John Doyle (and Tielman Van Vleck?)
The purpose of the project is to create a model based on historical data that can predict how fast counties or states can recover from economic recession in comparison with one another. The software tool will accept large data sets sorted by either state or county (yet to be determined; will depend on available data) and generate an animated view of the data set over a 2D map of the US. The goal of the tool is to make it is wide ranging as possible, so that it can accept more than one type of data set and still be able to effectively map the data. The scientific question to be answered will focus on the reasons behind the ability of certain geographical regions to recover more quickly than others from recession. The hope is that the model may provide some insight into what types of efforts work on a local level to speed recovery from recession.
A couple of different sources for data have been located. The historical data for counties and states is available from the University of Oregon Government Data Sharing Project. This data will be used to generate the initial animated model. The predictions will be based on data concerning the composition of local economies, found at Stat-USA. Hopefully this data will provide some insight into why some counties or states can recover faster than others. Only state data for industry concentration is available; unless I can find it broken down further by county at another website, the project may predict only on a statewide basis. I will also consult with Randy Nelson to explore other options for locating relevant data.
The tool will be created using Java (unless you think there may be something more useful!). Somehow we will have to create the boundaries for the color coding of the data. Whether state or county, I am hoping I can find some shortcut so we don't have to trace boundaries pixel by pixel for however many counties there are. Fifty states would probably be more reasonable. Aside from that, the tool will have to be able to understand the data set it is given, so we will have to consider the format of the data set. The tool will allow for a static frame to be generated for each period of time based on the available data. Users of the model will be able to input the magnitude and duration of the recession and also specify if certain industry or areas are particularly hard hit. Finally, the animation will take a number of static frames and string them together, and the user will be able to select how long the animation should run. The details of the tool will probably become clearer as the project progresses (in other words, I have no clue right now!!)
The concept of business cycles in economics is not new, nor is it particularly difficult to understand. However, the preictability of these cycles is very low. While this project will not answer the question of why, it may be able to give some insight into what factors aid in local recovery from recessions. By examining data on industry concentrations, income, and other possibilities that come along, I am hoping to come up with some answers as to why certain areas take longer than others to recover from recessions. If the size of the project does not fit the time frame, it can always be scaled back to include only a particular region. The question will also become clearer as we start to find some answers.