Overview

This web app is a small GIS project that was developed in Spring 2015 from start to finish with special emphasis on using ArcGIS Online and the Collector App for data collection and creating a web application for the communication of the results. In addition, a methodology taking into account the current food trucks, the street trash cans, the public eating areas, and the campus buildings has been developed to quantitatively analyze the suitability of each street section on campus to start a new food truck business.

The structure of a GIS project is critical to its success. This project was developed following the six steps of GIS problem solving developed by Dr. Edward Robinson.

1. Selection of Crisp, Clear, Actionable Question or Problem

In the past years, the food truck business on the University of Illinois at Urbana-Champaign campus has experienced a significant growth in the number of customers. The business is booming. As a result, the objective of this project is to conduct a quantitative analysis to find the best locations to start a food truck business.

2. Define Project Methodology

In order to develop a simple yet efficient methodology that would properly analyse the collected data both python and ArcGIS Modelbuilder were used. The methodology can be divided in 5 main parts (one for each type of layer -trash cans, food trucks, eating areas and buildings- and one for the final computation). In the calculation, the different attributes of each of the layers are used and taken into account. As an example, a trash can that is temporal, will have a lower impact on the food truck index than a permanent one, or eating areas with higher rating will have greater impact on the mentioned index. The final result of this analysis is an attribute called the Food Truck Index. This index, which ranges from -50 to 100, 100 being the highest, indicates the suitability of locating a food truck in a particular street.

It must be indicated that a fully operational ArcGIS tool was created based on this methodology. When opened, the tool ask the user to select the different data layers required and to indicate the different buffer distances that he/she wants to consider. From this input the ArcGIS tool will, in an efficient matter, go through the already described methodology and create a new shapefile with the final results. Upon request by email, we will gladly share this toolbox with anyone! The image below shows a representation of the methodology used. Click here for a full-size image.

3. Data Collection

Most of the data used in this project was collected through the Collector App. We first created a geodatabase with several layers in ArcCatalog and published them online. For those layers which needed to be collected on our own, we assigned domains and coded values to their attributes. Then we walked around the UIUC campus to collect the data with our cell phones. The following is a list of the main layers and the attributes used in this project:

a. Food Trucks on Campus: Name Type of food Price range Acceptance of Credit Cards Opening Hours Rating Comments

b. Street Trash Cans: Size Recycle or not Type of equipment Material Usage Frequency Maintenance condition Comments

c. Public Eating Areas: Capacity Ceiling or not Type of equipment Usage Frequency Surrounding environment Rating Comments

In addition, we also used two existing GIS shapefile. The first one is "University of Illinois Buildings" which was provided by Dr. James Whitacre. We added two fields to indicate the expected number of people in buildings during daytime and the floor area of each building. The second one is "Champaign County Highway" which was downloaded from the website of Illinois Department of Transportation. In order to apply it to our methodology, we split the streets on campus at intersections.

Data source: Illinois Department of Transportation - Champaign County Highway

4. Execution of Methodology

The GIS methodology designed in Step 2 is applied to the GIS data we collected. In this process ArcGIS for Desktop was used. After some optimization of the process, we were able to reduce the time required to run the methodology to less than 3 minutes.

5. Review of Results

The results of our analysis show that the best location to establish a food truck would be on the street section of S. Mathews Ave. between W. Green St. and W. California Ave. It is a reasonable result since it is surrounded by a lot of buildings, public eating areas, and garbage cans with only one existing food truck currently operating in the surrounding area. The results also indicate that the north campus is not a suitable place to start a new food truck business. The main reason is because of three food trucks are already operating in that area.

6. Communication of Results

A webapp was developed to help communicate our results. It was built on the ArcGIS Online framework using the ArcGIS API for JavaScript. We refered to several templates, but we made a lot of modifications.

The streets are color-coded in order to show the suitability of each section. Food trucks, garbage cans, and public eating areas are all built with pop-ups to show detail information and photos. We also incorporate the picture of food truck, our topic, and our names on top of the app which are linked to our personal websites. Some useful widgets were added to the map, such as home button, measure, query, etc. Especially, for query, users can simply apply filters to meet their requirements.

Future Work

• We believe more factors should be incorporated such as the location of fast food restaurants, the street space availability to park the food trucks, etc.
• It would be interesting and more practical if a more comprehensive database could be built to cover the UIUC campus-wide area.
• Our WebAPP can be improved by using ArcGIS for server to allow users to add their datasets, adjust the parameter to their needs, and perform analysis on the cloud.

Web APP developed by Manuel Martin and Hsiao-Hsian Liu as part of the UIUC class GEOG479 final project (2015)