National Community Planning Month

October is National Community Planning Month, an opportunity for planners to share the work they do with the communities they work for. Events take place nationally as well as locally: the American Planning Association publishes its annual list of Great Places in America, examples of excellent planning across the country (which included West Urbana in 2007, the first year of the program), and planners from around the country lead events and post, tweet, speak, and write about the planning field. For our Planning Month data blog, we would like to highlight the importance of data in community planning.

Planning requires data, and – despite the much-discussed rise of big data in the last decade or so – that’s not recent. The first Decennial Census was conducted in the United States in 1790, and has continued uninterrupted since then. Summaries of some of this data are still available: the Census Bureau website has a page dedicated to summary documents that date as far back as 1878. Additionally, there are published Census reports that detail the questions that were asked in each Decennial Census since 1790. There is value in these as well: we can learn about a decade not only from the data that was collected then, but also from what questions were (and weren’t) asked as part of the data collection, and how those questions were written.

Today, data is invaluable to planning. In developing plans, programs, and policies, quantitative data is useful at all stages. It helps to demonstrate the need for a program or policy in the first place, and to show whether the program or policy is successful after it’s been implemented. We’ll take a look at two examples, affordable housing and unemployment, to explore important uses of quantitative data.

Affordable housing is a topic of interest for many communities around the country. Housing data lets planners research what local median income and income distribution are in their community, get an idea of the prevailing rents and housing unit values, and even see estimates of what percentage of their income households in their area are spending on housing costs. This information is key in establishing local need for affordable housing programs or policies, and in narrowing down what the focus of such a program or policy should be.

The unemployment rate is one key measure of an area’s economy. If a city developed a workforce development program designed to match job-seekers with jobs, and to help develop job-seekers’ skills to prepare them for those jobs, the local unemployment rate would be an apt indicator of that program’s success over a period of several years. And if the unemployment rate and other selected indicators were not measured and published, the remaining data that could be collected by program staff (e.g., number of total participants, participant job placement rate) would stand alone in proving that the program was a success, and should continue to be funded and operated.

Qualitative data is also critical. Locally, that takes the form of survey responses, comments on draft plans during public comment periods, and feedback received at public meetings. It serves many of the same purposes – identifying local need, establishing local public approval or disapproval of a given plan or concept, and demonstrating the success of a program, plan, policy, or idea. The main difference is the source. Unlike the majority of the quantitative data discussed above, which comes from third-party government or research sources, qualitative data generally comes from you. Survey respondents, plan commenters, and meeting attendees tend to be local residents discussing local issues. This data has an immediacy and a granularity built in that national, state, or even county-level data can lack.

Both qualitative and quantitative data have merit, and both are necessary for local planning. So during Planning Month and throughout the year, we emphasize the value and the necessity of the data and data sources that we use. Because informed planning and decision-making are better planning decision-making.

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