A Rose by Any Other Name....What IS a CDP Anyways?

Occasionally someone will ask me "What is a CDP anyways?"

CDPs, in the world of the Census Bureau, stands for Census Designated Places. Technically the definition goes something like this - Census designated places (CDPs) are closely settled, named, unincorporated communities that generally contain a mixture of residential, commercial, and retail areas similar to those found in incorporated places of similar sizes. Got it ?

Let's look at this a little more closely. First, they are closely settled, meaning that CDPs have multiple residents that are housed (in some sense) relatively close to one another. Second, they are unincorporated, meaning that they are NOT a village. Villages already get special consideration in the census. Often, CDPs are what might be called "hamlets", although that designation does have a special connotation to it as well in New York. Third, they contain a mixture of residential, commercial and retail areas. This isn't a strict requirement but it would bolster the argument for creating or selecting an area as a CDP.

Probably MOST important in the process is the fact that they are NAMED ! So if you were to say "I'm heading to CDPville," people would know pretty much exactly where you were talking about. The names are something that are commonly used to describe the location in question.

The best example of a CDP (at least since the 2010 census was conducted) is Old Forge in northern Herkimer County. Old Forge is NOT a village - it is not incorporated as such. Yet people know exactly where you mean if you say you are heading to Old Forge. It also has a nice mix of residential, commercial and retail areas. And clearly, it is closely settled. So Old Forge was a perfect spot for the creation of a CDP in order to gain census data about that specific area.

Oh course the devil is always in the details - and specifically that means the details of EXACTLY what you include when you designate the place as Old Forge. For example, should it include Thendara ? Or should it stop on its southern border at the Moose River ? And how far north does it stretch, to say nothing about east and west? Regardless, through conversations with locals and the Census Bureau, a boundary was arrived at. As a result, there is now census data available for the CDP of Old Forge !

You can see 5-Year American Community Survey profiles for ALL of the CDPs in Herkimer and Oneida Counties by clicking on the links below. These include demographic, social, economic and housing profiles for Clark Mills, Chadwicks, Durhamville, Old Forge, Verona, Washington Mills and Westmoreland.

2006-2010 ACS Demographic Profiles for Herkimer and Oneida Counties CDPs

2006-2010 ACS Social Profiles for Herkimer and Oneida Counties CDPs

2006-2010 ACS Economic Profiles for Herkimer and Oneida Counties CDPs

2006-2010 ACS Economic Profiles for Herkimer and Oneida Counties CDPs

ACS Community Profile: Ilion

The American Communities Survey (ACS) provides snap shots of municipalities each and every year. For many of our communities, this represents the only source of data for things such as education, poverty, and transportation issues. As part of an effort to bring this data to the public's eye, I like to occasionally post the profiles for some of our smaller towns and villages.

Below are links to the demographic, social, economic and housing profiles for Ilion, NY, which can be found through the ACS. To learn more about this prominent village in Herkimer County, visit the New York Genweb site.



In the meantime, here are links to the ACS profile data for Ilion !

2006-2010 ACS Demographic Profile for Ilion

2006-2010 ACS Social Profile for Ilion

2006-2010 ACS Economic Profile for Ilion

2006-2010 ACS Housing Profile for Ilion

Understanding Margins of Error (MOE) in the ACS

The American Communities Survey (ACS) is the primary data source for communities since the demise of the long form from the decennial census process. For information about what the ACS is, and it’s relationship to the census, you might want to revisit this previous blog post.

Since the ACS is based on a smaller sample than the decennial census long form used to be, it has more variability in it’s statistics. Think of it this way, if you surveyed every single person in a community you would know with 100% accuracy how much money they made last year. If you only asked 1 out of every 7, you would still probably have a pretty good idea, but there would be a bit of measurement error in your number. So there would be some level of variability. But now imagine you only interview 1 out of every 50 people. You’d still have an income level to report but the amount of variance in your data would probably be a bit larger.

That’s the difference between ACS data and the decennial census data. The census “short form” was used to survey every single household. The long form only went to 1 in 7 households. Now along comes the ACS which only goes to 1 in roughly 50 households. The variability of the data is much greater when the sample size is smaller,

This is why there ACS provides “margins of error”, or MOEs. The MOEs provided in the ACS basically tell you that you can have 90% confidence that the ACTUAL number if you survey 100% of the community would be between the number provided and plus or minus the margin of error. Let’s use an example.

According to the 2010 One Year ACS estimate, the median household income in Oneida County is $47,286. The MOE (margin of error) is +/- $2,128. So that means that if you go above and below the median income level reported by $2,128, you can be 90% confident that the actually household income level is within that range. More specifically, I am 90% sure that the median household income in Oneida County is between $49,414 and $45,158 .

Why is this important to think about ? Well, numbers with really REALLY large MOE suggest that the data is less reliable. It might be interesting to know and give you a clue as to conditions for an area, but if the margin of error was HUGE it should also give you pause. Think about it this way – if in the last example the MOE for median income was +/- $40,000, that would mean we would feel confident that the median income was between $87,286 and $7,286 ! That really isn’t a particularly useful range of data for talking about median household income !

I’ll make a separate post on the importance of properly interpreting margins of error. In the meantime, keep the MOEs in mind as you look at ACS data ! And when they begin to exceed 20% of the base number being measured, you should at the very least take a moment to look at the data more closely and use caution in interpreting it.

It's A Wonderful Day in the Neighborhood !

Welcome to the Neighborhood !

Defining neighborhoods, in census terms, is not an easy prospect. Neighborhoods, well often well defined, do change over time as in and out migration, as well as the aging of populations, occurs. However, for the most part they generally remain in place for fairly lengthy periods.

One of the sets of data we get requests for in particular is for “neighborhoods” in the City of Utica. Such requests pose problems on several levels. The first is a matter of definition – what exactly ARE the neighborhoods of Utica ? And secondly, there is of course the issue of does any data exist for these specific areas being looked at?

Since 1980 data has been developed for what are seven distinct sections of the city. While there can be much debate about the boundaries of each area, they are historical “neighborhoods” from the standpoint of the availability of census data if nothing else. These seven can be seen on the map below, and include: East Utica, West Utica, North Utica, South Utica, Southeast Utica, Center City Utica, and Cornhill.

Neighborhood data exists then since about 1980 through the 2000 Census for Utica. Currently there is no such data put together for the 2010 Census, but efforts are underway to get at similar data for these areas of the city.

In the meantime, here is a basic three page demographic profile for the seven neighborhoods comprising Utica from the 2000 Census.

Population Flows from Cities and Villages to Towns

One of the great "truths" of demographics in the past 50 years has been the outflow of people from cities and villages to more rural settings. In a phrase, it's referred to as "urban flight".

Oneida County has seen considerable evidence of this in census data. The table below shows the change in population for cities, villages and remainders of towns (ROTs)from 1960 until the most recent census in 2010. ROTs are basically the town populations minus the village populations.

As you can see, since the 1960 census, the cities in Oneida County have experienced a decline of more than a third (36%) of their population. Villages, while not losing nearly as great a percentage, have lost more than a tenth (13%). ROTs on the other hand have seen their populations increase by about 34% ! This despite an over all loss of population for the county as a whole since 1970.

As a result, town populations (that is to say the populations in the parts of towns NOT in villages) now exceeds city populations in the county, and has done so since the year 2000. While approximately 99,000 people now live in one of the three cities in Oneida County, nearly 110,000 live in the non-village portions of its towns.

Interestingly, the number of people (population = 109,197) living in the ROTs has remained the same since the Census 2000. And by the same I mean EXACTLY the same ! Could this be a sign of a change in this trend ? With no real way to know, we will have to wait for another 8 years until the 2020 census to get a better view of where this trend is heading locally.