Using GIS to Identify the Potential of Transit-Oriented Development (TOD) in City of Iowa City, Coralville, and North Liberty

Yiru Wang, Jeremy Endsley

Community Transportation Committee

Introduction

​Currently, although the definitions of TOD (Transit-Oriented Development) vary, its core features are relatively consistent: TOD is essentially a medium- to high-density development pattern, typically located within walking distance of major public transit stations, integrating residential, employment, and commercial services, and designed to prioritize the needs of pedestrians and non-motorized travel (California Department of Transportation, 2002). TOD can be implemented through new construction or by redeveloping existing buildings, with “walkability” being the most critical design principle (Fard, 2013).

​Research and practice show that TOD not only reduces transportation costs and provides mixed-income housing but also plays an important role in promoting social equity and improving housing conditions (Reconnecting America, 2007). Well-planned TOD helps strengthen spatial connections between jobs and housing, reduces transportation inequities for zero-vehicle households and low-income groups, and promotes affordable housing through a diverse housing supply. In addition, TOD can revitalize aging neighborhoods, activate commercial corridors, and serve as an important tool to attract private investment and stimulate economic renewal (The City of Calgary, 2004). By optimizing land use and transportation systems, TOD provides cities with an integrated approach to achieving sustainable development, improving the financial efficiency of public transit, and protecting the mobility rights of vulnerable groups.

This study aligns closely with the mission of the Community Transportation Committee (CTC). CTC is committed to expanding inclusive and affordable transportation options for vulnerable groups, with a focus on zero-vehicle households, low-income residents, and others facing transportation inequities. CTC believes that improving transportation systems is not only about mobility but also directly affects access to jobs, housing opportunities, and regional economic vitality. However, the current public transit system in Johnson County faces financial pressures and limited service coverage. This results in higher travel costs and lost opportunities for transportation-disadvantaged groups.

​Based on the concept of TOD and its potential benefits, the core goal of this study is to promote the sustainable development of the regional public transit system through TOD while achieving social equity. Specifically, on one hand, TOD can improve the efficiency of public transit use, reduce overreliance on short-term financial subsidies, and create a positive cycle between the transit system and economic development, thereby supporting the financial sustainability of public transit. On the other hand, TOD can significantly improve transportation accessibility for zero-vehicle households and low-income groups and create more equitable housing and mobility conditions for vulnerable populations, thus promoting inclusive urban development.

Research Question​

​The core research question of this study is: Which areas in Iowa City, Coralville, and North Liberty have the highest TOD development potential based on transit accessibility, social equity, and housing development capacity?

Methodology

​This study uses a GIS-based composite index approach to evaluate TOD (Transit-Oriented Development) potential in Iowa City, Coralville, and North Liberty. The methodological design references the indicator framework proposed by Woodsong and Metropolitan Transportation Commission (n.d.), which includes density, diversity, walkability, and economic vitality. However, due to regional characteristics and data availability, four main categories of indicators were selected: Transit Accessibility, Equity, Land Use & Housing Development, and Functional Services & Employment. All data were processed in ArcGIS Pro, standardized, and combined using a weighted overlay to produce the final TOD composite potential map. The weighting model is as follows: TOD Score = (Poverty rate × 0.125) + (Zero-vehicle household rate × 0.125) + (Transit accessibility score × 0.30) + (Employment density × 0.12) + (Potential housing capacity × 0.10) + (Points of Interest density × 0.09) + (Government-owned parcels suitability × 0.09) + (Parking lot factor × 0.05).

Indicator SelectionThe first category is Transit Accessibility, which reflects the core principle of TOD—ensuring convenient walking access to public transportation. Transit stop locations were collected, and multiple buffer zones were created, with 0.25 miles as the highest accessibility level. Scores decrease gradually with distance (1–5 points), where farther distances receive lower scores. This indicator accounts for 30% of the total score and represents the importance of transit network coverage for TOD implementation. This indicator was adapted from Woodsong and Metropolitan Transportation Commission (n.d.) and directly quantifies the walkability of TOD zones.

The second category is Equity, which evaluates whether TOD can improve travel conditions for transportation-disadvantaged groups, including the poverty rate and percentage of zero-vehicle households. Data were sourced from the American Community Survey (ACS). Poverty rates (excluding students) and zero-vehicle household percentages were calculated at the tract level through spatial interpolation and then aggregated to analysis units. Natural Breaks classification was applied for a five-level scoring system. Each sub-indicator carries a weight of 12.5%, totaling 25%, ensuring that the TOD potential evaluation incorporates social equity objectives.

​The third category is Land Use & Housing Development, which assesses the feasibility of TOD implementation and the potential housing supply. Local development potential is an important TOD factor, defined as the amount of land available for future development (MTC, 2005). First, housing development capacity was calculated using zoning regulations and existing housing data, estimating remaining theoretical housing units per parcel and assigning scores based on capacity (weight 10%). Development potential also includes government-owned parcels and large parking lots, which often allow for land-use adjustments or redevelopment, making them key TOD resources. Government parcels were identified from cadastral databases, excluding parks and public facilities (weight 9%). Parking lots were detected using remote sensing and filtered by size (greater than 0.5 acres), with scores assigned by area (weight 5%). Together, these indicators reflect whether an area has land suitable for redevelopment and this indicator was adapted from Woodsong and Metropolitan Transportation Commission (n.d.), directly quantifying the potential within TOD zones for future residential development.

​The fourth category is Functional Services & Employment, which measures economic activity and access to essential services. Employment density (weight 12%) was calculated using OnTheMap data as jobs per acre, aggregated via Zonal Statistics and classified to reflect the concentration of employment opportunities and their ability to attract transit use. Points of Interest (POI) density (weight 9%) includes education, healthcare, cultural, and government facilities. Kernel Density analysis was applied to measure clustering, and scores were assigned using Natural Breaks. This indicator was adapted from Woodsong and Metropolitan Transportation Commission (n.d.), reflecting the current level of economic development in the area and indirectly capturing mixed-use characteristics while assessing accessibility to basic services.

In addition, a 0.25-mile commercial land buffer was overlaid to strengthen the representation of multifunctionality and land use diversity. 

Data ProcessingThe collected datasets, including demographic data, transit accessibility measures, zoning information, government-owned parcels, and points of interest (详见Data source章节), were standardized and integrated into a GIS environment. Data cleaning involved removing irrelevant features (e.g., parks and floodplains) and ensuring consistent projections across layers. Spatial processing steps included intersecting zoning polygons with block boundaries to allocate housing capacity proportionally, converting tract-level demographic indicators to block level using weighted averages, and assigning employment and POI density values through Zonal Statistics. All continuous variables were normalized and classified into five suitability levels using the Natural Breaks method. These steps ensured that all indicators were spatially aligned and comparable at the block level.

The final output is a block-level TOD potential map that identifies areas best suited for future transit-oriented growth.

Limitations

​Although this study referenced TOD site selection methods from multiple cities, it still has certain limitations.

1. To balance data availability and the level of detail in the final outputs, this study uses the block as the primary unit of analysis. However, variations within a single block exist (for example, different areas may have different transit access scores or government-owned parcel scores), which means a block could be divided into several areas with different scores. Even so, the analysis is based on the block level, and areas unsuitable for housing development (such as parks and floodplains) were excluded as much as possible. This means the study identifies which blocks are suitable for TOD development, rather than specific parcels. If a block-level area is chosen for TOD development, further research is needed to determine which specific parcels within that block can be developed.

2. Due to data availability constraints, in the Potential Housing Capacity Score map, the existing housing data is based on the 2020 Census DHC - P & H Tables (Blocks & Larger Areas), which may differ from current conditions.

3. Since the mapping process involved multiple data levels, including tract level, block level, zoning level, and polygons, adjustments were required to standardize them to a single level for creating the final composite score map. In this process, area-weighted allocation was used, which may introduce some discrepancies between the results and actual conditions.

4. Regarding land use diversity, due to a lack of relevant data and a unified standard applicable to all three cities, this study did not use a formal land use diversity index. However, the analysis addressed this by overlaying commercial zones and using POI and employment density layers to indirectly reflect land use diversity. Additionally, the final composite map was further refined by integrating zoning characteristics to identify two tiers of areas most suitable for TOD development.

Findings

​​Based on the above methodology, two composite maps were produced: the first is the Composite TOD Suitability Level Map, and the second is the Zoning Constraint Effects Map, created according to the current local upzoning practices. Additional detailed spatial distribution maps of the individual indicators can be found in the appendix.

​These areas are located within 1/4 mile of major transit corridors, ensuring strong transit accessibility. They also demonstrate high employment density and a significant concentration of points of interest, which provide strong support for mixed-use development. Combined with their proximity to transit and potential land use flexibility, these blocks represent highly suitable locations for TOD. While some parcels may require zoning adjustments, prioritizing these areas for TOD implementation can maximize land-use efficiency, improve transit ridership, and promote equitable, mixed-use communities. For TOD planning, areas with high suitability should be prioritized for mixed-use development and high-density residential construction, while areas with low suitability may require additional transportation infrastructure investment and policy support to avoid poor overall investment returns and improve TOD efficiency. Therefore, future development should be concentrated in high-suitability TOD areas to maximize transportation benefits and reduce environmental pressures. 

This map shows the impact of zoning constraints on TOD potential in Iowa City, Coralville, and North Liberty. The analysis indicates that after considering potential housing capacity, the overall TOD composite potential in some areas decreases significantly. This means that even if these areas have advantages in equity indicators, points of interest density, or transit accessibility, their TOD development potential is still restricted by current zoning regulations (such as low-density residential zoning) and housing development limitations.

The legend classifies the reduction levels as follows: No reduction (<0), Slight reduction (0.01–0.07), Moderate reduction (0.08–0.25), and Significant reduction (0.26–1.46).

Significant reduction areas (dark blue, 0.26–1.46) are mainly concentrated in downtown Iowa City (which includes many blocks classified as Tier 1 in Table 1) and along major transit corridors, especially near the city center core and surrounding campus areas. These areas have strong TOD conditions (high accessibility and high equity), but due to current zoning restrictions such as low-density residential development, potential housing capacity is insufficient, creating a major barrier to TOD implementation. Therefore, these areas should be prioritized for upzoning or the introduction of mixed-use policies to unlock TOD potential.

Moderate reduction areas (medium blue, 0.08–0.25) are mainly distributed in the northeast, southeast, and northwest parts of Iowa City, around commercial corridors in Coralville, and residential zones near transit routes in North Liberty. These areas perform well in transit and POI coverage but are similarly constrained by low-density zoning, resulting in moderate TOD potential loss. Such areas could be considered for phased zoning reforms that moderately increase floor area ratios and allow multifamily housing while maintaining neighborhood character.

Slight reduction areas (light blue, 0.01–0.07) are mostly found on the city’s edges or in low-density development zones. These areas have limited TOD potential, and zoning reform priority is relatively low, but they can serve as long-term reserve areas for future TOD strategies or transit expansion.

​Since the analysis is based on block-level data, the results are mainly intended to identify hotspot areas where TOD potential is restricted. Further implementation will require more detailed parcel-level feasibility assessments to ensure efficient land use and targeted development policies.

Conclusions and recommendations

​This study developed a composite TOD potential scoring model that combines multiple factors, including transit accessibility, equity indicators, land use potential, and functional service vitality, to identify areas with the highest TOD development potential in Iowa City, Coralville, and North Liberty. The analysis shows that high TOD suitability areas are primarily concentrated in downtown Iowa City (as shown in Tier 1 category in Table 1). These areas stand out in TOD indicators, featuring high transit accessibility, strong equity characteristics (high poverty rate and high proportion of zero-vehicle households), as well as high employment and POI densities. They also offer potential residential development capacity and include mixed-use parcels.

Promoting TOD in these high-potential areas can not only increase public transit usage and improve the financial sustainability of the current transit system but also directly advance inclusive housing development and transportation equity. This is particularly important for improving accessibility for zero-vehicle households and low-income groups, narrowing mobility gaps, and alleviating housing affordability issues. At the same time, guiding compact development through the TOD model is expected to reduce infrastructure pressure caused by urban sprawl, enhance land use efficiency, and stimulate regional economic vitality.

However, the analysis also found that current zoning regulations pose significant constraints to TOD implementation, especially in the core areas of Iowa City and along major transit corridors. Low-density residential zoning prevents many high-potential TOD areas from being fully developed.

In summary, to achieve TOD objectives, the government should adopt the following measures:

1. Emphasize transit as the core: Prioritize development in areas with high transit accessibility to maximize the impact of TOD on improving the financial sustainability of the transit system. This also aligns with TOD’s fundamental principles.

2. Strengthen equity orientation: Implement TOD-related infrastructure investments and housing subsidy policies in communities with high poverty rates and a high proportion of zero-vehicle households to ensure TOD does not lead to the marginalization of low-income groups.

3. Introduce mixed-use development policies: Existing mixed-use parcels should be treated as TOD priority zones, and the integration of commercial, residential, and public service functions should be encouraged to create compact, walkable neighborhoods. However, since mixed-use parcels are currently limited in the three cities, the government should consider approving more mixed-use zoning in high-suitability TOD areas or adopting other mixed-use development policies.

4. Prioritize upzoning reforms: For high-potential TOD areas constrained by existing zoning restrictions, development limits should be raised and regulations on multifamily and mixed-use development relaxed to unlock housing capacity potential.

5. Leverage public land resources: TOD projects should first be launched on government-owned parcels and large parking lots to reduce development resistance, accelerate implementation, and ensure public benefits and affordable housing goals.

​In conclusion, the findings of this study not only provide a data foundation and spatial analysis framework for TOD site selection and policy-making but also lay the groundwork for integrating policy reforms, financial investments, and community engagement to advance transportation improvement, housing equity, and regional economic vitality in a coordinated manner.

​Appendix

Figure 3 shows the spatial distribution of transit accessibility scores based on proximity to existing bus stops and routes (2024). The highest level of accessibility (score 5, distance less than 0.25 miles) extends outward from the Iowa City downtown area, especially around the city center and the University of Iowa campus, where bus routes are most concentrated. Coralville also demonstrates high transit accessibility along its main southeastern corridor, while outer areas have much lower accessibility (scores of 2 or below). In contrast, North Liberty has limited transit coverage, with most areas located more than 0.75 miles from the nearest bus stop.

Overall, the central area of Iowa City, most of the southeastern part of Coralville, and the central area of North Liberty show good transit accessibility and are suitable as TOD site selection points, while other areas still display characteristics of transit service gaps. This spatial pattern has significant implications for TOD implementation. Transit accessibility is a core element of TOD planning and execution, and areas with higher transit accessibility strongly overlap with communities targeted for equity interventions (those with high poverty rates and a high proportion of zero-vehicle households), further highlighting their suitability as priority areas for TOD initiatives. 

Figure 4 shows the spatial distribution of adjusted poverty scores in Iowa City, Coralville, and North Liberty, based on ACS 2019–2023 five-year estimates (excluding enrolled students). High-poverty areas (poverty scores of 4 and 5) are mainly concentrated in downtown Iowa City and parts of the southern area, especially the city center core and its surrounding neighborhoods. In addition, some scattered high-poverty areas are observed in the southwestern part of Coralville. In contrast, poverty scores in North Liberty remain consistently low (poverty rate < 8%), reflecting its relatively higher socioeconomic status.

These spatial distribution patterns are highly significant for our Transit-Oriented Development (TOD) analysis. Prioritizing TOD-related interventions in areas with an adjusted poverty score of 4 or higher can improve housing options and enhance public transit accessibility. This approach supports the goal of creating inclusive, transit-friendly communities and aligns with the equity-focused TOD strategy of this study. 

Figure 5 shows the spatial distribution of zero-vehicle household scores in Iowa City, Coralville, and North Liberty, based on ACS 2019–2023 five-year estimates. Areas with a high concentration of zero-vehicle households (scores 4 and 5, proportion ≥ 13%) are mainly located in downtown Iowa City, particularly in the city center and surrounding neighborhoods. This indicates a higher dependence on the public transit system in these areas. Coralville shows a moderate level in its southern areas, suggesting medium transit dependency. In contrast, North Liberty has a relatively low proportion of zero-vehicle households (mainly scores 1 and 2, proportion < 6%), reflecting higher car dependency.

​These spatial distribution patterns are highly relevant to TOD planning because communities with a higher proportion of zero-vehicle households are more likely to rely on public transportation. Concentrating TOD measures in these areas can enhance travel equity and improve connectivity for transit-dependent populations, which aligns with this project’s goal of promoting inclusive and sustainable transit-oriented development. 

Figure 6 shows the distribution of potential housing capacity in Iowa City, Coralville, and North Liberty, calculated based on remaining developable residential density. Areas with the highest capacity (scores 4 and 5, more than 15 units per acre) are limited in number and concentrated near downtown Iowa City, reflecting opportunities for infill development. In the southern, eastern, and northern parts of Iowa City, there are several areas with a score of 3 (7–15 units per acre). Overall, Coralville exhibits medium to low-capacity levels, while North Liberty shows predominantly low capacity, though both cities have some higher-potential areas along major corridors. Non-residential zones shown in gray remain unsuitable for residential development. Therefore, at the city scale, Iowa City has a much higher housing capacity potential, with a noticeable gap compared to the other two cities.

In addition, this map highlights zoning districts with policy support for TOD. These areas are mainly distributed in and around Iowa City’s urban core and in some parts of Coralville. Several zoning districts explicitly supporting TOD development (shown in purple) align with high-capacity areas, further reinforcing their suitability for future TOD initiatives. These findings underscore critical opportunities to prioritize zoning adjustments and infrastructure investments in areas that combine development potential with policy support, in order to maximize the impact of TOD implementation. 

Figure 7 shows the employment density scores, reflecting the concentration of jobs in Iowa City, Coralville, and North Liberty. The highest employment density (scores 4 and 5) is concentrated in downtown Iowa City, the University of Iowa area, and parts of the southeastern region, highlighting the city’s role as a regional employment center. An additional high-density corridor extends northwest along the transit route to Coralville. North Liberty shows localized job clusters within its commercial and industrial areas, but most of its territory has low employment density (scores 1 and 2).

These spatial distribution patterns are critical for Transit-Oriented Development (TOD) planning because areas with higher employment scores offer more job opportunities and concentrated activity, making them suitable for promoting TOD that balances housing and employment while reducing commuting distances. Integrating TOD strategies with these corridors and nodes can improve accessibility, support sustainable growth, and increase public transit ridership.

Figure 8 shows the Point of Interest (POI) density scores based on census blocks, reflecting the concentration of commercial, service, and public facilities within Iowa City, Coralville, and North Liberty. The highest POI density (scores 4 and 5) is concentrated in downtown Iowa City, particularly in the city center and around the University of Iowa, forming the main commercial and service hub. Coralville shows medium POI density along the northwest corridor near the transit route, while POI distribution in North Liberty is scattered, with overall low scores (mainly scores 1 and 2). Areas with higher POI density provide stronger living convenience and are more attractive for residents and developers.

​These POI-dense areas not only support mixed-use development but also enhance walkability and overall convenience, thereby increasing public transit usage. Integrating TOD strategies with these high-POI areas helps promote a compact and functionally diverse urban development pattern. 

​Figure 9 shows the distribution of government-owned land (excluding parks) within Iowa City, Coralville, and North Liberty. These parcels, marked in orange, are scattered across different areas of the three cities, but their overall number is small, their size is limited, and there is no clear spatial pattern. A small cluster effect is observed mainly in the southeastern part of Coralville, while Iowa City and Coralville display a more dispersed distribution, and North Liberty shows an even sparser distribution.

​For TOD planning, these government-owned parcels can provide significant advantages for final TOD site selection, as they offer potential opportunities for publicly led development, infrastructure construction, and affordable housing projects. Prioritizing the use of these parcels, which come with policy leverage, can reduce development barriers, accelerate implementation, and better achieve equity goals and the integrated development vision centered on public transportation.

Figure 10 shows the distribution of residential parking lot area scores (represented by centroids) within Iowa City, Coralville, and North Liberty. Parking lots with higher scores (area ≥ 3.5 acres) are limited in all three cities and are mainly concentrated in downtown Iowa City and some southern parcels. Most parking lots are smaller in size (scores 1 and 2) and are concentrated in low-density development areas. However, all parking lots shown on the map have certain redevelopment potential (with an area greater than 0.5 acres and a width greater than 30 feet).

For TOD planning, large parking lots have significant redevelopment potential and can be identified as underutilized parcels, especially in areas near transit corridors and employment centers. Converting these sites into mixed-use or residential developments can improve land-use efficiency, reduce dependence on private cars, and promote a compact, transit-oriented urban development model.

Data Sources

Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [Data set]. IPUMS. https://doi.org/10.18128/D050.V19.0

Used for obtaining census tract, block group, and block-level demographic and socioeconomic data，including poverty rates （exclude students），zero-vehicle households, total housing units, and corresponding shapefiles (tract, block, place boundaries).

Iowa Department of Transportation. (2024). General Transit Feed Specification (GTFS) data for Iowa transit providers [Data set]. Iowa DOT. https://data.iowadot.gov/maps/7222eb942cfa4d0fb764298e2f18598f/about

Used for obtaining route-level transit accessibility and stop coverage for Iowa City, Coralville, and North Liberty based on GTFS data.

City of Iowa City. (2024).

Iowa City zoning map layer [GIS data]. City of Iowa City. https://maps.iowa-city.org/server/rest/services/GeneralData/IC_Zoning/FeatureServer

Used for identifying zoning district boundaries and allowed residential density in Iowa City.

City of Coralville. (2024). Coralville zoning districts [GIS data]. Provided by T. Sorgenfrey, GIS Specialist, City of Coralville, via personal communication.

Used for identifying zoning district boundaries and estimating allowed residential density in Coralville.

​City of North Liberty / Johnson County, Iowa. (2024). North Liberty zoning districts [GIS data]. https://nc.johnsoncountyiowa.gov/index.php/s/EBJeCDLRaPcZm7N

Used for identifying zoning district boundaries and estimating allowed residential density in North Liberty.

City of Iowa City. (n.d.). Zoning code – Title 14: Zoning [Municipal code]. https://codelibrary.amlegal.com/codes/iowacityia/latest/iowacity_ia/0-0-0-19813

Used to obtain permitted land uses and minimum lot size requirements for each zoning district in Iowa City.

City of North Liberty. (n.d.). Zoning code – Chapter 165: Zoning regulations [Municipal code]. https://codelibrary.amlegal.com/codes/northliberty/latest/northliberty_ia/0-0-0-4925

Used to determine permitted uses and residential density standards for North Liberty.

City of Coralville. (n.d.). Zoning code – Title XVI: Zoning regulations [Municipal code]. https://codelibrary.amlegal.com/codes/coralville/latest/coralville_ia/0-0-0-6773

Used to identify zoning definitions and calculate potential residential capacity in Coralville.

Johnson County, Iowa. (2024). Parcels auditor year [GIS data]. Johnson County GIS. https://iowagisdata.org/index.php/apps/files/files/854?dir=/County/Johnson/Open

Used for identifying parcel boundaries, ownership attributes, and government-owned properties in Johnson County.

​U.S. Census Bureau, Center for Economic Studies. (2022). OnTheMap: Work area profile analysis, Johnson County, IA (employment locations) [GIS data]. https://onthemap.ces.census.gov/

Used to estimate employment density and job accessibility across Johnson County, including Iowa City, Coralville, and North Liberty.

​Johnson County, Iowa. (2025a). Building footprint [GIS data]. Johnson County GIS. https://iowagisdata.org/index.php/apps/files/files/854?dir=/County/Johnson/Open

Johnson County, Iowa. (2025b). site addresses  [GIS data]. Johnson County GIS. https://iowagisdata.org/index.php/apps/files/files/854?dir=/County/Johnson/Open

Johnson County, Iowa. (2025c). transportation surfaces [GIS data]. Johnson County GIS. https://iowagisdata.org/index.php/apps/files/files/854?dir=/County/Johnson/Open

Used to cross-identify parking lots across Johnson County, including Iowa City, Coralville, and North Liberty. And Point of Interest (POI) locations and calculate kernel density scores across Iowa City, Coralville, and North Liberty.

Johnson County, Iowa. (2025d). Parks [GIS data]. Johnson County GIS. https://iowagisdata.org/index.php/apps/files/files/854?dir=/County/Johnson/Open

​Used to locate and exclude park areas that are not suitable for TOD development on the map.

Federal Emergency Management Agency. (n.d.). FEMA flood map service center [Map service]. U.S. Department of Homeland Security. https://msc.fema.gov/portal

Used to locate and exclude floodplain areas that are not suitable for TOD development on the map.

​References

City of Calgary. (2004). Best Practices Handbook for Transit Oriented Development. http://reconnectingamerica.org/assets/Uploads/bestpractice031.pdf

​Fard, P. (2013). Measuring transit oriented development: Implementing a GIS-based analytical tool for measuring existing TOD levels (Master’s thesis, University of Twente). University of Twente Faculty of Geo-Information Science and Earth Observation.

Woodsong, G. G. & Metropolitan Transportation Commission. (n.d.). GIS for TOD: Transit Oriented Development in the Bay Area. In Metropolitan Transportation Commission. https://proceedings.esri.com/library/userconf/proc06/papers/papers/pap_1473.pdf

 Reconnecting America. (2007). Why transit-oriented development and why now. The Center for Transit-Oriented Development.

Metropolitan Transportation Commission. (2005, June). Beyond maps: GIS support of transit-oriented development planning (ESRI Paper UC1741). Paper presented at the ESRI International User Conference, San Diego, CA. https://proceedings.esri.com/library/userconf/proc05/papers/pap1741.pdf

California Department of Transportation. (2002). Statewide transit-oriented development study: Factors for success in California.