An Emerging Development Pressure Index for Corridor Planning Jessica Y. Guo National Center for Freight and Infrastructure Research and Education Department of Civil and Environmental Engineering University of Wisconsin, Madison 2205 Engineering Hall, 1415 Engineering Drive Madison, WI 53706
[email protected] Dadit G. Hidayat National Center for Freight and Infrastructure Research and Education Department of Civil and Environmental Engineering University of Wisconsin, Madison 2205 Engineering Hall, 1415 Engineering Drive Madison, WI 53706
[email protected] David Cipra Wisconsin Department of Transportation Room 933, 4802 Sheboygan Avenue Madison, WI 53707
[email protected]
ABSTRACT Recognizing the importance of consistency in the state’s corridor planning activities, the Wisconsin Department of Transportation uses a statewide, systematic process for identifying priority management corridors. As part of this prioritization process, an index is needed to reflect the likelihood of future residential and commercial development. This paper describes a methodology for creating such an index, referred to as the Development Pressure Index (DPI). A high DPI value suggests a higher likelihood of future growth around the highway segment and indicates the need for increased level of service or capacity on the highway segment. A wide range of data, from population and economic projections to land development plans and forest protection programs, are incorporated into the computation of the proposed DPI. A geographic information system is used to perform various spatial join and aggregation methods to derive a set of growth indicators. A scoring and weighing process is then applied to collapse the multiple indicators into one index. The paper concludes by discussing the validity of the proposed methodology and possible directions for further enhancement. Key words: corridor planning—corridor prioritization—emerging development
Proceedings of the 2007 Mid-Continent Transportation Research Symposium, Ames, Iowa, August 2007. © 2007 by Iowa State University. The contents of this paper reflect the views of the author(s), who are responsible for the facts and accuracy of the information presented herein.
1. INTRODUCTION State highways represent billions of dollars of transportation capital investment. As with any other transportation asset, a highway system needs continual investment to maintain, update, and expand. Due to the limited availability of public funds and resources, it is important to make these investment decisions in an objective and equitable manner. In Wisconsin, these decisions are made based on the corridor management philosophy and approach, with considerations of the highway facility in the context of surrounding land uses, access management, condition of adjacent facilities, etc. The corridor management process begins by identifying priority corridors that warrant specific attention due to existing mobility problems, safety concerns, or emerging development pressure. Once these priority corridors are identified, a management vision is then developed and implemented for each corridor through the coordinated application of various planning activities, strategies, and tools. Recognizing the importance of consistency in the state’s corridor planning activities, the Wisconsin Department of Transportation (WisDOT) employs a systematic, statewide process for identifying priority management corridors. The process includes two analysis stages: a quantitative analysis conducted at the state level and a qualitative analysis conducted at the district level (WisDOT 2004). In the first stage, WisDOT staff evaluate the state trunk highway (STH) segments (approximately 15,000 segments in total) along three dimensions: mobility, safety, and development pressure. The evaluation along each dimension involves first assigning to each STH segment a set of scores corresponding to the constituting factors. The scores are then weighted and summed across all factors to give an index value. The weighted sum of the mobility index, safety index, and development pressure index forms the final priority score. The priority scores obtained from the quantitative analysis stage are then provided to the district planning agencies for the second stage of the priority corridor identification process. In this stage, the district staff review the priority scores in conjunction with local knowledge and qualitative considerations to determine the highpriority corridors for their respective districts. This paper describes a framework developed to enhance WisDOT’s prior approach for evaluating the development pressure along STH segments during the first stage of their corridor prioritization process. For the purpose of corridor prioritization, development pressure is defined as the likely intensity of future residential and commercial development in the vicinity of a STH segment. High development pressure indicates the need for an increased level of service or capacity on the highway segment. Currently, WisDOT determines development pressure based on three factors: population growth, employment growth, and land conversion rate. The population growth is measured by the GEH statistic, computed based on the population size of the base year (2000) and the projected population for year 2020. The employment growth is also measured by the GEH statistic, computed in a similar fashion. Land conversion is measured by the number of conversions from agricultural/vacant land to residential, commercial, or manufacturing uses. The values for all three factors are first computed for each city, town, and village (CVT) and subsequently mapped to the individual STH segments. There are at least two issues with WisDOT’s current approach for estimating the development pressure along STH segments. First, since CVTs are large administrative areas that may contain up to hundreds of STH segments, the mapping of population growth, employment growth, and land conversion rates from CVTs to STH segments inevitably leads to high homogeneity in the factor values, and the corresponding scores, among the STH segments. This lack of spatial variation and accuracy prevents planning agencies from being able to pinpoint the location of problematic corridors. Therefore, a computational approach that provides higher spatial variation and accuracy is much needed. The second issue with the current approach lies in the fact that only three factors are currently used to measure development pressure. Since richer and more detailed geographical data are becoming available both within and outside of WisDOT, it
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is highly desirable to identify and include additional, and perhaps better, indicators of development pressure in the computation of corridor priority scores. In view of the abovementioned issues, in this study we have developed a new Development Pressure Index (DPI) that will be used to replace WisDOT’s current set of three development pressure-related scores. The remainder of this paper describes the development and application of the DPI. Section 2 discusses potential indicators of development pressure. Section 3 describes the various data sources considered for DPI computation. Section 4 provides an overview of the new DPI computation framework. Section 5 presents the results obtained from applying the proposed framework to Wisconsin. Finally, Section 6 summarizes the paper and discusses the general applicability of the proposed framework. 2. INDICATORS OF DEVELOPMENT PRESSURE Our scan of existing literature revealed that most states do not explicitly consider development pressure during their corridor evaluation and prioritization process, though some do consider factors such as population growth projections as indicators of travel demand (for example, Zemotel and Montebello 2002). Therefore, as the first step to developing a new DPI, a panel of transportation planners and engineers from WisDOT was invited to participate in a discussion of what factors would support or impede future development near highway segments. This panel discussion led to the identification of two categories of indicators: inclusion indicators and exclusion indicators. 2.1. Inclusion Indicators Inclusion indicators refer to the factors thought to be positively related to the level of development pressure. For instance, higher (vs. lower) population growth near a STH segment is an indication of greater (vs. weaker) emergent development pressure. This category includes the three factors already used by WisDOT: population growth, employment growth, and land conversion rate. It also includes factors such as real estate development, utility systems extensions, transportation infrastructure improvements, access management plans, and school and business expansions. 2.2. Exclusion Indicators In contrast to the inclusion indicators, the exclusion indicators are those thought to be negatively related to the level of development pressure. Examples of these types of indicators include steep terrain, flood plains, and protected forestry. The presence of the exclusion factors near a STH segment is expected to slow down or prohibit future development near the segment. 3. DATA SOURCES While it is relatively straightforward to identify good candidates for inclusion and exclusion indicators, collecting the necessary data for measuring each of these potential indicators is a challenging process. This is because some data either are not readily available in the desired electronic format or do not cover the entire study area (state of Wisconsin). Some data are proprietary data that either are not accessible or need to be purchased by WisDOT. Other data are simply unavailable from any of the agencies that the research team has contacted. The final dataset used for this study includes 16 complete data items collected from various organizations; 11 of them represent inclusion factors and 5 are exclusion factors. The sources and contents of these data items are summarized in Table 1. All data items are compiled into ArcGIS-compatible format.
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Table 1. Summary of data items used in computing the Development Pressure Index Name
Source Inclusion Factors Annual Growth Rate Wisconsin Department of (AGR) of traffic Transportation Employment Growth Wisconsin Department of Transportation Land Conversion Wisconsin Department of Revenue Population Growth Agriculture Land Cover DPI Student Enrollment Growth Land Value
Planned Development
Railway Stations Tax Incremental District (TID)
Transition Lanes
Wisconsin Department of Administration Wisconsin Department of Natural Resources Wisconsin Department of Public Instruction (DPI) Wisconsin Department of Revenue (Research and Policy Division) Wisconsin Department of Transportation Wisconsin Department of Transportation Wisconsin Department of Revenue
Wisconsin Department of Transportation
Exclusion Factors Elevation Wisconsin Department of Natural Resources Poverty Applied Population Laboratory, University of Wisconsin Managed Forest Law Wisconsin Department of Natural Resources Managed Lands Wetland and Open Water Land Cover
Wisconsin Department of Natural Resources Wisconsin Department of Natural Resources
Content Projected traffic AGR from 2004 to 2024 Projected growth in employment from 2000 to 2025 by traffic analysis zones (TAZ) Observed growth in number of land parcels converted into residential, manufacturing, or commercial use from 1990 to 2006 by CVT Projected growth in population from 2000 to 2025 by CVT Areas covered by agricultural land use Observed growth in number of enrollments from 2000 to 2006 by school district Observed growth in total equalized value of residential, manufacturing and commercial land and improvement from 2000 to 2006 Areas where a traffic impact analysis (TIA) has been conducted recently or where known real estate development has been planned Point locations of existing and planned rail stations Number of active TIDs by CVT. TIDs are created by cities and villages to financially support new development or redevelopment of blighted areas, or areas in need of rehabilitation Location of 'tapers’ – where the number of lanes reduces and traffic bottleneck is likely to occur – on the STH network Digital Elevation Model of Wisconsin that is used to determine terrain slopes of 100m by 100m grid cells Proportion of population below poverty line by census tract Polygons representing land parcels currently enrolled in the Managed Forest Law program, which prevents the land from being used for non-agricultural development Polygons representing land parcels that the WDNR has acquired in fee, easement, or lease Areas covered by wetland or open water
4. DPI COMPUTATION FRAMEWORK The computation process by which the 16 input data items are combined to produce the final DPI is depicted in Figure 1. The process consists of four main steps, as discussed below.
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Geospatial Analysis
Score Assignment
Weight Assignment
DPI Calculation
Figure 1. Main steps in the process of computing the Development Pressure Index 4.1. Geospatial Analysis The first step in the DPI computation process is to produce an array of 16 attribute values for each STH segment based on the input data items. Since the 16 input data layers refer to different types of spatial objects, including lines (highway segments), points (site locations), and polygons (CVT, traffic analysis zones, school districts, census tracts, and land parcels), different spatial join operations have been devised to map the attribute values from each of these data (source layers) onto the STH segments (target layer). This geospatial analysis step is key to the integration of various spatial data sources. A total of four types of geospatial operations are used. These are illustrated in Figure 2 and are discussed in turn below.
Figure 2. Operations involved in computing the Development Pressure Index Case 1. Mapping from STH Segments to STH Segments In the simple case where the source layer describes the same polyline objects as those in the target layer (for example, AGR Traffic and Transition Lanes), the source layer can be directly mapped to the target layer through a simple one-to-one join based on object ID.
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Case 2. Mapping from Points to STH Segments This case includes the Railway Stations data. The mapping from points to polylines requires that a buffer first be created around the highway segments. The number of rail stations that fall within each buffer area is then obtained using a location-based spatial join. A buffer size of 0.25 miles was used. Case 3. Mapping from Polygons to STH Segments Several data items describe polygons in space, including Agriculture Land Cover, Traffic Planned Development, Managed Forest Law, Managed Lands, and Wetland and Open Water Land Cover. The influence of each of these factors on a highway segment is measured by the amount of the corresponding polygons that fall within a buffer area defined around the segment. A buffer radius of one mile is applied to all of these polygon data. Case 4. Mapping from Zones to STH Segments A common type of source data is zone data. These are data that describe the quality of administratively or analyst-defined spatial units that are nonoverlapping and that collectively cover the entire study area. The different zoning systems involved in this study include CVT, traffic analysis zones, school districts, census tracts, and grid cells. Mapping attribute values from these zoning systems to STH segments requires a buffer to be first defined around the segments, then followed by spatial joins based on location. When multiple zones are overlaid onto a single segment buffer, appropriate aggregation methods (such as sum, average, or max) are applied to collapse multiple zonal values into one. The choice of aggregation methods depends on the nature of the factor. 4.2. Score Assignment Once all 16 factor values are obtained for each STH segment, the next step is to assign a score for each factor. We accomplish this by first ranking all segments based on each of the 16 factors. Each segment is then assigned with 16 separate score values, ranging from 0 to 10, based on its percentage ranking with respect to the 16 factors. For any given factor, a score value of 0 indicates a relatively low likelihood of emerging development due to that factor; whereas a value of 10 indicates a high likelihood of development. For example, based on the number of Tax Increment Districts (TID) computed for each segment, the segments are rank ordered and are assigned a score of 0 if the segment is among the bottom 40% in the ranking, 3 if between 40% and 60% of the ranking, 6 if between 60% and 80% of the ranking, and 10 if above 80% of the ranking. For exclusion factors, the higher a segment is ranked, the lower its score value will be. For example, after STH segments are ranked based on the amount of Managed Land within a one-mile radius of the segments, a score value of 0 is given to those segments ranked 90% or above, 2 to those ranked between 80% and 90%, 5 to those ranked between 60 % and 80%, and 10 to those ranked in the bottom 60%. 4.3. Weight Assignment Depending on the spatial accuracy, timeliness, and other qualities of the input data, the 16 factors represent varying degrees of strength in explaining emerging development pressure. In order to determine the relative importance of these factors, a peer review process involving five panelists from the Bureau of Planning and Economic Development at WisDOT was conducted. Each panelist was asked to rate the 16 factors using a five-point scale, with 1 being very unimportant and 5 being very important. For each factor, the average of the scores provided by the five panelists was computed and used as the weight of
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the factor. The five factors found to have the highest weights were traffic annual growth rate (4.6), employment growth projection (4.4), population growth projection (4.2), land conversion rate (4.2), and planned development (4.0). The ones with the lowest weights are railway stations (2.0), poverty rate (2.0), elevation (2.0), managed forest law (2.2), and managed land (2.4). 4.4. DPI Calculation Once the score values are assigned and the weights are defined for each factor, we can compute a raw DPI value for each STH segment as the weighted sum of score values:
Raw_DPI l = ∑ wk ⋅ Slk
(1)
k
where
Raw_DPI l is the raw DPI value of the lth STH segment wk is the weight given to the kth factor Slk is the score assigned to the lth STH segment for the kth factor Based on the above equation, the highest raw DPI value that a highway segment can possibly attain is 160. In practice, however, the highest value found across all STH segments is likely to be lower. We denote this highest observed value as Max _ Raw_DPI . This value is then used to normalize all DPI values as follows so that the final DPI takes a value between 0 and 10:
DPI l =
Raw DPI l ⋅ 10 Max _ Raw_DPI
(2)
5. RESULTS The results obtained from applying the DPI computational process to the STH network of Wisconsin are described in this section. These results have been verified using statistical analysis and thematic mapping. 5.1. Statistical Analysis Since the DPI values are computed as a function of many scores, the level of correlation among the constituting scores is a concern. The simultaneous inclusion of highly correlated indicators should be avoided, as it would lead to biased results. Our analysis of correlation coefficients between all pairs of 16 scores (as shown in Table 2) reveals that the highest correlation is between the scores for TID and Land Value, with a correlation coefficient of 0.53. The correlation between most of the remaining pairs of scores is generally low, suggesting that little “double counting” effect has been introduced into the DPI values. The normalized DPI values obtained for the 14,912 segments on the Wisconsin STH are found to have an average value of 4.85 and a standard deviation of 1.60. The frequency and cumulative frequency distributions of the values are shown in Figure 3. Overall, the frequency distribution resembles a bell shape, which typically suggests a normal distribution.
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Ma na For ged est La w Ma nag Lan ed ds We tla n d Op en and Lan Wat e r dC ove r
ver ty Po
vat i on Ele
Tra ns Lan i ti on es
TI D
me nt Ra ilw Sta ay t ion s
P la nn De e d vel op
Em pl Gro oym e wt h nt Lan d Co nv e rsi o n Po pula Gro t ion wt h Ag ri cu Lan l tura l dC ove r DP IS En tuden roll me t nt Lan dV a lu e
AG R
Correlation Coefficient
Tr a ffi c
Table 2. Correlation coefficients between all pairs of scores constituting the DPI
AGR Traffic
1.00
Employment Growth
0.06
1.00
Land Conversion
0.26
0.03
1.00
Population Growth
0.13
0.36
0.21
1.00
Agricultural Land Cover
0.20
0.01
0.43
0.19
1.00
DPI Student Enrollment
-0.03
0.01
0.02
0.04
0.02
1.00
Land Value
0.22
0.26
0.50
0.27
0.27
0.05
1.00
Planned Development
0.26
0.10
0.29
0.22
0.25
0.03
0.41
1.00
Railway Stations
0.06
0.02
0.12
0.04
0.01
0.01
0.13
0.05
1.00
TID
0.11
0.22
0.18
0.10
0.16
0.01
0.53
0.21
0.09
Transition Lanes
0.07
0.24
0.06
0.12
0.01
0.04
0.51
0.22
0.06
0.46
1.00
Elevation
0.00
0.03
0.02
0.07
0.07
0.02
0.09
0.07
0.01
0.08
0.18
1.00
Poverty
0.02
0.15
0.09
0.10
0.13
-0.01
0.15
0.04
0.02
0.09
0.12
0.05
1.00
Managed Forest Law
0.05
0.14
0.07
0.13
0.08
-0.04
0.18
0.06
0.04
0.13
0.12
0.01
0.42
1.00
Managed Lands
0.13
0.00
0.30
0.18
0.36
0.06
0.12
0.12
-0.04
-0.21
-0.05
0.10
0.08
0.03
1.00
Wetland and Open Water Land Cover
0.02
0.23
0.03
0.20
0.05
0.06
0.13
0.07
0.00
0.12
0.15
0.01
0.11
0.12
0.07
1.00
1.00
Distribution of DPI Values 100%
1800
90%
1600
80%
1400
70%
1200
60%
Frequency
2000
1000
50%
800
40%
600
30%
400
20%
200
10%
0
0% 0
1
2
3
4
5
6
7
8
9
10
Normalized DPI Value Frequency
Cumulative %
Figure 3. Distribution of DPI values computed for Wisconsin STH segments
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5.2. Thematic Map The spatial distribution and variation of the DPI values can be best visualized in a thematic map, shown in Figure 4. The darker the line color, the higher the development pressure is. The thematic map suggests that there is significant variation in DPI values across space. The map also shows spatial clusters of segments with similar DPI values. The areas with a concentration of high DPI values are highlighted in Figure 5. These areas include Appleton, outskirts of Milwaukee, south of Madison, and Eau Claire. Detailed maps of these areas were reviewed by selected WisDOT staff who have strong knowledge of the state’s development pattern. The reviewers verified that these areas are indeed among those undergoing significant growth and expecting increased traffic volumes in the near-term future. It is also noted that, although currently urbanized areas tend to receive relatively high DPI values, the highest DPI values are found in the urban fringes, where spillover growth and development is imminent.
Figure 4. Thematic map based on normalized DPI values
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Eau Claire
Appleton
Madison
Milwaukee
Figure 5. Areas with highest levels of development pressure, as indicated by the proposed DPI 6. CONCLUSIONS This paper has described the systematic development and application of a DPI that can be used as a predictor of emerging development pressure along a STH segment. The DPI represents an integration of a rich set of geographical data that are considered as preliminary indicators of development pressure. The DPI is to be used in conjunction with additional indices of mobility and safety to help WisDOT planners identify priority corridors for further planning activities. Our empirical results and the subsequent assessment of the results reveal that the proposed methodology provides an effective and objective way to account for multiple influencing factors of development pressure.
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It should be noted that the general methodology presented in this study is applicable to the planning-level evaluation and prioritization of corridors in future planning horizons. It is also potentially applicable to communities other than the state of Wisconsin. However, the methodology must be tailored to the planning goals and the data available for the planning activities. In particular, the design of the scoring and weighting schemes depends greatly on the quality of the data available for the indicators of consideration. The scoring and weighting schemes also need to be based on the relative emphasis that the public agency and other stakeholders place on the various indicators. Many challenges related to data availability and data quality have been encountered during the course of this study. These challenges arise from the fact that the analysis of development pressure is a data intensive exercise. The analysis calls for data produced by many different agencies that often differ in format and nature, making the task of acquiring and integrating these various datasets laborious and difficult. Moreover, as the data are updated by the different agencies over time, tracking down the latest data and keeping the DPI values up to date presents yet another challenge. The lessons learned from this study point to the need for improving the data interoperability and data accessibility within state departments of transportation and across various state departments in order to support a comprehensive corridor planning process. It is envisaged that, as geographic data of higher quality becomes available in the future, the DPI computational methodology proposed in this study can be further refined in at least a couple of ways. First, the DPI is currently computed for highway segments with a priori defined start and end nodes. The definitions of these segments are often too coarse and too inflexible to support the accurate identification of localities with emerging development. A computational method that is not bound by the STH segment definitions and that allows the DPI to be computed at a higher spatial resolution is desired. However, such a refined methodology would be effective only if data of high spatial resolution were available. Second, the DPI is currently computed based on variables and weights that have been selected in an ad hoc fashion. If historic data were available about the traffic volumes (endogenous variable) and the various development pressure indicators (exogenous variables) being considered, one could develop a multivariate time-series model to determine the statistical significance and the relative explanatory power of the exogenous variables. The model estimation results could then be used to inform the selection of input variables and the associated weights.
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ACKNOWLEDGMENTS The research reported in this paper has been funded by the Wisconsin Department of Transportation (WisDOT). The authors are grateful to the suggestions provided by Bruce Aunet, Mike Schumacher, and Lee Samson from WisDOT and the data support provided by the Wisconsin Department of Natural Resources, Wisconsin Department of Administration, Wisconsin Department of Revenue, and HNTB Corporation. The authors also thank the three anonymous reviewers who provided helpful comments on an earlier draft of this paper. REFERENCES WisDOT. 2004. Executive Summary and Technical Report of the Corridor Management Workgroup. Wisconsin Department of Transportation. http://www.topslab.wisc.edu/workgroups/ sketchplanning/SPT4/Technical_Report_Corridor_Management_WG.pdf Zemotel, L.M. and D.K. Montebello. 2002. Interregional Corridors: Prioritizing and Managing Critical Connections between Minnesota's Economic Centers. Transportation Research Record, 1817, 79–87.
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