Using ArcView to Create a Transit Need Index John Babcock GRG394 Final Presentation

Objective Demonstrate an application of GIS technology in transit planning; Demonstrate the methodology and applicability of the Transit Need Index as a strategy for forecasting transit use Learn to use the Spatial Analyst extension for ArcView

Background Transit planners use a variety of methods to forecast transit use for different service area. One such method is the Transit Need Index (TNI), which allows the comparison of different parts of the service area based on relative ability to generate transit use

Background Before the use of GIS software, this was generally done through the use of hard-copy census data and manual creation of color-coded maps. The process was very slow and the end products were difficult to replicate; with new data, the process had to be started over again.

Factors Enabling the Use of Arc/View for TNI Generation Availability of desktop GIS software: allows geographical display of quantitative information; allows easy updating and reformulating of data Availability of electronic census data at the block group level, downloadable on-line

Steps in Creating a TNI Acquisition of Data Data Preparation Selection of Demographic Factors Creation of Raster Maps Analysis of Data

Step 1: Data Acquisition For this project, U.S. Census TIGER file data was used; these files contain demographic data at the Census Block Group level (in this case, for Harris County, TX).

Creating the TNI Step 2: Data Preparation The attribute table for the census block group theme must be joined to the demographic data dbase table; to do so, a new field must be created in each with a unique number for each block group

Creating the TNI: Step 3: Thematic Mapping Once the tables are joined, thematic maps can be created using the data; for example, this map displays the population density of Harris County

In predicting the ability of a block group to generate transit ridership, a variety of demographic characteristics are relevant. They could include: Population Density Car Ownership Economic Characteristics Place of Employment Age Characteristics Other factors

Although these factors may all have predictive value, mapping them individually may not be useful For example, a tract may have a high population density, which would predict high transit use; yet the same tract may also have 100% car ownership, which predicts low transit need. The advantage to aggregating the factors is that they give you a more accurate picture of a whole range of predictive values.

The use of a raster-based GIS system allows planners to combine predictive demographic variables to create an aggregate transit need map. This map will show the relative transit need (versus the local median). It could also be made to compare local characteristics to non-local values.

Creating the TNI: Step 4: Selection of Demographic Characteristics Before creating raster layers, planners must select which demographic characteristics have predictive value for transit use. Although there are many possibilities, for the purposes of this project, I have chosen six variables:

Selected Demographic Characteristics Population Density (Housing units/acre) Percent of households not owning an automobile Percent of population aged Percent of population aged over 65 Percent of the population that is employed Percent of population working in the City of Houston

The values for each demographic characteristic vary widely and are not necessarily comparable (for example, density is in units/acre while other characteristics are in percentages). In order to compare them, each characteristic must be adapted to a similar scale.

Because the purpose of the TNI is to compare characteristics to the local medians, all values in the median range were set to zero. Values above and below the median were labeled 1 to 10, based on the relative distance from the median. This gave each characteristic a value from around -5 to around 5.

Step 5: Creating Raster Maps A separate raster grid map must be created for each of the demographic characteristics I am generating. To create a raster map, I first had to create a vector map, then use the Spatial Analyst extension to convert it to a raster map

Translating Vector Data to Raster Data: Density, Vector Map

Translating Vector Map to Raster Map: Density, Raster Map

Translating Vector Maps to Raster Maps Continue to change all the other vector maps to raster maps (in this case, percentage of households with no car)

Step 6: Aggregating the Raster Data Once a raster map for each demographic characteristic has been created, the maps can be combined using the “Calculator” function under the “Analysis” menu

Step 6: Aggregating the Raster Data Not all of the demographic factors I am considering have an equal effect on transit ridership generation; as a result, the formula I use in combining them should weight certain factors more heavily than others

Step 6: Aggregating the Raster Data Because car ownership is probably the best predictor, I am adding a 2x modifier On the other hand, I believe the >65, teen, and employment statistics are weaker, so I will multiply them by 0.5 I will leave density and work in Houston unchanged

Step 6: Aggregating the Raster Data Accordingly, my formula looks like this: Aggregate Data=2 x (Car Ownership) +Population density+percent working in Houston+0.5 x (percent >65)+0.5 x (percent teen)+0.5 x (percent employed)

Step 6: Aggregating the Raster Data Or, in the calculator:

Step 7: Analysis of Data Once the formula is applied, the aggregated map of Harris County’s transit need looks like this:

Step 7: Analysis of Data Additional map layers can be overlaid to help analyze the map data:

Step 7: Analysis of Data The map layers can also be used to analyze data at a very detailed level

Step 7: Analysis of Data The map can also be adjusted to show other information; on this map, the darker areas are those with a transit need level higher than the city median

Why is this Useful? Transit agencies can overlay their route structures to ensure they match their service area’s transit needs Agencies can identify areas of high potential transit need that are not being served Neighborhood advocates can use the method to demonstrate that they are being underserved

Why is this Useful? Ridership statistics can be matched to levels of transit need; then, forecasts of transit use can be made for areas of similar need The same methodology can be used to determine the need for other services, such as roads, water, waste, social services, etc.

How can the model be improved? Other demographic factors can be added to improve the model Statistical data can be used to refine the weights of demographic data used The model can be adapted to include transit attractors as well as generators

Problems The model does not necessarily take into account unique characteristics of certain cities (such as natural boundaries, infrastructure quality, etc.) Statistical modeling does not necessarily predict human behavior, especially with small numbers of people (example: the rail versus bus issue)

Problems The model used may not be accurate Statistics may be out of date There may be other causal factors not accounted for in census data (such as personal preferences, crime fears, poor or inconsistent transit service, etc.)

Conclusion The use of Spatial Analyst for geographical need indexing can be a useful tool for transit demand modeling and forecasting The quality of the end product is heavily dependant on the quality of data and the accuracy of the model Models should improve over time as more site-specific data is gathered and gradual improvement to the model are made