30 April 2013

7 comments:

Zach Haertl said...

This presentation seems pretty straight forward, the only thing that is hard to understand is the U.S. Maps. I'm not really sure whats happening with the different color tones and what they mean. Also i dont know if this would be too much information to try to wrangle with but, what if you included statistics on income in relationship to building permits? Based on that recessions have a lot to do with lost insome, maybe that has will help back up some of your findings. I hope this was helpfull keep up the nice work!

Zach Haertl

Zach Haertl said...

This presentation seems pretty straight forward, the only thing that is hard to understand is the U.S. Maps. I'm not really sure whats happening with the different color tones and what they mean. Also i dont know if this would be too much information to try to wrangle with but, what if you included statistics on income in relationship to building permits? Based on that recessions have a lot to do with lost insome, maybe that has will help back up some of your findings. I hope this was helpfull keep up the nice work!

Zach Haertl

Zach Haertl said...

This presentation seems pretty straight forward, the only thing that is hard to understand is the U.S. Maps. I'm not really sure whats happening with the different color tones and what they mean. Also i dont know if this would be too much information to try to wrangle with but, what if you included statistics on income in relationship to building permits? Based on that recessions have a lot to do with lost insome, maybe that has will help back up some of your findings. I hope this was helpfull keep up the nice work!

Zach Haertl

Nstraube said...

I think it is an interesting and valuable subject. I would examine the outliers, those most greatly/quickly effected by the decline and likewise by the recovery and find correlations or feel free to speculate on reasons for their non-normative behavior? Why the Dakotas? My guess is fracking. Why DC? That seems a little strange. Housing starts are often seen as the canary in the economic coal mine, how should this information inform future action/policy decisions?

Ainsley McMaster said...

I think this analysis would be a fantastic springboard for further analysis of other variables not yet considered:

You discuss that the south and midwest show little sign of recovery. Given that the midwest (northern Wisconsin, etc.), is a large wood manufacturer of housing materials, can you see a trade-off between other regions recovering faster and the need for housing material production within the slow-recovering states? What other correlations can be investigated to determine the lax in recovery? How are jobs numbers relative to each state? Looking at population trackers, are the states which are recovering faster also increasing in population at a faster rate than others? Are there any incentives being put into play by some states vs. others to increase housing development?

AmandaKay said...

I was going to suggest looking into the unemployment rate in each state, as that is a very relative factor.

John Annis said...

Some of the graphics were unclear. You may want to think about constructing the graphics with you not being there to tell what things mean ie. the color of the u.s. maps.

Also the diagram with all the states labeled with their spark lines doesn't stand out enough even though the information is all there. The spark lines need to be the hierarchical part of the composition. Don't let the text overwhelm the composition.

I am being a bit critical, but only because your data is good, the graphics just need work.