Classification Categories

The American Walkability Atlas classifies neighborhoods into five categories based on their Employment Mix scores. This variable measures the diversity of employment opportunities within a neighborhood by evaluating how evenly jobs are distributed across different economic sectors. Rather than simply measuring the number of jobs, Employment Mix reflects the variety of employment types available, including retail, office, service, industrial, institutional, entertainment, and other economic activities. Within the EPA’s National Walkability Index, neighborhoods with a greater diversity of employment opportunities generally provide more destinations that support walking throughout the day.

Research consistently demonstrates that neighborhoods containing a diverse mix of employment types generate more walking, shorter trip distances, and greater transportation choice because residents and visitors have access to a wider variety of destinations (Cervero & Kockelman, 1997; Frank et al., 2005; Ewing & Cervero, 2010).

• Very Low Employment Mix: Areas where employment is highly concentrated in a limited number of employment categories or where the distribution of employment across categories is very uneven. These locations represent the lowest relative level of employment diversity.
• Low Employment Mix: Areas with limited diversity among employment categories. Multiple types of employment may be present, but one or a small number of categories remain dominant.
• Below Average Employment Mix: Areas with some diversity in employment activity, but the distribution of jobs across employment categories remains less balanced than in more mixed areas.
• Moderate Employment Mix: Areas with an intermediate degree of employment diversity. Jobs are distributed across several employment categories with a moderate level of balance among them.
• Above Average Employment Mix: Areas with relatively diverse employment activity and a more balanced distribution of jobs across employment categories. These conditions provide a stronger mix of employment uses than the middle range.
• High Employment Mix: Areas with a broad and relatively balanced distribution of employment across multiple categories. This high level of employment diversity is associated with built-environment conditions that are more supportive of walking.
• Very High Employment Mix: Areas with the greatest relative diversity and balance among employment categories. These locations represent the highest level of employment mix measured by this component of the National Walkability Index. 

Planning Implications

Employment diversity is a fundamental characteristic of walkable neighborhoods because it creates destinations that generate trips throughout the day. While residential density determines where people live, employment diversity influences where people work, shop, dine, receive healthcare, access services, and participate in recreational activities. The EPA includes Employment Mix within the National Walkability Index because neighborhoods containing multiple employment sectors typically support more frequent walking and shorter trip distances.

Research has consistently shown that mixed employment environments encourage active transportation by increasing the number and variety of nearby destinations. Cervero and Kockelman (1997) identified land-use diversity as one of the “3 Ds” influencing travel behavior, while Frank et al. (2005) demonstrated that diverse land-use patterns are associated with increased walking and improved public health outcomes. Likewise, Ewing and Cervero (2010) concluded that land-use diversity significantly influences travel behavior by encouraging shorter trips and reducing automobile dependence.

Neighborhoods with High and Very High Employment Mix often benefit from planning policies that preserve mixed-use zoning, encourage adaptive reuse of commercial properties, support small businesses, and maintain pedestrian-oriented commercial districts. Investments in streetscapes, transit access, bicycle infrastructure, and public spaces can further strengthen these neighborhoods by improving connections among diverse employment destinations.

Neighborhoods with Low or Very Low Employment Mix may benefit from strategic infill development, neighborhood commercial centers, mixed-use redevelopment, and zoning reforms that gradually introduce additional employment opportunities while preserving neighborhood character. However, planners should recognize that some neighborhoods intentionally contain specialized land uses—such as airports, industrial parks, military installations, or large recreational areas—where limited employment diversity reflects their intended function rather than a planning deficiency.

Ultimately, Employment Mix should be evaluated alongside residential density, street connectivity, transit accessibility, and the balance between employment and housing to understand how neighborhoods support complete, walkable communities.

Understanding Neighborhood Walkability

Employment Mix contributes to neighborhood walkability by increasing the variety of destinations that residents and visitors can reach within a reasonable walking distance. A neighborhood containing offices, restaurants, healthcare facilities, retail stores, schools, entertainment venues, and public services offers more opportunities for people to accomplish multiple daily activities without relying exclusively on an automobile.

Unlike municipal averages, neighborhood-level Employment Mix captures localized differences in economic activity within the same city. One neighborhood may function primarily as a residential community with few employment destinations, while another may serve as a regional commercial center with offices, restaurants, retail businesses, educational institutions, and entertainment venues located within a compact area.

High Employment Mix generally supports:

• Greater diversity of walkable destinations.
• Increased opportunities for walking throughout the day.
• Stronger local economies.
• Reduced automobile dependence.
• More resilient neighborhood business districts.

Employment Mix alone, however, does not determine neighborhood walkability. A neighborhood may contain numerous employment sectors but still experience poor pedestrian conditions because of wide arterial roads, disconnected street networks, limited sidewalks, or inadequate transit access. Likewise, primarily residential neighborhoods can still provide excellent walking environments when they feature connected street networks, nearby parks, schools, neighborhood commercial areas, and safe pedestrian infrastructure. Consequently, Employment Mix should be interpreted alongside the other components of the National Walkability Index.

Geographic Patterns in Neighborhood Walkability

The neighborhood-level Employment Mix results reveal a substantially different distribution from both Intersection Density and Proximity to Transit. Across all 2,740 neighborhoods, the average employment-mix score is approximately 10.27, with a median of approximately 10.32. Scores range from 1.00 to 20.00, demonstrating considerable variation in the degree to which different employment types are represented within neighborhoods. The dataset itself identifies this measure as emp_mix, with individual neighborhood values ranging widely even within the same municipality.

Using the same seven fixed intervals with an interval size of approximately 2.857143, the distribution is concentrated primarily in the middle classes. 72 neighborhoods (2.6%) fall between 0–2.86, 255 (9.3%) between 2.86–5.71, 598 (21.8%) between 5.71–8.57, 732 (26.7%) between 8.57–11.43, 684 (25.0%) between 11.43–14.29, 300 (10.9%) between 14.29–17.14, and 99 (3.6%) between 17.14–20.00. Approximately 73.5 percent of all neighborhoods therefore fall between 5.71 and 14.29, indicating that moderate employment diversity is considerably more common than either extremely low or extremely high employment mix.

This pattern is consistent with the EPA National Walkability Index methodology. Employment mix is intended to capture the diversity of employment types within a Census block group, rather than simply the total number of jobs. The EPA methodology recognizes that a greater mixture of employment types can increase the variety of destinations available within an area and therefore contribute to an environment more supportive of walking. Employment mix operates alongside intersection density, proximity to transit, and employment-and-household mix in the overall National Walkability Index.

Unlike Intersection Density, however, Employment Mix does not display as straightforward a center-to-edge geographic pattern. Downtowns generally perform better than the dataset as a whole, but high employment-mix scores also occur in suburban employment centers, commercial districts, industrial-commercial areas, and other specialized locations. Among the 52 neighborhoods containing “Downtown” in their names, the average Employment Mix score is approximately 13.96, substantially higher than the overall neighborhood average of 10.27. Nevertheless, individual downtown scores vary considerably.

For example, Stockton Downtown scores 17.75, San Angelo Downtown 17.00, Portland Downtown approximately 16.22, Dallas’ Central Business District 14.83, Bellevue Downtown 14.80, Houston Downtown 14.00, Los Angeles Downtown approximately 13.83, San Diego Downtown approximately 13.36, Riverside Downtown approximately 12.09, Waco Downtown 10.25, Miami Downtown approximately 9.29, and Long Beach Downtown 9.80. These differences demonstrate that central location alone does not guarantee a highly diverse employment structure. A downtown dominated by a limited number of employment sectors can score lower than a neighborhood containing a more balanced range of employment activities.

The results also show that some of the highest Employment Mix scores occur outside traditional downtowns. Irvine’s Cypress Village receives the maximum score of 20.00, while Spectrum Center–Spectrum 4 & 6 scores 19.00, Orchard Hills 19.00, and Lower Peters Canyon 18.00. Similarly, Dallas’ Cedars scores 20.00, Deep Ellum 19.00, and the Design District 19.00, compared with 14.83 for the Central Business District. These examples reinforce the idea that Employment Mix measures the composition of economic activity rather than simply urban density or proximity to a city center.

San Angelo provides a particularly useful example of substantial within-city variation. Downtown scores 17.00, Fort Concho 18.00, the Bluffs 16.75, Southland 16.50, Santa Rita approximately 15.83, and Country Club 15.00. At the other end, Sunset scores 1.00, Angelo Heights 3.00, Belaire 5.00, Fort Concho East 6.80, and Glenmore 7.00. This variation is especially notable because all San Angelo neighborhoods received the minimum score for Proximity to Transit. Employment Mix therefore provides differentiation that the transit variable could not capture within the city.

Waco displays a similarly varied pattern. Austin Avenue scores 17.50, Richland Hills approximately 17.33, Heart of Texas 14.50, Lake Waco 13.40, and Kendrick approximately 13.33. By contrast, Dean Highland scores 3.20, Timbercrest approximately 3.67, Airport approximately 4.33, Carver 4.60, and Landon Branch 5.60. Downtown’s score of 10.25 falls close to the dataset-wide mean. This again illustrates that the geography of employment diversity is considerably more complex than a simple assumption that downtown neighborhoods necessarily contain the greatest employment mix.

Los Angeles further demonstrates this complexity. The city’s Downtown neighborhood scores approximately 13.83, while neighborhoods such as Pico-Robertson (15.85), Pacific Palisades (15.81), Playa Vista (15.67), West Los Angeles (15.60), Sawtelle (15.54), and Carthay (16.00) perform as well as or better than Downtown. At the opposite extreme, Chatsworth Reservoir scores 1.00, Watts 3.44, Hansen Dam 5.00, Green Meadows approximately 5.26, and Vermont Vista 5.70.

These patterns illustrate an important characteristic of Employment Mix: high scores can emerge in several different urban contexts. A traditional downtown may contain a diverse collection of offices, retail establishments, restaurants, institutions, and services, but similar diversity can also occur within suburban commercial centers, mixed employment districts, medical or institutional areas, and redevelopment districts. Consequently, Employment Mix does not correspond as directly to a particular street-network pattern or distance from downtown as Intersection Density often does.

The lowest Employment Mix values should likewise be interpreted carefully. A low score does not necessarily mean that a neighborhood has little employment overall. Rather, it can indicate that employment is concentrated within a narrower range of employment types. Specialized industrial districts, institutional campuses, primarily residential neighborhoods, parks, and other single-purpose areas may therefore receive relatively low scores because their economic or land-use functions are less diverse.

Overall Pattern

Overall, Employment Mix displays a distinctly middle-heavy distribution. The largest class is 8.57–11.43, containing 732 neighborhoods (26.7%), followed closely by 11.43–14.29 with 684 neighborhoods (25.0%) and 5.71–8.57 with 598 neighborhoods (21.8%). Together, these three middle classes contain 2,014 of the 2,740 neighborhoods, or approximately 73.5 percent of the entire dataset.

The extremes are comparatively uncommon. Only 72 neighborhoods (2.6%) occupy the lowest class, while only 99 neighborhoods (3.6%) occupy the highest. This differs considerably from Proximity to Transit, where neighborhoods were strongly concentrated toward the upper end while a substantial group also occupied the lowest class. Employment Mix instead resembles a more gradual continuum in which moderate values dominate.

This distribution makes conceptual sense because achieving an extremely high employment-mix score requires a neighborhood to contain a particularly diverse economic structure. Many neighborhoods naturally specialize to some extent. Predominantly residential neighborhoods may contain relatively few employment categories, while industrial, institutional, commercial, or office districts may contain substantial employment but remain concentrated within particular types of activity. Moderate scores can therefore characterize neighborhoods that contain some mixture of economic uses without approaching the greatest levels of employment diversity.

The results also demonstrate why Employment Mix should not be interpreted as a direct substitute for mixed land use generally. The measure concerns the diversity of employment represented within the EPA methodology. It does not independently establish whether housing, retail, parks, schools, sidewalks, or other destinations are conveniently arranged for pedestrians. Those characteristics must be interpreted alongside the other components of the National Walkability Index.

This distinction becomes especially important when comparing Employment Mix with the previous variables. Intersection Density describes the connectivity of the street network; Proximity to Transit describes access to transit service; Employment Mix describes the diversity of employment opportunities. A neighborhood can therefore perform strongly in one category while performing poorly in another. San Angelo demonstrates this particularly clearly: Downtown combines a high Intersection Density score (18.00) and high Employment Mix score (17.00) with the minimum Proximity to Transit score (1.00). Its component scores reveal a more nuanced built environment than its overall NWI score alone can show.

Another important finding is that Employment Mix appears to be less spatially predictable than Intersection Density or Proximity to Transit. Connected street grids frequently correspond to traditional urban development, while transit accessibility often follows identifiable transit corridors and metropolitan transit systems. Employment diversity, by comparison, depends heavily on the economic function and composition of individual neighborhoods. High values can therefore occur in downtowns, suburban centers, redevelopment districts, commercial corridors, and other employment concentrations.

Perhaps most importantly, the results again reinforce the value of neighborhood-scale analysis. Municipalities contain neighborhoods serving very different economic functions, and these differences produce substantial variation in employment diversity within the same city. Looking only at a citywide average would conceal the contrast between diversified employment centers, predominantly residential neighborhoods, specialized districts, and areas with relatively little employment activity.

Taken together, the Employment Mix results add an important economic dimension to the broader walkability analysis. The geographic patterns demonstrate that creating an environment supportive of walking involves more than constructing connected streets or providing transit. Neighborhoods also benefit when multiple types of employment and destinations are represented within relatively compact areas, increasing the potential for different activities to occur near one another. When Employment Mix is considered alongside Intersection Density, Proximity to Transit, and Employment-and-Household Mix, the result is a more complete picture of how transportation networks, land-use patterns, and economic activity interact to influence neighborhood walkability.