AZREGIONTREND: A MICROECONOMIC FORECASTING SYSTEM

Transcription

1 AZREGIONTREND: A MICROECONOMIC FORECASTING SYSTEM MIS 510: WEB COMPUTING AND MINING BRIAN LEUNG, GLENN MUSA, MIN SOCK PARK, JIHYEON SEO MAY 9, 2012

2 Contents Introduction... 2 Competitor Analysis... 2 Business Case... 3 Sample Scenarios... 4 Novelty... 6 System Specifications... 7 API s Implemented... 8 Team Members and Contributions... 9

3 Introduction With the rise of Web 2.0 and the social facilitation of the web, people sharing information, goods and services with others online has been rapidly accepted as a social norm. The rise of e-commerce sites such as ebay, Amazon and Craigslist serve as great examples of this phenomenon and reinforce that in the early 21 st century more and more users continue to adopt and conduct business online. AZRegionTrend captures the effect of the online marketplace Craigslist on a microeconomic level. By collecting data from the eight subcategories of Craigslist in Arizona, AZRegionTrend gives analysis of those regions, as well as provides recommendation as to when and where to buy specific items within Arizona. Competitor Analysis AZRegionTrend is in a unique market space with few competitors. While large publications and media outlets publish macroeconomic data and analysis, very few online resources look at the economies of communities in a microeconomic sense. City-Data.com summarizes its objective as: collect[ing] and analyz[ing] data from numerous sources to create as complete and interesting profiles of all U.S. cities as we could Here s how City-Data and AZRegionTrend stack-up: City-Data AZRegionTrend Intuitive User Interface Region By Region Analysis Analytical Portion Social Media Plugins Zip-Code Drilldown Data Future View Records beyond 2006

4 CNNMoney.com is the self-proclaimed world's largest business website. CNNMoney is the online home of FORTUNE and MONEY magazines, and serves as CNN's exclusive business site. CNNMoney is a competitor to AZRegionTrend because of their yearly reviews of CNN Money s Best Places To Live. CNNMoney AZRegionTrend Intuitive User Interface Region By Region Analysis Analytical Portion Social Media Plugins Zip-Code Drilldown Data Future View Records beyond 2006 As one can see in the side-by-side comparisons of AZRegionTrend s competitors, the clear market advantage for AZRegionTrend lies in its Region-by-Region analysis comparing the KPI s of city and community subsets of Arizona and the future, predictive, view of microeconomic data. Business Case AZRegionTrend operates on a freemium and ad-supported model. AZRegionTrend provides information for the previous quarter (previous four months) for free to any user visiting the site. However, to access historical data or to look into the future and use our predictive models, the user would purchase a premium account for a nominal fee. The pricing strategy would be tiered by amount of data: Basic $10.00/yr Statistical Surveyor $39.99/yr Yearly History Look Into the Future Tool Historical Records for All-Time X The ad-supported revenue from AZRegionTrend will be generated by Google AdWords displayed in related areas. For example, if a user is looking at the Average Cars by Region page, AdWords will display advertisements for cars in the specified region. Despite nominal click through, the ad-supported revenue should somewhat offset the web hosting costs.

5 Sample Scenarios Use Case #1: Relocating -- Where to move to and what kind of job can I get? A user lands on the homepage, and after investigating the hotspots, decides they d like to learn more about Phoenix: Before I can even move to Phoenix and begin looking at houses, I need to know what kind of job I can get there! the user says. Naturally, they advance into the Jobs subsection of the Phoenix Region:

6 From the Jobs subsection, the user is then able to identify that Sales and Healthcare are two of the most in-demand industries in Phoenix. Now, they ll simply fill in the indeed job search form on the page with Sales or Healthcare and they ll be on their way to their interviews! Now that the user has a job, let s assume he wants to find a place to live. Next, the user will visit the Housing subsection of Phoenix: With this visualization, the user can view the temporal trends for the number of housing postings in Phoenix and can have a better understanding of when to purchase a home or rent an apartment in Phoenix. Judging by this scenario, the user is likely to attain a sales position and move into Phoenix in January! Use Case #2: When and where to buy a car in Arizona? A potential used car buyer wants to find out when and where in Arizona is the best time to purchase cars. He comes to RegionTrend website, clicked on RegionByRegion, click on average car value graph and noticed that Show Low has the lowest trend of car values compared with other cities in Arizona, which prompted him to potentially shop for cars in Show Low. Then he clicked on the drop down menu that directs him to the Show Low city page, where he could look at when to buy cars, price prediction, and purchasing power in Show Low. The user noticed this month s average car sale values are lower than the overall average car sale value, which triggered him that this month is a good month to purchase a car in Show Low.

7 Novelty RegionTrend includes analysis in the following areas: Regional sales forecast, housing forecast, housing posts temperature, sales temperature, job postings temperature, top 10 jobs, and city comparisons for jobs, housings, and sales. Analysis is performed by counting specific postings of the category of interest and calculating the regression line for the predictive analysis. Regression analysis is performed by using WEKA and Apache Common Math java libraries in the codes for our backend engine. These predictive analyses are live and calculated each time user refresh the page. To elaborate RegionTrend s regression analysis in greater details, daily postings are counted and averaged for the categories of interest and inserted as data points for the regression lines. An array of these data points are stored and fed into WEKA and Apache Common Math java methods. These methods output the slopes for the regression lines such that we are able to plot a linear line with the obtained slopes in the graph. Users could predict the housings, jobs, or sales volume for each city by inputting the desired date in the future and the system generates the predicted values. This is another novelty of RegionTrend, where currently no other web analytic sites are performing predictive analysis. RegionTrend s goal is to bring forecast of Arizona cities as values for users who are interested in relocating to cities in Arizona or buying or selling items in Arizona.

8 System Specifications AZRegionTrend is hosted on Amazon EC2 Cloud architecture. AZRegionTrend scraped data bi-weekly, with total records mined just shy of one million posts: Phoenix 552,754 Tucson 254,790 Yuma 23,511 Mohave 30,499 Sierra Vista 14,405 Flagstaff 36,452 Prescott 33,969 Show Low 6,880 Total Records Crawled, Parsed, Processed: 953,620

9 API s Implemented Below is just a subset of our implemented API s, AZRegionTrend utilized over 15+ API s: API Facebook Connect Facebook Comment Facebook Like Twitter Google Maps Google Visualization Google WebFonts Google Translate PayPal Indeed Job Search Flickr Apache CommonMath WeKA AZRegionTrend Use User login and tracking User to User Interaction User sharing User sharing Visually represent regions Visually represent mined data Reduce server load and improve graphical UI Enable AZRegionTrend internationally User Payment for Premium Returns Job Descriptions based on Regional Job Data Return images of cities Analytics and regression Analytics and prediction

10 Team Members and Contributions AZRegionTrend was a collaborative effort with each team member contributing unique skills. Brian Leung Data Mining, data analysis and heuristic definition, concept development Glenn Musa API development, front-end design, concept development Min Sock Park Database, server, and hosting administration, API development Jihyeon Seo Parser development, front-end JavaScript integration, data mining