For a recent custom data request, I pulled building construction permit data for US counties and places (aka cities). Many people don’t realize that the US Census Bureau collects data on building permits by county and place, and makes it publicly available.

Below are step by step instructions for how to pull this building construction permit data from the US Census Bureau’s website. We’ll use Orange County, Florida as an example — but the same  steps apply if you want to pull data for a city.

Step-by-Step Guide to Finding Building Permits by County

The building permits page of the US Census allows you to search for building permits either monthly or yearly, going back to 1996. Not all areas report to the Census Bureau monthly – some only report yearly.

If when you are searching for building permit data you cannot find the county or place you are searching for, go back and change your search to “Yearly.” And there may be cases where building permit data are just NOT available for smaller counties (in terms of population) and smaller cities.

Now, here are the steps to follow:

Step 1: On the building permits page, choose the month and year you want building permit data for.

Home Page where it all begins

Step 2: Pick county or place. Place reports are usually from individual municipalities, but some townships or unincorporated towns also report to the Census Bureau.

Step 3: Pick the state you are interested in finding data from. For our example here, we’ll be looking at Counties in Florida.

Steps 2 and 3

Step 4: Click “Submit”

Step 5: On the next page, pick the county you want data from. Let’s pick Orange County. If the county we were looking for wasn’t listed, that would mean some of the municipalities in the county only report yearly, so we’d have to go back to the prior page and change our time to annual.

We’ll select Orange County as an example

Once you click “Submit” on the second page, you will see the data building permits in Orange County for the month you selected. The information is broken down into permits for Single Family, Two Family, Three and Four Family, Five and More Family, and the total for all building types. In March of 2013, we can see that 353 Single Family building permits were reported, that there were 353 units in those buildings and that the construction cost was $76,359,451. We can also see the US Census Bureau’s estimate of building permits including any that may not have been reported (353 for March, so the same number reported.) The chart also includes totals for the year so far.

You will see the data building permits in Orange County for the month you selected

In the very left column of the chart there is a “Browse” button. Clicking on this allows us to compare building permits by county throughout Florida. We can choose to compare building permits by county for a specific type of building, or the total building permits in each county. If we “Browse” Five and More Family buildings, we can see that while Orange county only reported 3 building permits for this building category in March, Miami-Dade reported 13 building permits for the county. Clicking on the “Profile” button for Orange County brings us back to the building permits by county page for Orange that we came from.

This allows us to compare building permits by county throughout Florida

At the top of this page, you will see drop down boxes for month and year. If you want to compare building permits for Orange County for March in different years, you can pick 2012 from the drop down menu and see that Orange County reports only 267 building permits issued in March of 2012 – meaning nearly 100 more building permits were reported in 2013.

If you want to compare building permits for Orange County for March in different years, you can pick 2012 from the drop down menu

I hope the above steps and screenshots save you time when pulling building construction permit data for counties. If you need to pull lots of building permit data, like all monthly permit data since 1996, or if you need to pull building permit data AND other types of Census data (like year structure built, median value, median rent, etc.), you should check out Cubit’s custom data pull option.

Image from Images_of_Money

Recently for a custom data request project, I needed to find historic home sales data by zip code for the entire United States. While the US Census Bureau has median home value data for ZCTAs, you can’t get count of sales or sale prices for individual home sales data from the Census datasets.

In addition to the Census Bureau, the county appraisal districts are a fantastic source of real estate data. But in Texas, you can’t even get home sales data from the county appraisal districts, because Texas law makes reporting home sales optional. And even for states unlike Texas that do require the reporting of home sales, it would be painful, time consuming, and expensive to contact every county appraisal district/office in the United States & request this data.

Below are all of the options that I explored to get historic home sales data by zip code for the entire US.

2 Viable Options to get Historic Home Sales Data by Zip Code

1. BEST OPTION – Purchase the data from DataQuick. DataQuick has been selling real estate data for over 30 years. They have reasonable prices. For example, I could get 2 data points (like number of sales and median sale price) for each MONTH for zip codes in the US for the past 10 years for $10,000. If I needed historic home sales data by zip code for the previous 10 years, that price was $7,000. And if I wanted additional data points in addition to the 2 mentioned above, each data point was $1,000 for 10 years. This pricing structure was easy for me to understand and to communicate. Also, DataQuick returned my phone calls quickly and were pleasant to work with. Their turnaround time for the data was 4 days. But there’s one small catch – they only have 70% coverage of US zip codes – which I understand to be based on population. Basically, you can get home sales data for the 70% of US zip codes with the largest populations. For the custom data request research that I was doing, 70% of zip codes was good enough. One could assume that the smaller the population, the fewer home sales are likely. But if you HAVE to have a higher coverage level than 70% of US zip codes, I did find a second option for you.
2. Buy the data for Real Quest. Real Quest is a division of CoreLogic – which is also a company that sells real estate data. The benefit of Real Quest is that they have data for 98 to 99% of residential properties in the US. That’s pretty impressive. What wasn’t impressive was their customer service or their pricing. I never could get a price quote from the sales person who was “helping” me. The best I could get is that “we’ll work with you if you have a $50K budget.” The other specific issue that bugged me was that they kept asking me to identify my client who I was doing research for – which I wasn’t comfortable doing without permission from my client. Since I had such a poor experience with their sales person, I hesitate to even list these guys as a viable option. But I can’t overlook the 98-99% data coverage.

Not Viable Options For My Purposes But These Options Might Work For You 

1. Policy Map. Policy Map looks to be a pretty sweet web app for pulling demographic data. I keep meaning to sign up for a free trial & check ‘em out, but it never gets to the top of my to-do list. Policy Map wasn’t a good fit for this particular request, because their sales data only went back to 2006. It appears they have number of sales, median sale price, aggregate sales amounts & loan-to-value ratios on a quarterly basis for 2007 to 2012 and on an annual basis for 2006 to 2007. 1 note: you can’t access this data as part of the Free Account.
2. Regional Multiple Listing Services (MLS). A real estate multiple listing service is basically a shared database of that allows real estate brokers & realtors to see what homes are for sale & have currently sold in the past. There are 900+ regional multiple listing services in the United State. During my research, someone told me that there are 935 MLSs over the phone & I haven’t been able to verify it. I did find this link with data sources indicating that the number is between 900 and 1000. And then I found to buy access to 1 MLS would be $350. A rough estimate of 1,000 MLS x $350 for access = $350K. And if that price point is no problem, I think [emphasis on think – I stopped digging into this option at this point] you have to be a realtor/have a real estate license to get access to a MLS.
3. Real Estate data APIs like Trulia & Zillow. Using an API like Trulia or Zillow to get sales data was my idea for where to go to get historic home sales data by zip code. But when Anthony actually read the terms of service for the APIs, it was against the terms & service of the APIs for us to use them in such a way that we could pull all current & historic sales data for the US. Both Trulia & Zillow implement throttling limits, which I presume, prevents someone from downloading their entire database.
4. National Association of Realtors. While they have home sales data, they don’t have it at the zip code level.
5. Realtor.com. These guys never emailed or called me back.

I hope this information saves you some serious time, because it took me awhile to piece it all together. If you know of another way to get historic home sales data for US zip codes, please contact me. I’m interested!

Several problems were coming up repeatedly in customer emails and user tests (where we observe a person using the app). We knew that some tables were too wide for the page. We learned that people wanted larger maps. We discovered later that some folks had a hard time navigating both the scroll of the page and the scroll-to-zoom feature on the maps.

Problem #1: Big Tables

Some tables in the reports were too wide for the page. For some customers, the Race & Origin, Income Distribution and Industries tables were too wide, no matter how wide the browser window. For the rest of the tables, as we added and updated data, things started to get cramped and harder to read.

The Solution:

Reports have more room to breathe.

Allow the report to take up more horizontal space in the app. To do this, we had to move the vertical navigation out of the way. The links to the reports are now, along the top of the page, by the name of the project. You’ll find the reports grouped by category: Demographics, Income, Povery & Jobs, Housing & Transportation and Other.

Problem #2: Big Maps + Scrolling Confusion

People want larger maps for editing and viewing. This one was kind of a freebie. By solving the table problem (above), we increased the size of the maps. But we compounded another problem: scrolling over the map. It happens to me, too. I’m scrolling down the page and start scrolling over a map and the map zooms! It’s doing exactly what it’s supposed to. And I can move my mouse off to the side to continue scrolling. BUT. We noticed while watching people that it was a huge distraction. If a person was exploring or creating/updating a drawing, they’d get off course.

The Solution:

Turn off mouse-wheel-scrolling on the map. This solves the accidental zooming problem and allows for more natural scrolling of the page. Zooming is still available via the zoom widget on the map (the more popular way to zoom anyway).

Bonus:

We also introduced some cleanup to the projects dashboard: further reducing the visual clutter, making the text larger, and giving the projects more space to breath so it’s easier to find your projects.

We hope you like the changes we’ve made. As always, if you have any feedback let us know.

The Least You Need to Know

The most current zip code income dataset that’s available right now for the entire US is the US Census Bureau’s 2011 ACS 5 year estimates. An updated ACS dataset will be released every year (i.e. by the end of 2013, the 2012 income data will be released.)

Data Source 1: US Census Bureau

First, the US Census Bureau provides income data for zip code tabulations areas (ZCTAs) — which are very similar to zip codes. I’ve got another blog post that explains the difference between ZCTAs and zip codes.

You can pull income data by ZCTA for all US ZCTAs for the years 2000 and 2011 on the Census Bureau’s website. For the 2000 income data, you’ll want to use the Decennial Census 2000 SF3 dataset. For the 2011 income data, you’ll want to use the ACS 5 year estimates dataset. Learn more about ACS data vs Decennial Census data. Each year, the Census Bureau will release updated ACS income data (i.e. in 2013, they will release the 2012 income data).

While there are zip code level data for the 1990 & 1980 decennial censuses AND there are income data for the 1990 & 1980 decennial censuses, I haven’t done the necessary digging to figure out if there are 1990 & 1980 zip code income data available. You can do this research by reading through the documentation on the Census’ FTP site (links are in the table above).

Your next question is probably “what income related data points can I get from the US Census Bureau”?

Popular Zip Code Income Data for 2011

I’m defining popular as my personal favorite tables as well as the tables that I pull most often for custom data requests. Most of the data below are in 2011 inflation-adjusted dollars.

General

• Mean Income in the Past 12 months
• Median Income in the Past 12 months
• Median Family Income in the Past 12 months
• Per Capita Income in the Past 12 months

Household

• Median Household Income in the Past 12 Months
• Median Household Income in the Past 12 Months for White Alone Householder; Black or African American Alone Householder; American Indian and Alaska Native Alone Householder; Asian Alone Householder; Native Hawaiian and Other Pacific Islander Alone Householder; Some Other Race Alone Householder; Two or More Races Householder; Hispanic or Latino Householder

Type of Income

• Wage or Salary Income in the Past 12 Months for Households
• Self-Employment Income in the Past 12 Months for Households
• Interest, Dividends, or Net Rental Income in the Past 12 Months for Households
• Social Security Income in the Past 12 Months for Households
• Supplemental Security Income (SSI) in the Past 12 Months for Households
• Public Assistance Income or Food Stamps/Snap in the Past 12 Months for Households
• Retirement Income in the Past 12 Months for Households

Poverty

• Poverty Status in the Past 12 Months
• Poverty Status in the Past 12 Months of Families by Family Type by Social Security Income by Supplemental Security Income (SSI) and Cash Public Assistance Income

Other

• Gini Index of Income Inequality
• Median Income in the Past 12 Months by Sex by Work Experience in the Past 12 Months for the Population 15 Years and over With Income
• Receipt of Supplemental Security Income (SSI), Cash Public Assistance Income, or Food Stamps/Snap in the Past 12 Months by Household Type for Children Under 18 Years in Households

Popular Zip Code Income Data for 2000

I was going to put together a list of tables for 2000 income data like the above list of tables for 2011 income data, but when I was working on the list for 2000 income data tables, there was a lot of overlap. Basically, you can get 2000 income data tables that are similar to the 2011 income data tables with a couple of exceptions. For example, there’s no Gini Index of Income Inequality table for 2000. The 2000 income data are in 1999 dollars.

Data Source 2 – SOI Tax Stats on the IRS website

“SOI” stands for Statistics of Income. SOI is a program that publishes income data and is run by the Statistical Information Services, a US government agency.

The SOI is currently reviewing its methodology to safeguard individual taxpayer confidentiality. So the 2008 data set is a preliminary dataset that contains data for zip codes in which 250 or more returns were filed — which means that you won’t be able to get income data about rural zip codes from the preliminary data set. The data are based on individual income tax returns from the IRS’ Individual Master File system, which includes a record for every Form 1040, 1040, and 1040EZ. The records included in the 2008 dataset were returns that were filed between January 1, 2009 and December 31, 2009.

The 2008 dataset include the following data:

Zip Code Income Data Maps

Now that you know where to get income data for zip codes, you can use data to do some pretty cool analysis — like building zip code maps.

Here are some quick zip code income maps for the US and a couple of the largest states using the US Census Bureau’s 2011 data and my favorite free GIS – QGIS.

Click to see a larger map.

Click to see a larger map.

Click to see a larger map.

Click to see a larger map.

Comparing Historic Income Data

If you are interested in looking at changes in income over time, you’ll probably want to adjust for inflation. Check out this other blog post about a cool & free tool that helps you calculate buying power.

Don’t Have Time to do Any of the Above Work but Still Need Zip Code Income Data?

I pull zip code income data for businesses for a fee. If you’d like to get a quote for me to pull this data for you, please complete the Custom Data Request form.

Image from JoeInSouthernCA

So you need apartment statistics.

I often get requests for apartment data from folks who are:
1. researching where to locate a business that provides services to apartment dwellers like a washateria or car wash; or
2. researching where to buy or build a new apartment facility (and the bank has asked them to include the number of apartment units in an area in their business plan).

What you might not know is that the US Census Bureau reports apartment statistics — which means you don’t HAVE to pay a ton of money to a real estate data company to get an estimate of the number of apartment units in a particular area.

How to get Apartment Statistics from the US Census

If you’d rather spend your time doing more interesting things than learning how to use the American Fact Finder 2 — you can always pay me to pull this data for you using the custom data request form. But just in case you are curious, a glutton for punishment or a college student doing research, here’s how:

1. Head over to the American Fact Finder 2 – Advanced Search.

2. In the Geographies tab on the left, select your geographic area of interest (i.e. New York city, NY or Cook County, Illinois).

3. Then type in these magic words into the search box: “Units in Structure”

See the Geography tab on the left & where to enter the magic words!

4. Then look for the Units in Structure table in the list.

There are 3 ACS Units in Structure tables.

5. Depending on the size of your geographic area & the margin of error that you are comfortable with, you’ll need to choose between the American Community Survey (ACS) 1 year, 3 year or 5 year data. As of today, the ACS 1 year 2011 data are the most current data available. Take a look at the margins of error in the different ACS products to see what table will be the best fit for your purposes.

Look at the margin of error data to help you figure out if you want to use ACS 1 year, 3 year or 5 year data.

Fun fact! Now that you know how to pull data via the American Fact Finder 2, there are other statistics that you can get about apartments — not just Units in Structure data.

Understanding Your Apartment Statistics

Now you have your apartment data from the Census — but what do the row headings “1, attached”, “1, detached”, “2″ actually mean? Here are the Census’ definitions per the American Community Survey definitions.

• 1-Unit, Detached – This is a 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. Mobile homes to which one or more permanent rooms have been added or built also are included.
• 1-Unit, Attached – This is a 1-unit structure that has one or more walls extending from ground to roof separating it from adjoining structures. In row houses (sometimes called 7 townhouses), double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.
• 2 or More Apartments – These are units in structures containing 2 or more housing units, further categorized as units in structures with 2, 3 or 4, 5 to 9, 10 to 19, 20 to 49, and 50 or more apartments.
• Boat, RV, Van, Etc. – This category is for any living quarters occupied as a housing unit that does not fit the previous categories. Examples that fit this category are houseboats, railroad cars, campers, and vans. Recreational vehicles, boats, vans, tents, railroad cars, and the like are included only if they are occupied as someone’s current place of residence.
• Mobile Home – Both occupied and vacant mobile homes to which no permanent rooms have been added are counted in this category. Mobile homes used only for business purposes or for extra sleeping space and mobile homes for sale on a dealer’s lot, at the factory, or in storage are not counted in the housing inventory.

US Cities with the Most Apartment Units

Just for fun, here are the 40 US cities with the largest number of apartment units according to the American Community Survey 2011 data. No real surprises here. The cities with the largest populations have the most apartment units as do tourist and college towns.

U.S. Census Bureau. 2011 American Community Survey: B25024 UNITS IN STRUCTURE. Retrieved February 26, 2013 from http://factfinder2.census.gov 

Image from cooper.gary

The US Department of Health & Human Services has released the 2013 Federal Poverty Guidelines.

We’ve updated both the Cubit web app as well as the Cubit Quick Reports to reflect the 2013 Federal Poverty guidelines.

Here are the 2013 Federal Poverty Guidelines.

2013 POVERTY GUIDELINES FOR THE 48 CONTIGUOUS STATES
AND THE DISTRICT OF COLUMBIA

2013 POVERTY GUIDELINES FOR ALASKA

2013 POVERTY GUIDELINES FOR HAWAII

For more information, check out buy cialis online the Federal Register where these guidelines were published.

Image from P.J.P on Flickr

I want to live to be 100. I don’t eat processed foods. I take cod liver oil daily. I exercise.

But one factor that’s working against me reaching triple digits is that I reside in Austin, Texas rather than a blue zone. A blue zone is a “demographic and/or geographic area of the world where people live measurably longer lives.” Okinawa, Japan; Sardinia, Italy; and supposedly Loma Linda, California are blue zones. Let’s take a look at US Census data to identify blue zones.

What’s a centenarian?

A person who is 100 years of age or older.

How many centenarians are there in the US?

According to US Census 2010 data, there are 53,364 US centenarians. 83% of US centenarians are female–which makes me quite happy about my 2 X chromosomes.

Table 1. Number of US Centenarians

How Many Centenarians live in Your State?

See Table 2 below. Of course, California, New York, Florida, Texas & Pennsylvania have the largest total numbers of centenarians. But these states also have the largest populations. We’d expect higher total numbers of centenarians in these states. But what we want know is are there states with MORE centenarians than we’d expect to be find in that state — like a blue zone state. Basically, I’m going to guess (aka hypothesize) that centenarians are not evenly distributed throughout the US. And if I’m right, then we want to identify which states have more than their expected number of folks living over 100 years of age. A quick & dirty way to calculate the expected number of centenarians is to calculate the percent of population living in a state & multiply that percent by the total number of US centenarians. Let’s look at California as an example.

• (Pop of CA / Pop of US) * Centenarians in US = Expected Centenarians in CA
• (37,253,956 / 312,471,327) * 54,325 = 6,477

Then we compare the expected number with the actual number of centenarians in CA.

• 6,477 Expected CA Centenarians vs 5,921 Actual CA Centenarians

The next step we could do is to calculate if the difference between the expected centenarians & actual centenarians is significant. But this calculation would be moving beyond quick & dirty.

Table 2. Number of Centenarians by State: Sorted by the Difference between the Actual & Expected

Per the data above, New York & Florida both have MORE centenarians than we’d expect to be in these 2 states, whereas California and my beloved Texas have FEWER centenarians than we’d expect to be in these 2 states. To better my odds of living to over 100, I should move from Texas to New York or Florida. New York & Florida are big states, so where in these states should I move to? I actually looked at data for all counties in the US, and the top 10 counties with the largest difference between the actual number of centenarians and the expected number of centenarians were all in either New York or Florida. Hartford, Connecticut finally breaks into the list at number 12.

Table 3. 15 US Counties with the Largest Positive Difference between the Actual & Expected Number of Centenarians

Conversely, the table below contains the 15 counties with fewer centenarians than we’d expect to live in these counties. I’ve lived either in or just next door to Harris County, Bexar County & Travis County for most of my life — all 3 of which are in the table below.

Table 4. 15 US Counties with the Largest Negative Difference between the Actual & Expected Number of Centenarians

Image from Adam Jones, Ph.D. on Flickr

But I won’t be packing my bags for New York County, New York right now. As always, correlation doesn’t indicate causality. Or in this case, geographic location might not in & of itself increase my chances of living to be over 100. Maybe people who wear green hats are more likely to live in New York County, New York; and wearing a green hat makes you live longer.

Come to think of it, I’d need a hefty pay raise to afford to live in New York. And just scanning down the names of the counties in Table 3, these counties have the reputation of having high median incomes — much higher than the US average. Or maybe we should substitute ”wearing a green hat” for “making a lot of money” An interesting next step would be to look at the median incomes of these counties with the largest difference between the actual & expected number of centenarians & see if we could identify a couple of counties with low to moderate median incomes and a high difference. But I digress. And since I plan on living in Travis County, Texas for the foreseeable future, I had better continue taking my cod liver oil.

Weird Census data stuff

• Yes, the 3 different tables sum up to 3 different total US centenarians counts. For example, table 1 says that there’s 53,364 centenarians whereas table 2 (the sum of the state counts) says that there’s 54,325 centenarians.  Differences like these happen when you compare different tables of Census data.
• I used the original population count data. There were revised population numbers for some states & some counties, but to just keep everything simple, I used the original Census 2010 population count data.

[UPDATE: We've updated the Housing & Transportation report too! 2012-01-30 by Anthony Morales]

We’ve been working on adding the most current American Community Survey data for small geographic areas (think: blocks & block groups) to the Cubit web application.

The Census Bureau recently released the American Community Survey (ACS) 2007-2011 5 year estimates data. And we’re adding ACS 2007-2011 data to the Cubit app. This data replaces the ACS 2006-2010 data.

The Income, Poverty & Jobs report in the Cubit app has been updated to include ACS 2007-2011 data.

If you need ACS 2006-2010 data, you can still access this data in the historic reports section of Cubit.

We’ll be updating the other Cubit reports with ACS 2007-2011 data in the next few weeks. Let Anthony or me know if you have any questions about the updated income data.

Thanks to gringer  on flickr for the thumbnail image of “money shot” for this post. 

I recently told a custom data request customer that “the Census Bureau sources that I use don’t provide 1 year incremented age breakdown data.” But while doing some research for another client, I stumbled across a report that provides age breakdown data in one year increments. So let me remove a bit of egg from my face, and we’ll continue.

There aren’t even any fun tricks I can give you to pull this data. Just head over to the Census Bureau’s American Fact Finder 2. Search for “single years” and select your geographic area of interest. And then look for the report named “Single Years of Age and Sex: 2010″ from the “2010 SF1 100% Data”.

Here’s what the report looks like:

This is actually pretty cool data. Since this post was a bit short & boring, I’m going to do a deeper dive in the future into the 1 year increment data focusing on people who are 100 years of age or older or centenarians.

Background

While the US Census Bureau does help collect school district population data, I can’t find school district data on the Census Bureau’s website. For example while I can select “all school districts in New York” in the American Fact Finder 2, I can’t actually pull any demographic or population data for the school districts. The Census Bureau’s website says that they have a project called the Education Demographic and Geographic Estimates project which “produces a variety of geodemographic data for the National Center for Education Statistics.” Ah! Now we are getting somewhere.

Let’s head over to the National Center for Education Statistics website. They have a cool tool called the School District Demographics Systems (SDDS).

Tool 1: NCES’ School District Demographic Systems

• Good for basic demographics & heat maps
• Bad for detailed demographics & pulling the most current demographic data available

Positive: SDDS is Easy to Use

Make your selections in the green box to right, and the map will change accordingly. For example, here’s a map of the Under 18 Hispanic persons for all Texas Unified School Districts.

If you want the actual data that’s displayed on the map, click on the Data Table button. Then click the Save to File button. Pretty nifty, huh?

You can get the following data points:

• Total Students
• Total Population
• Total Occupied Housing Units
• Owner Occupied Housing Units
• Renter Occupied Housing Units
• Average Household Size
• Average Family Size
• Percentage – Householder/Male
• Percentage – Householder/Female
• White Alone
• Black or African American alone
• Asian Alone
• Hispanic or Latino
• American Indian and Alaska Native
• Native Hawaiian and Other Pacific Islander
• OtherMulti-Race
• Total Population Under 18
• Sex by Age Breakdown
• Total Teachers
• Total Librarians / Media Specialists
• Total Revenue

Positive: SDDS Displays Individual Schools & School Boundaries

To see individual schools, select schools in the greenbox to the right. Zoom in to get more detailed data about individual schools. In the map below, you can see schools by type for schools in Harris County, Texas. In addition to school district boundaries, you can also get see individual school boundaries by clicking on Map Layers & changing the selections there.

Positive: Export Spatial Data for School Districts with the SDDS

If you want to build custom maps or do spatial analyses, you can also export the spatial data. Click on the Map Tools button, and then click on Get Spatial data. Select the data which best meets your needs – Census, ACS or SAIPE. If you don’t already know what dataset to use, you’ll probably need a little help from a demographic data nerd or spend a couple of hours digging on the Census Bureau’s website. The differences between these datasets are way more complicated than can be summarized in this blog post. Or you could just download all of the datasets & then pick between then later on.

Negative: Very Limited Data Points are available with the SDDS

So we’ve seen that the SDDS is an awesome and easy to use tool for pulling school district population data–kudos to the builders of this tool–but there’s a potential problem. What if you need data points for school districts that aren’t included in the list above? Here are just a few examples of data points that folks working with school district data want & can’t get from the SDDS (even though these data points below are published by the Census Bureau):

• median household income
• median home value
• household type & family type
• poverty data
• unemployment rates
• industry & occupation data
• transportation statistics
• educational attainment
• languages spoken at home

Enter a 2nd NCES data tool.

Tool 2: NCES’  Demographic Profile Webmap

• Good for more detailed demographic data & heat maps
• Bad for getting the most current demographic data available

The Demographic Profile Webmap is another awesome tool that works like the SDDS tool with the added benefit of providing us with even more demographic data. Oh boy! The only problem is that as of today the Demographic Profile Webmap provides us the ACS 2006 -2010 data. The most current ACS 5 year dataset that is available today is 2007-2011. Hopefully, this app will be updated in the near future. But as of now, you can’t get the most current data from this tool.

Technique 3: GIS Analysis

Let’s say you want the most current data available for the number of people who speak Spanish in a school district. Here’s a very simplified outline of how to do this analysis (warning: if you don’t have a basic understanding of GIS, the following outline might be gibberish).

How to Estimate Data Points for School Districts that are not included in SDDS

1. Download the school spatial boundaries. If you want school district boundaries, you can download them either from the SDDS (see above) or the Census Bureau’s website. If you want individual school boundaries, try the SDDS, but for the most current boundary data, you’ll probably need to contact either the school district or the state education agency.
2. Download the Census boundaries for your area of interest (i.e. state, county, etc.). You can download these boundaries from the Census Bureau’s website. If you aren’t sure what geographic level to use, Census block groups are the smallest level of Census geography with almost all data points. But in the case of language data, we can get much more detailed language data for Census tracts than for block groups. So you may have to do some guess & check to determine the best Census geography to use.
3. Use the school spatial boundaries as “cookie cutters” (aka calculate intersections) to cut out the Census boundaries that are contained within the school boundaries. Let’s pretend that Census Tract A, B & C are all contained within your school district boundary.
4. Then pull the Census language data for Census Tracts A, B & C from the Census Bureau’s website. Now if you are a GIS guru, you could do this step along with step 2 & combine the demographic data with the spatial data. But if you are like me and are more comfortable working with databases than with GIS (GISs? I’m not sure how to pluralize GIS. Or is it like sheep – and GIS is the pural?), you can pull the demographic data at this point & write a quick query to grab only the data points that you need.
5. Finally, sum the counts of Spanish speakers for Census Tract A, B, & C to produce your estimate of Spanish speakers in a school district. So if Tract A has 10 Spanish speakers, Tract B has 5 Spanish speakers & Tract C has 0 Spanish speakers, we can estimate that the school district has 15 Spanish speakers. Summation works well for basic count data, but if you need to estimate “median” values, you’ll probably need to use something like a weighted average calculation.

Hopefully, this post has armed you with enough tools & techniques that you can now pull the school district population data that you need. But if you’d rather not learn the difference between Decennial Census vs ACS data or when it’s better use use Census blocks vs block groups vs tracts, you can always hire me to pull this data for you.