspond
to low or even very low density, single-family zones or subdivisions that are unlikely to generate
substantial pedestrian travel demand, actual or latent. Therefore, relating small parcels and street-blocks
to potential pedestrian travel demand do not capture the appropriate land use conditions in postwar
development.
Past research and tools have made important contributions to estimating and locating pedestrian
demand. The tools developed and being used in Portland are especially ambitious. Our critique of these
tools is not meant to belittle this work, but to build and improve on it to construct the Pedestrian Location
Identifier tools (PLIs). The PLIs are especially designed to identify suburban, postwar areas that have
land use characteristics likely to create substantial latent demand for pedestrian travel.
PEDESTRIAN LOCATION IDENTIFICATION TOOLS
Two tools were developed to identify locations with latent demand for pedestrian travel. The two tools
use different sets of databases. Pedestrian Location Identifier One (PLI_1), relies on readily available
census data, GIS software, and aerial photographs. Pedestrian Location Identifier Two (PLI_2), uses
parcel-level data with GIS software. PLI_1 has a wide range of applications because the data are easily
acquired. PLI_2 requires databases that only MPO’s, large cities, and some counties and states may have
(24, 25). The reliance on different databases affects the methods used to identify locations with latent
demand for pedestrian travel. PLI_1 is a “manual” method because of its dependence on the individual
analyst’s judgment when comparing the data from the census to aerial photographs to delineate clusters.
PLI_2, on the other hand, is an “automated” method, because the criteria for delineating the pedestrian
locations are explicitly defined and integrated into GIS-driven routines.
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At the same time, the tools are similar on several accounts. First, they rely on spatial units of data
that are small enough to capture the actual attributes of land development. Second, the same concepts
structure both tools to identify areas with potential for pedestrian travel. The following combination of
generator and attractor land uses is selected between which relatively high volumes of pedestrian volumes
are likely:

Identification of a dominant land use, which represents a sufficient market (generator) of potential
pedestrians. The tools focus on residential land use with residential density housing types serving
as measures of the dominant land use. The second tool, PLI_2, can easily be modified to focus on
other types of dominant land uses.

Identification of functionally complementary land uses. The tools focus on retail facilities and
schools.

Definition of the spatial complementary of land uses—meaning that the distance between
complementary land uses is short enough that people can realistically choose to walk rather than
drive. A walking shed of 0.8 kilometer (0.5 miles) in radius is used to define the maximum airline
distance between trip origin and destination.
Finally, using the tools involves three basic steps. First, the denser residential parts of suburban
areas are highlighted as generators of pedestrian travel. Second, nearby retail areas are identified as
attractors. Third, areas characterized by dense residential and retail development are delineated into
clusters or zones with pedestrian travel potential. Figures 1 and 2 display potential pedestrian zones using
PLI_1 and PLI_2, respectively, in the same area of the Puget Sound’s South King County.
Following are summaries of the specific protocols followed in each one of the tools. The
methodological steps together with an illustrative application to a specific area can be reviewed on the
web (26). Complete manuals to assist in the use of the tools are also available (27).
PLI_1
This method uses U.S. census of population data at the census block level—the smallest spatial unit of
data available—and aerial photos covering the same extent—at a scale of approximately 1= 24,000. The
census data are used to identify areas in relatively dense residential development (dominant land use and
travel generator). The aerial photographs serve, first, to actually “see” where dense residential
development takes places on the ground, and, second, to visually scout for the types of complementary
land uses that are nearby areas of concentrated residential development.
Queries of census data are used to identify blocks with population densities above certain
thresholds and blocks with high percentages of denser housing types, specifically non-single-family
housing and multifamily units in structures with 10 or more units. Queries by housing type are necessary
because large concentrations of dense housing may be located on very large blocks with low overall
densities.
Census blocks highlighted in the GIS as a result of these queries represent a first attempt at
identifying potential clusters. Spatial displays show groups of identified blocks distributed throughout the
area being studied. The analyst eliminates single blocks and groups of blocks that have low total
populations. A threshold of 1,400 people per clustered groups of blocks was used for applications in the
Puget Sound region. This was based on existing development patterns and pedestrian studies in suburban
areas in the region (1, 2, 19).
Areas defined by the highlighted census blocks are then analyzed from aerial photographs. The
photographs enable the analyst to establish the precise location of multifamily development shown in the
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census blocks, as well as to identify retail development, schools, or office development (none of which
are available in the Census data) that may lie close to the dense residential development. The analyst uses
the photographs to delineate areas with multifamily housing, retail and other selected uses in close
proximity. The delineation process makes it possible to isolate areas of concentrated development and
mixed uses from adjacent low-density development.
The spatial display of clusters delineated on the photographs reveals both small and large areas of
concentrated and mixed-use development. A template defining walking sheds is then overlaid on the
potential clusters. A template representing a 0.8 kilometer (0.5 mile) radius circle (a 200 hectare [500
acre] area) was used in the Puget Sound work. Some clusters of concentrated development will be 0.8
kilometer in radius or smaller, forming a potentially walkable area. Others may involve areas that are
much larger than 200 hectares (500 acres). These will require further analysis of the distribution of retail
and residential land uses to determine likely walking patterns within a 0.8 kilometer radius. To do so, the
template needs to be centered on the largest roadway intersection with the most intense retail
development round it as determined by the aerial photographs.
Census blocks are then re-selected that best match the potential cluster areas delineated using the
photographs, and corresponding to the 0.8 kilometer (0.5 mile) distance from the central corner. The
analyst will find that census blocks often cover areas larger than the actual clusters shown in the
photographs, often stretching beyond the walking shed. This is unavoidable because census blocks are
generally defined by street-blocks and many suburban street-blocks are extremely large. If large census
blocks include areas of vacant land or sporadic single family development (they often do), then
population densities obtained from the census data will be lower than the actual cluster’s density. In a
final step, the analyst will derive basic statistics for each cluster from the census data. An example of such
statistics is shown in Table 1.
PLI_2
PLI_2’s uses parcel data to identify clusters. Parcel data allow for an analysis of land use patterns at a
very fine scale. They also permit the use of GIS routines that specify explicitly the criteria used for
defining pedestrian clusters. Conceptually, PLI_2 is similar to PLI_1. It defines clusters by identifying the
dominant land use and the land uses that are functional complements, i.e. land uses likely to generate
travel between them. It also tests whether these uses are concentrated into areas that are small enough to
be traversed by walking This is the test of whether functional complements are also spatial complements.
If land uses are both functionally complementary and concentrated in small areas, they are defined as
clusters that are likely to have latent demand for pedestrian travel.
The first step in PLI_2 is to select out parcels with dominant and complementary land uses from
the entire set of parcels. Because data are available for all parcels regardless of the type of land use on the
parcel, PLI_2 can collapse the identification of dominant and complementary land uses into one step. In
the Puget Sound work, parcels were selected with residential development above 25 dwellings per hectare
(10 units per acre) (generators), parcels with retail (attractors) and schools (also attractors). Other uses
such as transit stations, parks, and recreational facilities can also be included. Note, also, that the selection
of this set of uses is designed to identify mixed-use, residential neighborhoods as potential pedestrian
zones. The method would also be suitable for different mixes of functionally complementary land uses
that may be found in employment centers. In either case, uses not selected for cluster delineation are
reintroduced at the end of the delineation process to ensure a full accounting of parcels within defined
clusters.
The next steps test whether these selected land uses are within spatially concentrated areas, and, if
so, define them as clusters. PLI_2 uses a raster data model for this purpose. This model translates the
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parcel polygons into grid cells to facilitate spatial analysis operations. Grid cells can take different
specified values. In the PLI_2 raster model, cells in the same land use have the same value and represent
“patches” as spatially contiguous areas of land in the same use. A patch may correspond to one or many
parcels. The raster model thus displays dominant and complementary land uses as patches of
homogeneous land uses.
To test whether the patches of selected land uses are spatially complementary, a buffer is
established around each patch, extending it outward by a specified distance. A 60-meter buffer was used
in the Puget Sound work. Where buffers from one patch intersect buffers from another, the patches are
defined as being spatial complements. They are within a specified distance of each other and are defined
as belonging to the same cluster. Note that buffers will include not just the selected land uses, but also
vacant land, office uses, and streets and roads. Other technical steps refine the delineation of potential
clusters. At the end of the process, areas are delineated connecting many patches into the same cluster. A
final step is to recapture parcel data attributes. These data are shown in Table 2 and can be used to
analyze the size, mix, and intensity of resultant clusters.
CONCLUSIONS
Both PLI_1 and PLI_2 enable the analyst to differentiate between suburban areas that do and do not have
potential for pedestrian travel. They add to current practice, but also require further research. On the
“plus” side, the tools offer the following improvements:
 By considering combinations of land uses that are generators and attractors of pedestrian travel,
they capture the characteristics of land use mixes that have the highest potential for substantial volumes of
pedestrian trips.
 By using small spatial units of land use data, they adequately capture the characteristics of
development on the ground. At the same time, because the small spatial units of data are available for
areas of large extent, the tools can be readily applied to today’s spread-out cities and urbanized regions.
 The small spatial units of data also allow a precise and accurate measurement of the land use
characteristics of the small areas that correspond to short walking distances.
 By focusing on multifamily as the dominant land use, the methods bring attention to suburban
areas that have been neglected in the past as locations with yet-to-be-tapped potential for pedestrian
travel. At the same time, the tools need to be applied to areas where employment land uses dominate to
complete the identification of areas that deserve special consideration for latent pedestrian travel demand.
It is worth mentioning that the tools have applications beyond pedestrian travel. In general, they
serve to identify areas of compact development in suburban areas. Compact development is of increasing
interest to planners interested in “smart growth” as a form of land development that is infrastructureefficient—infrastructure including not only transportation systems, but also water and utility systems.
Compact development also represent areas where special consideration is needed for public service
delivery—fire, police, etc. In this sense, the tools can be used in a number of public policy situations
affected by the distribution and concentration of population and activities. Both tools enable planners to
quantify the attributes of compact development. PLI_2 has the advantage of objectivizing the delineation
of clusters, a task that needs to be done manually in PLI_1. PLI_2 also enables the planner to access a
larger set of data attributes defining land development than is available in census data at the block level.
On the “to do” list, the tools demand further research to match the added precision in measuring
pedestrian-supportive land uses with added precision in measuring the pedestrian friendliness of the
transportation facilities. The suburban areas identified are typically deficient in pedestrian infrastructure,
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including low levels of connectivity and accessibility, with large street-blocks, incomplete sidewalk
systems, large at-grade parking lots, fenced-in development, etc. As a result, considerable investments
will be required to retrofit clusters with the safe and comfortable walking conditions that will support
substantial increases in pedestrian travel volumes. The Puget Sound region has many of these places and
other regions likely do also. Because of this, it will be necessary to go beyond identifying areas with high
latent pedestrian demand and prioritizing them for investment above areas with low latent demand.
Prioritization will also need to take place between clusters to yield the highest benefits.
Portland’s Pedestrian Deficiency Index addresses this issue by measuring the characteristics of
transportation facilities that do or do not support pedestrian travel. As part of this research, a third tool,
the Pedestrian Infrastructure Prioritization Decision System tool (PIP), was sketched out to guide further
selection of clusters where investments will generate the highest potential benefits for pedestrian travel
(27). PIP differs from PDI in that it integrates both land use and transportation facility considerations.
Finally, and importantly, the methods proposed attempt to advance both the concepts and the
measures used to capture the characteristics of pedestrian-supportive land use and development patterns.
They attempt to build on previous efforts. Like those efforts, however, they remain mostly theoretical and
untested. Field data are needed first to further the understanding of the theoretical constructs discussed,
and second to yield tools that can actually quantify the demand for pedestrian travel associated with given
land use and development patterns, as follows:
 The concept of a dominant land use as the generator of pedestrian travel deserves further
attention. While we are confident that future measurements of land use mix conducive to pedestrian travel
should not assume co-variance between residential and employment land use intensity, the respective
demands for such travel in concentrations that are primarily residential versus primarily employment land
uses need investigation.
 Given a dominant land use, it will be necessary to know the effect of such single variables on
walking volumes as increased residential and/or employment density, presence of specific
attractors/generators such as schools, parks, etc.
 Similarly, the concept of functionally complementary land uses needs to be better
operationalized. What land uses are best linked by pedestrian travel? For whom?
 The spatial complementarity of land uses needs testing as well, and particularly the 0.8kilometer (0.5-mile) walking shed needs to be validated as applied to different land uses, populations, and
topographical conditions.
 Given pedestrian-supportive land use and development patterns, the effect of different levels of
service in transportation infrastructure needs to be better understood.
Such data gathering will require substantial funding commitment. It is essential, however, in
order to quantify the relationship between land use and travel behavior, and specifically, to properly
estimate expected pedestrian volumes. Such estimates will yield the best approaches to prioritize future
infrastructure investments.
Overall, this and other work suggest that some of the land use and development patterns
generated over the past half-century could support pedestrian travel. The relatively high proportion of
short car trips shown in transportation data further substantiates the possibility of substituting these by
walking. Yet while some of the environmental conditions and trip characteristics for walking are not
entirely discouraging, improvement to the share of non-motorized travel is unlikely unless further changes
are made in both public policies and public attitude toward non-motorized travel. The share of public
11
Moudon, Hess, Matlick, Pergakes
spending for infrastructure supporting pedestrian and non-motorized travel must be increased
substantially. Transportation data must focus on transit riders, walkers, and bikers. Also, the public must
be made aware of the real costs of the escalating number of motorized trips and want to reduce them.
ACKNOWLEDGMENTS
The authors thank the six anonymous reviewers for their thoughtful and constructive comments.
12
Moudon, Hess, Matlick, Pergakes
REFERENCES
(1) Hess, P. M., Moudon, A. V., Snyder, M. C., Stanilov, K. Site Design and Pedestrian Travel.
Transportation Research Record, 1674, TRB, National Research Council, Washington, D.C.,
1999, pp. 9-19.
(2) Moudon, A. V., and Hess, P. M. Suburban clusters: The Nucleation of Multifamily Housing
in Suburban Areas of the Central Puget Sound. Journal of American Planning Association,
Vol. 66, No. 3, 2000, pp. 243-264.
(3) Pucher, J., and Dijkstra, L. Making Walking and Cycling Safer: Lessons from Europe.
Transportation Quarterly, Vol. 54, No. 3, 2000, pp. 25-50.
(4) Federal Highway Administration. Guidebook on methods to estimate non-motorized travel:
Supporting Documentation. U.S. Department of Transportation. National Bicycling and
Walking Study, Case Study No. 9, 1999.
(5) Nationwide Personal Transportation Survey. United States Department of Transportation,
National Highway Administration, 1990.
(6) Washington State Energy Office. Municipal Strategies to Increase Pedestrian Travel.
Olympia, WA, 1994.
(7) Dunphy, R. T. Transit Trends. Urban Land, (May 2001) pp. 79-83.
(8) 1000 Friends of Oregon. The Pedestrian Environment, Vol. 4A. Making the Land Use,
Transportation, Air Quality Connection (LUTRAQ), Portland Oregon, 1993.
(9) http://www.tea21.org/guide/charts/2000.htm and
http://www.walkinginfo.org/insight/fact_sheets/index.htm
(10)
Accommodating Bicycle and Pedestrian Travel: A Recommended Approach, FHWA,
2000. http://www.fhwa.dot.gov/environment/bikeped/design.html. Accessed July 10, 2001.
(11) United States Department of Transportation. Bicycle and pedestrian data: Sources, needs &
gaps. Bureau of Transportation Statistics, 2000.
(12) Antonakos, C. L. (1995). Nonmotor Travel in the 1990 Nationwide Personal Transportation
Survey. Transportation Research Record 1502, TRB, National Research Council,
Washington, D.C., 1995, pp. 75-82.
13
Moudon, Hess, Matlick, Pergakes
(13) Holtzclaw, J. Using Residential Patterns and Transit to Decrease Auto Dependence and
Costs. National Resources Defense Council, San Francisco, CA, 1994.
(14) Newman, P. W. G., and Kenworthy, J. R. Cities and Automobile Dependence: A Source
Book. Ashgate Publishing Company Brookfield, VT, 1989.
(15) Levine, J., Aseem Inam, Gwo-Wei Torng. Innovation in Transportation and Land Use as
Expansion of Household Choice. Unpublished paper 2000.
(16) Rutherford, G.S., Ishimaru, J.M, Chang, J, and McCormack, E. The Transportation Impacts
of Mixed-Use Neighborhoods. Washington State Transportation Innovations Unit for
Washington State Transportation Commission, Olympia, WA, 1995.
(17) Leinberger, C.B., Metropolitan Development Trends of the Late 1990s: Social and
Environmental Implications in Land Use in America, Diamond, LH and Noonan, P.F. eds.,
Island Press, Washington, D.C., 1996.
(18) Moudon, A. V., Hess, P. M., Snyder, M. C., and Stanilov, K. Effects of Site Design on
Pedestrian travel in Mixed-Use, Medium-Density Environments. Transportation Research
Record, 1578, , TRB, National Research Council, Washington, D.C , 1997, pp. 48-55.
(19) Hess, P. M. Pedestrians, Networks, and Neighborhoods: A Study of Walking and Mixed-Use,
Medium-Density Development Patterns in the Puget Sound Region. Ph.D. Dissertation.
University of Washington, Seattle, WA, 2001.
(20) Oregon Department of Transportation. Oregon Bicycle and Pedestrian Plan. Bicycle and
Pedestrian Program, ODOT, Salem, OR, 1995.
(21) Landis, B., and Toole, J. Using the Latent Demand Score Model to Estimate Use. Pro Bike
Pro Walk 96: Forecasting the Future, Bicycle Federation of America/Pedestrian Federation
of America, 1996, pp.. 320-325.
(22) Dixon, L. Adopting Corridor-Specific Performance Measures for Bicycle and Pedestrian
Level of Service. Transportation Planning, Traffic Engineering Department, City of
Gainesville, FL, 1995, pp. 5-7.
(23) Hess, P., Moudon, A.V., Logsdon, M. Measuring Land Use for Transportation Research.
Transportation Research Record, National Research Council, Washington, D.C., in press.
14
Moudon, Hess, Matlick, Pergakes
(24) Kollin, C., Warnecke, L., Lynday, W., and Beattle, J. (1998). Growth Surge: Nationwide
Survey Reveals GIS Soaring in Local Governments. Geo Info Systems (February 1998) pp.
24-30.
(25) Moudon, A.V., and Hubner, M., eds. Monitoring Land Supply with Geographic Information
Systems: Theory, Practice, and Parcel-Based Approaches. New York, NY: Wiley 2000.
(26) An abbreviated, step-by-step version of the tools is being inserted into the following
websites: http://www.wsdot.wa.gov and http://www.urbanformlab.net.
(27) Moudon, A.V. Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist
Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel.
Washington State Transportation Commission and U.S. Department of Transportation. WARD 519.1. Also available at
http://depts.washington.edu/transnow/Research/Reports/TNW2001-05.pdf
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Moudon, Hess, Matlick, Pergakes
Tract No.
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029404
029501
029501
Totals
Block
212
208
304
303
301B
301A
306
307
309
310B
213
310A
312
311
302
214
201
901
Pop.
1140
73
50
82
229
994
97
95
74
105
83
54
271
47
140
47
211
858
4650
16
Acres
77.6
5.9
4.9
4.2
11.9
45.2
6.9
8.6
6.7
3.5
17.3
3.5
32.6
3.7
5.7
5.4
114.9
119.3
477.89
Total
10+ Units # NonPop.
Housing 1 Unit per per
Single
HU
% Non% Less
Density
Units
structure Structure
Family HU % Non- SF Density
white
18YR
14.7
431.0
79
183
352.0
81.7
5.6
19.1
38.3
12.3
21.0
20
0
1.0
4.8
3.5
11.0
32.9
10.1
17.0
17
0
0.0
0.0
3.4
0.0
26.0
19.5
21.0
21
0
0.0
0.0
5.0
19.5
36.6
19.3
75.0
45
26
30.0
40.0
6.3
14.0
30.6
22.0
515.0
11
360
504.0
97.9
11.4
9.5
23.5
14.0
31.0
30
0
1.0
3.2
4.5
15.5
32.0
11.0
32.0
32
0
0.0
0.0
3.7
6.3
26.3
11.1
24.0
24
0
0.0
0.0
3.6
18.9
29.7
30.4
35.0
17
17
18.0
51.4
10.1
3.8
30.5
4.8
52.0
2
5
50.0
96.2
3.0
6.0
8.4
15.6
16.0
16
0
0.0
0.0
4.6
38.9
33.3
8.3
120.0
16
34
104.0
86.7
3.7
16.6
28.4
12.7
23.0
19
0
4.0
17.4
6.2
4.3
29.8
24.6
92.0
0
74
92.0
100.0
16.2
11.4
15.7
8.6
28.0
0
27
28.0
100.0
5.2
10.6
14.9
1.8
116.0
54
61
62.0
53.4
1.0
0.5
22.3
7.2
422.0
39
322
383.0
90.8
3.5
14.3
26.3
13.06
2071
442
1109
1629
43.34
5.29
11.59
25.56
Table1: Data derived from Census block analysis for cluster A, shown in Figures 1 and 2
Total Area
524 acr
210 ha
Mean Block Size
48 acr
19 ha
Total Lots
266
Mean Lot Size
1.7 acr
0.7 ha
No. of Buildings
653
Total Housing Units
4232
Housing Density Gross
8.1/acr
20/ha
Commercial Floor Area 1.4 M. sq. ft. 137 k sq. m.
Commercial FAR
0.18
Population
9321
Population Density
17.8/acr
44.5/ha
Table 2: Data derived from parcel database for cluster A, shown in Figures 1 and 2
Moudon, Hess, Matlick, Pergakes
17
Key:
Query 1 only (10 people/acre)
Query 2 (50% NSF) and in some cases Query 1
Query 3 (large apts.) and in some cases contain
Query 1 and Query 2
0
1 mile
Figure 1: PLI_1, Census blocks delineating clusters with potential demand for pedestrian
travel (depicted area is the City of Kent in South King County, Washington)
Moudon, Hess, Matlick, Pergakes
18
Key:
Reds – Retail
Tans and Browns – Medium Density Residential
Blue - Schools
0
Figure 2: PLI_2, Patches delineating clusters with potential demand for
pedestrian travel (depicted area is same as above)
1 mile
Moudon, Hess, Matlick, Pergakes
19