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Neighborhood Locator

Neighborhood Locator. Team Members: Qian Hao Nick Miller Doug Shover Tagwireyi Paradzayi. Overview. Overview. Background. Objective. Demonstration of Website. Approximate duration: 20 minutes. Introduction. Project Background. Website process. Conclusion. Discussion / Take Away.

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Neighborhood Locator

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  1. Neighborhood Locator Team Members: QianHao Nick Miller Doug Shover TagwireyiParadzayi

  2. Overview Overview Background Objective Demonstration of Website Approximate duration: 20 minutes Introduction Project Background Website process Conclusion Discussion / Take Away What our project hopes to accomplish Results Methodology Demonstration Results Discussion Conclusion

  3. Background • Franklin County is located in the Central region of the state of Ohio and has an area of 543 square miles with a population in 2000 of 1,068,978 people. (http://en.wikipedia.org/wiki/Franklin_County, Ohio, 2010). Franklin County is also home of the largest university in the United States and with this large student body there is a huge amount of transition of people moving into and out of the area. Many times when people move into an area they evaluate their housing location based on proximity to a specific feature, school, parks, or work for example but without high level tools that take existing data and apply their preferences, so they are left making decision based on word of mouth, or based on one or two features.

  4. Objective • Our project hopes to offer a prototype of a solution to enables a potential resident an opportunity to evaluate the Franklin County area with the help of an interactive website that evaluates their preferences and identifies those positions that they might find attractive to live.

  5. Methods • Our output is in html, however there are several components that are utilized prior to this point; Specifically, ESRI ArcMap, HTML coding tools, javascript coding tools, and PHP coding tools. Each component has a specific purpose and role in enabling ourdemonstration to work properly. • ESRI ArcMap produces the shape files necessary to demonstrate the points on the map which has data stored in a map file indicating the map shape file location, name, projection and coordinates of the original data. The HTML, javascript coding and PHP coding tools used in this project enabled us to configure the specific component based on its capacity, javascript for action, PHP for PHP calls against a PHP server, HTML for display on a webpage. • We begin by identifying the shape files to use in our display; due to a shortage of data we only have shopping centers, golf courses, and parks. We then created a master file of Franklin County with a lattice of lines identifying a space of 200 meters by 200 meters covering Franklin County which became our base map to evaluate the distance in meters from those individual spaces to the attributes of the selected features.

  6. Methods Demonstration of the lattice box master file. • As demonstrated, each point is defined as a polygon with a specific ID and the distance detailed to the closest attribute we are evaluating against. • This table also details the attribute name of the features we are identifying a distance to which we used as our base layer to evaluate the distance in meters to. • A webpage was created to allow the user to define the distance they would like to live from those specific features as well as enable the option to evaluate points that they would like to live away from built on a Google Maps base map.

  7. Methods Initial selection for the user.

  8. Methods • The background behind the process begins with the user specifying their desirable (or undesirable) points as well as defining the distance from or away those points. Once the submit button is pressed the information is sent to a javascript that evaluates those positions that the user defined in their query. The process then evaluates the original map file for the original lattice of Franklin County that satisfies the defined parameters and then sends this information to a PHP file that creates a new map file on the server. From there the configurations are defined to display those points in grey in a new map layer which the user can then select in the openlayers demonstration.

  9. Methods Demonstration of a successful attribute query.

  10. Methods • Further adding to the functionality of the project, the user can click on a feature in the display and the details about that point will be displayed to them, These details include, the name of the neatest feature type, (golf course, shopping center, or park), as well as the distance to that feature and are displayed in the right hand corner under PLACE INFO.

  11. Demonstration http://gis.osu.edu/g787/gis07/neighborhood_locator/project/county.html

  12. Results • Our project was successful in meeting the objectives we set. If home seekers visit our page, they can use our tool successfully to identify their desirable and not desirable neighborhoods based on distance from shopping centers, distance from parks and distance golf courses. We provided the Open Street map as the base layer so that users can identify the relative location of their areas of interest at street name level. Using this tool we can easily evaluate potential places around the area to live, of course as we add more points we increase the diversity of the project and enable a more specific analysis. The feature query adds the added functionality that a user can evaluate further those specific details regarding their potential location. The intent of the feature is to allow an open evaluation of the final product, enabling the user to evaluate features within and outside of their selection results.

  13. Discussion • While the results of our prototype met the expectations of displaying the functionality of our process there are some shortcomings due to the lack of point data. One of the issues we had in development was the data we were able to retrieve for Franklin County were in a different projection than could be demonstrated in openLayers, this limited our ability to easily demonstrate data based on the time constraints for our project. In the future and given more time, we can increase the functionality of our demonstration to include additional features for the user. Additionally, adding housing pricing would be advantageous so that a user could evaluate locations based on what fits their budget. This would be a feature to be added as the project grows.

  14. Conclusion • Our project shows a lot of promise, not only in functionality, but also in demonstration of resources. While this version is only in prototype form future dynamics can easily make this a very powerful and useful tool as long as the data contained in the archive is accurate. This product ultimately develops a platform for a user to make an educated decision when moving to Franklin County with details about their potential living area as well as details in regards to features they might find appealing.

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