*Using Geospatial Analysis to Assist with Clean Vehicle Infrastructure DOI: http://dx.doi.org/10.5772/intechopen.110864*

Land use


#### **Table 1.**

*Input demand factor scoring.*

model was first applied to separate the landscape into two categories: mountains and valley floor. The subsequent layer was converted to a polygon and intersected with a major lake feature class to get polygons of mountain and lake areas. A score of −1 (unsuitable) was assigned to mountain and lake areas, a score of 1 (suitable) was assigned to valley floors, and a score of −1 (unsuitable) was assigned to a separate polygon layer that contains major rivers. A score of −5 (unsuitable) was given to protected wetlands in the study area, which protects these locations from being used for potential EV charging stations.

A score of 5 (high suitability), meaning a strong attraction to the driving public, was assigned to the majority of the input demand factors for infrastructure. Facilities that are often visited by the public belong to this category and are assigned a score of 5. These infrastructures are identified as gas stations, airports, health care facilities, libraries, golf courses, government buildings, postal offices, schools, shopping malls, places of worship, senior centers, popular parking garages, park and ride lots, major attractions such as parks and ski resorts, as well as major road intersections and roads. A score of 3 (moderate suitability) was given to government fuel sites since these locations might be unavailable to the traveling public. Adding these sites to the analysis would encourage government agencies that utilize fleet vehicles to invest in EV technology. Existing EV charging stations were the only negative infrastructure input factor that was assigned a −5 (unsuitable) score. The reason for this negative input was to reduce the chance of highlighting areas that already had an EV charging station established.

Analysis of the distribution of demographics assisted with the perspective of socioeconomics in the area. The current and future population trends, employment and employers, and trip destinations were all evaluated for the study area to assign the scoring number for the input demand factors. A quantile classification was run in ArcMap to score the 2040 employment estimates contained in the Traffic Analysis Zones (TAZ). Areas of high employment (score of 5), medium employment (score of 3), and low employment (score of 1) were labeled respectively based on the classification. The 2040 TAZ population estimates were analyzed using a different approach. Most EV owners will generally have a home EV charging station installed in their homes for convenience. This factor needed to be taken into consideration since the targeted locations for this research project focused on trips away from home. To avoid highlighting residential areas, the population was analyzed on a regional scale and used as an added factor to the overall analysis. This allowed for

*Using Geospatial Analysis to Assist with Clean Vehicle Infrastructure DOI: http://dx.doi.org/10.5772/intechopen.110864*


#### **Table 2.**

*Largest employers in the state of Utah.*

high to moderately populated areas to receive additional points to better identify potential locations of high EV charging usage. Areas of high to moderate population were given a score of 2, while areas of the low population received a score of 1. The Utah Department of Workforce Services identifies Utah's largest employers on an annual basis [28]. Out of the top 10 employers in Utah, 9 of them reside along the Wasatch Front as seen in **Table 2**. These employers generate a high number of workrelated trips and therefore were given a score of 5 (high suitability).

The State of Utah conducted a Household Travel Survey in 2012 to better understand the travel patterns of the driving public. The trip destination portion of this study was utilized in this research project to identify areas where the traveling public visit the most frequently. A quantile classification was performed to identify areas of trip frequency. The above-average trips are scored 5, a moderate amount of trips are scored 3, and low trips are scored 1.

Land use plays a direct role in determining the types of destinations that are taken by the driving public, therefore the attractiveness of the land use type determines the scores for this input factor. Plans for general land use of each county was referenced to project current and future land uses. Land use types that offer services and employment usually attract a high number of destination trips, such as lands used for commercial, industrial, government, institutional purposes, or mixed use of these functions were scored 5 with high suitability for charging stations. On the other hand, environmental land use types such as agriculture, open space, and sensitive areas are unsuitable for installing charging stations and were scored −5. Charging stations for residential use are generally installed in private residences for owners of vehicles. Residential areas were unsuitable and therefore scored −4. This scoring process eliminates the possibility of residential areas being highlighted but also prevented adjacent optimal land use types from being overshadowed.

Each input demand factor was produced as a separate grid to illustrate destination attractions and spatial distribution of the demand factor within the study area. Each of the resulting input demand factor grids was composed of 16,790 individual 0.5 × 0.5-mile grid squares. **Figure 3** illustrates all the 28 input demand factor grids. In these maps created, input factors with positive score are shown in blue whereas negative scores are colored orange. There were a total of 23 out of 28 input demand

**Figure 3.** *Maps of the demand factor grids.*

factor grids that received a positive score. As shown in **Figure 3**, these positive input demand factor grids are characterized as major transportation hubs, employment centers, shopping districts, public facilities, health care centers, recreational areas, future populations, and trip destinations. The resulting positive input demand factor grids played an important role in determining the final output of this suitability

#### *Using Geospatial Analysis to Assist with Clean Vehicle Infrastructure DOI: http://dx.doi.org/10.5772/intechopen.110864*

analysis. Only 3 input demand factor grids received all negative scores which included existing EV charging stations, rivers, and wetlands. These particular negative input demand factor grids helped to deter these locations from being identified in the final analysis. Also, 2 of the input demand factor grids contained both positive and negative scores. These specific input demand factor grids represented land use and physical features. Additionally, these input demand factor grids assisted in identifying land area types that were suitable and unsuitable for locating EV charging stations within the study area. The scoring system contained in these input demand factor grids became the vital stepping stone that allowed for the final results to be generated. Converting the initial input datasets into these uniform input demand factor grids allowed for a final composite score to be spatially calculated. The final composite scores were used to display the final results as a hot spot map.

A comparison of each input demand factor grid revealed a unique spatial distribution pattern. This unique pattern outlined where these input demand factors were concentrated along the Wasatch Front area. The majority of these input demand factors were located in urbanized centers or adjacent to major transportation facilities. This spatial pattern became very apparent when these input demand factor grids were compared with Utah's major transportation facilities. I − 15 is the principal interstate that runs north and south through the entire State of Utah. Many of the input demand factors throughout the Wasatch Front were generally concentrated near this major transportation facility. This spatial concentration allows for greater access to these particular input demand factors. Prime examples that illustrate this spatial phenomenon can be seen in the following input demand factor grids: government offices, gas stations, libraries, major attractions, major employers, park & ride lots, post offices, and senior centers. Salt Lake County differs a bit from other counties along the Wasatch Front when it comes to core concentrations of input demand factors along I − 15. This distribution difference is due to a higher population density and the effects of urban sprawl that has led to a greater spatial expansion of input demand factors in this area. Salt Lake County is home to more than one interstate; I-15, I-80, and I-215, which provides greater accessibility than any other surrounding counties in the study area. This wider spatial distribution of input factors within Salt Lake County is also apparent in many of the resulting input demand factor grids. Higher concentrations of input demand factors will allow for a greater probability of locating a greater number of EV charging stations within Salt Lake County. Other input demand factors that did not necessarily follow the core concentration concept were factors that provided services to smaller geographic areas such as local parks, places of worship, health care facilities, schools, and ski resorts. These input demand factors had a broader geographical stretch compared to other input demand factor grids. This allows for areas outside the core concentration area to be considered and assists in developing a more expansive EV charging station system. These individual input demand factor grids help portray each input factor's influence in the overall analysis.
