GEOSPATIAL DATA REPORT : Finding and Using GIS Data

http://www.census.gov/geo/www/tiger/tgrshp2009/tgrshp2009.html
(or directly from http://www2.census.gov/geo/tiger/)

2009 version just came out on 10/1/09! Previous versions are also linked from this site.

The TIGER/Line Shapefiles are extracts containing selected geographic and cartographic information from the Census Bureau’s MAF/TIGER® database. Unlike the previous Cartographic Boundary Files site, this site includes block boundary files as well as hydrography, transportation/streets and landmark data files. Users can also download multiple TIGER/Line Shapefiles at a time via this FTP site.

Again, these files contain no demographic information but are designed to be used with decennial census population and housing data as well as other related federal datasets.

http://www.cmap.illinois.gov/LandUseInventory2005.aspx

This is one of the most popular datasets among urban planning students! It’s a land use dataset of the seven-county region of northeastern Illinois. Northeastern Illinois is comprised of Cook, DuPage, Kane, Kendall, Lake, McHenry and Will County. The data is available both in shapefiles and ArcGIS personal geodatabase formats. Land use is aggregated to 49 categories, and was created using black and white orthorectificed aerial photography that was captured in April, 2005.

Additionally, CMAP (formerly known as NIPC & CATS) released a revised (Version 2.1) 2001 Land-use Inventory, which can be used for any comparative studies with the 2005 Inventory.

On this site, find a link to their FTP site – registration is required (but free!)

For more sources for landuse data, see under Landuse & Landcover, US category.

http://www.icpsr.umich.edu/cocoon/ICPSR/SERIES/00059.xml

[Available to ICPSR members only - most U.S. higher education institutions are member institutions!]

“The United States Historical Election Returns series was developed by ICPSR and was supported by grants from the National Science Foundation and the National Endowment for the Humanities. ICPSR’s holdings of historical election data cover the years 1788-1990 and consist of several discrete datasets that contain county- and state-level returns for all elections to the offices of president, governor, United States senator, and United States representative.”

I haven’t used the files yet. I will post the resulted maps later..

http://www.americaview.org/

“AmericaView (AV) is a nationwide program that focuses on satellite remote sensing data and technologies in support of applied research, K-16 education, workforce development, and technology transfer.” Most state’s AmericaView program provides access to its satellite imagery and remotely sensed data collection. Visit the member state websites below to find more about the state programs.

Current full member states are : Alabama, Alaska, Arkansas, California, Georgia, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi, Montana, Nebraska, New Hampshire, New Mexico, North Dakota, Ohio, Pennsylvania, South Dakota, Texas, Virginia, West Virginia, Wisconsin, Wyoming

Current affiliated member states are : Colorado, Nevada, New York, North Carolina, Utah, Vermont, Washington

http://www.woodstockinst.org/

Woodstock Institute is a nonprofit organization and a strong advocate of housing issues in Chicago. The organization has been collecting foreclosure data in the Chicago metropolitan area. The foreclosure data is now accessible through the Woodstock Institute’s website and also through Everyblock Chicago website (http://chicago.everyblock.com/foreclosures/).

The NU Library owns the Woodstock’s Chicago Area Community Lending Fact Book data on CD (1999-2004, tract level.)

[Image on the left: Foreclosure rates per 10,000 mortgageable properties in 2007 - click on the image to enlarge.]

http://www.huduser.org/Datasets/nsp_foreclosure_data.html

HUD’s Neighborhood Stabilization Program (NSP) local level foreclosure data is a statistically generated dataset to estimate a foreclosure rate as small as at a tract level. The data was created, under a new law, Economic Recovery Act of 2008, to assist local efforts to sustain high risk neighborhoods by acquiring and redeveloping foreclosed properties. Since foreclosure data is not readily available (no public & nationwide data sources available), some users may find the data useful. Remember: the data is not actual data but estimated numbers using public datasets – read the methdoroloogy document!

[Image left - HUD NSP Foreclosure Rate in Chicago metropolitan area (sorry, I forgot to add a legend!) - click on the image to enlarge]

http://na.unep.net/datasets/datalist.php

The United Nations Environment Programme (UNEP) / Global Resource Information Database (GRID) Spatial Data Clearinghouse offers a variety of global and international spatial datasets.

Datasets available are:

• Africa Population Distribution Database – years 1960, 70, 80, 90 & 2000
• Asia Population Distribution Database – year 1995
• Asia Administrative Boundaries – years vary
• Asia Towns/Cities point data with population – 1995 estimated & other pop years vary
• Latin America & Caribbean Population Distribution Database – years 1960, 70, 80, 90 & 2000
• Latin America & Caribbean Soil & Terrain Database
• Global Population Distribution Database – 1990
• Brazil Datasets – Soils & Vegetation

http://www.gns.cri.nz/research/qmap/qmapproducts.html#downloads

QMAPs, 1:250,000 geological maps of New Zealand, are georeferenced raster map by the Institute of Geological and Nuclear Sciences (GNS.) Not all map sheets are completed yet, but finished coverages are now available for free downloading.

http://www4.uwm.edu/eti/PurchasingPower/purchasing.htm

[Image left: Estimated annual expenditures for food away home per square mile, Metro Chicago by zipcode - click on the image to enlarge.]

What marketing people are very interested in is learning consumer’s spending patterns. The main data source for this is Bureau of Labor Statistics (BLS)’s , Consumer Expenditure (CE) Survey, which is available only at a national level not at any smaller geographic levels.

But, many people were also interested in learning “where” people might spend money – i.e. geographic patterns of consumer spending, so marketers/business data companies developed a way to create such data by adjusting the BLS Consumer Expenditure Survey data to local demographics using decennial censuses. This data is now widely available for fee from various companies, such as ESRI (data methodology),  Applied Geographic Solutions (data methodology – page 14), and Geolytics (the Northwestern University Library has this CD, 2003/Estimates and 2008/Projections, which include consumer expenditure at small geographic levels down to BG.)

GIS users who do not wish to spend money for this kind of data can use this Free Purchasing Power Profiles and Workforce Density Data for All Census Tracts and Residential ZIP Codes in U.S. instead. This dataset, based on 2002 CE survey plus 2000 census data, is provided by the University of Wisconsin-Milwaukee, Employment and Training Institute. It’s totally free, but the interface is not so flexible – I still don’t know what is the best way to download this data. Oh, well! It’s free – what can we say. The data methodology page is available from here.

http://www.nass.usda.gov/research/Cropland/SARS1a.htm
(Note 1: CDL is available for selected states only. Note 2: CDL data prior to 2007 is available at the Geospatial Data Gateway)

Cropland Data Layer (CDL) is a raster data set showing types of agricultural products grown on the land. The land use/cover analysis was conducted using satellite imagery and remote sensing analysis software (so I understand – more information about methodology & classification process is available from the methodology & FAQ pages.)

This dataset can be used as an alternative source for landuse/landcover data – which I learned from our geography instructor, Professor Greene. Though the focus of the NLCD (National Land Cover Dataset) and the CDL is different (i.e. NLCD=focuses on more general land use/cover types with more classes for urbanized area, CDL=more classes for crop types – instead of just one class, like “82: cultivated crops”), it’s possible to create a “crosswalk” to compare land use/cover changes over time since both datasets use similar classifications – like 1992/2001 NLCD crosswalk and Land Cover Institute’s Classification System Crosswalk table (scroll down a bit!)  As you see, main land use/cover classes are basically the same: water, developed/urban land, barren land, forests, shrub land, grassland, cropland/agricultural land, wetland, others/clouds – all we need is a crosswalk to create common classes. (Note: of course, we still need to consider data accuracy, data methodology differences.)

http://www.irs.gov/efile/article/0,,id=118376,00.html#DR

[Image left: showing % change in average adjusted gross income (AGI) between 2004 and 2007 (tax year) - click on the image to enlarge.]

Another IRS data – source for community (zipcode) economic/income level. Unfortunately, income ranges data isn’t available from this file – which is available for fee. See: SOI Tax Stats – Individual Tax Statistics – Zip Code Data

Pros: available annually since tax year 2004 at a relatively small geographic level (i.e. zip code.)

Cons: incomplete data – the data doesn’t include people who didn’t send tax forms.

Data fields included are:

• Age of primary taxpayers by range (<30, 30-44, 45-60, >60)
• Average adjusted gross income (AGI)
• Average refund
• County
• Town
• Zipcode
• and many tax form related fields

http://www.thearda.com/

“The Association of Religion Data Archives (ARDA) strives to democratize access to the best data on religion. Founded as the American Religion Data Archive in 1997 and going online in 1998, the initial archive was targeted at researchers interested in American religion. The targeted audience and the data collection have both greatly expanded since 1998, now including American and international collections and developing features for educators, journalists, religious congregations, and researchers.”

ARDA is a great source to look for religion information and datasets. The data includes U.S. county level demonimational profile data, which is now also accessible via Social Explorer.

http://www.irs.gov/taxstats/charitablestats/article/0,,id=97186,00.html

If you are looking for list of nonprofit, charitable or religious organizations, this is a good starting point. This dataset includes name and address of organizations, classification/foundation codes and organizational codes (note: this is useful but incomplete!), etc.  See the current data document – http://www.irs.gov/pub/irs-soi/eobk09.doc

Here is one example: on May 17, 2009, Chicago Tribune published a story about a distressed Chicago neighborhood, North Lawndale (where homicide is the 4th leading cause of death in 2003 – above accidents, according to the Chicago Dept of Public Health report.) The center of the story was storefront churches. “Aldermen and business leaders in the North Lawndale area of Chicago are questioning whether the area’s numerous churches are causing economic harm.” Storefront churches: Causing more economic harm than spiritual good? (05/17/2009) A map in the paper showing location of churches along Roosevelt and Pulaski roads (which is not available online) intrigued me, so I mapped locations of churches in Chicago found in the IRS Exempt Organizations master file – see the map on the left (click on the map to enlarge.)  By comparing the map in Chicago Tribune and my map, it is obvious that my church data (extracted from IRS file where foundation code = 10) missed the storefront churches found by Chicago Tribune. Further investigation on the data is perhaps necessary.

According to my professor, Dr. Michael Barndt of Nonprofit Center of Milwaukee, the dataset is fairly comprehensive, but some information may not be updated. So, be aware of such shortcomings, if you use this data..

Other sources for nonprofit organizations are:

• National Center for Charitable Statistics (NCCS)/Urban Institute – http://nccs.urban.org – is said to have more comprehensive and updated data but this dataset is not free (thank you for the information, Professor Barndt..)
• Phone/yellow books – yes, old fashioned but still vital source of organization listings.

http://www.historicaerials.com/

“HistoricAerials.com provides free online access to historic and current aerial photography. You can view aerial photography from the 1930s through today. Use our multi-year comparison tools to detect changes in property. Come and explore your favorite points of interest at HistoricAerials.com.”

Keywords: historical, historic, airphotos, aerial photographs, aerial photos, orthophotos, orthoimagery, ortho photographs

http://acct.multi1.cps.k12.il.us/cps/accountweb/

This office provide CPSs (Chicago Public Schools) with table data and analysis, such as drop-out rate and performance – goal achievement rate. To access the data, click on “School and Citywide Reports” and then click on “Download Data” which includes publicly available data for the Chicago public schools and the district.

[Image left: Chicago Public High Schools attendance areas showing drop-out rates in 2006 - note: boundaries data files are not available from this site, just numeric data only.]

Note: On June 14th, 2009, Chicago Tribune reported on a questionable practice where CPS school officials manipulated school attendance records to keep the number of dropouts low. So, be aware of potential pitfalls of the “official” CPS records! Reference: In Chicago high school: Class attendance or numbers game? (06/14/2009); Dropouts on attendance books at Chicago school (06/14/2009)

Access to individual health related data is restricted as you can imagine – personal data must be protected/kept confidential. Hence public health data are generally available as aggregated data. Here are some public health data available for Illinois.

Typically, public health summary reports like the ones below contain: vital statistics, chronic disease cases such as cancers, and various community health indicators, such as asthma, (teen) pregnancies, infectious diseases, suicide/homicide rates, accidents & injuries.

County level data

• IPLAN (IL) http://app.idph.state.il.us/
  
• Cancer registry (IL) http://www.idph.state.il.us/cancer/statistics.htm (generate a table and copy and paste in Excel)
• More statistics and reports by type (IL) http://www.idph.state.il.us/health/statshome.htm (once generating a report, scroll down to the end/bottom, and click on “download” to get the data in comma delimited format.)
  

Municipality/community level

• [Note: the following data needs to be transcribed to data table format.]
• Cook County suburbs data reports http://www.cookcountypublichealth.org/programs-services/community-epidemiology-health-planning/community-profiles
• Chicago community area profiles:  http://www.cchsd.org/cahealthprof.html
• … check your local community’s public health offices

[Image above: EPA Superfund sites (worst hazardous wastes sites) over Illinois counties map showing pediatric cancer rates (per 100,000) ]

http://www-fars.nhtsa.dot.gov/

FARS (Fatality Analysis Reporting System) contains data on all fatal traffic crashes in U.S. and Puerto Rico. The data system was designed to “assist the traffic safety community in identifying traffic safety problems, developing and implementing vehicle and driver countermeasures, and evaluating motor vehicle safety standards and highway safety initiatives.”

FARS is a relational database containing four normalized tables (crashes, persons, vehicles, drivers) that are related to the others. You can either download the entire annual databases from a FTP site, or select cases by data fields and geography. To select cases, click on the Query tab and follow instructions (i.e. Step 1: select a year – submit – step 2: select fields – submit, etc.)

Make sure to read the data manuals to understand this dataset – they are available from this FTP site, ftp://ftp.nhtsa.dot.gov/FARS/FARS-DOC/.

[Image above: pedalcycle fatal collisions 2001-07, over Chicago bike paths.]

http://www.maplibrary.org/stacks/gadm/

“GADM is a database of the location of the world’s administrative areas. Administrative areas in this database are countries and lower level subdivisions such as provinces, departments, districts etc. GADM has been developed as part of the BioGeoMancer project. It was partly funded by the Gordon and Betty Moore foundation. The work was done at the Univeristy of California, Berkeley, Museum of Vertebrate Zoology, and the International Rice Research Institute by Robert Hijmans, Nell Garcia, Julian Kapoor, Arnel Rala, Aileen Maunahan, and John Wieczorek.” – accessed on 5/27/09 – read more on the website above.

http://apps.who.int/whosis/database/gis/salb/salb_MDATA.aspx

*Not a complete set!*  SALB is a project of UN (accessible through WHO – but why WHO?), which has been launched in the context of the activities of United Nations Geographic Information Working Group (UNGIWG).

SALB not only collects current boundaries but also keeps track of historical changes (but don’t go back too far..) So, it’s great resource if data is available for your study area. Data are available in two formats: ArcInfo exchange (E00) or Shapefiles. Check also the data providers page – you may find other related GIS data files from the data providers’ websites.

http://www.ngdc.noaa.gov/hazard/

“The National Geophysical Data Center archives and assimilates tsunami, earthquake and volcano data to support research, planning. response and mitigation. Long-term data, including photographs, can be used to establish the history of natural hazard occurrences and help mitigate against future events.”

We can extract global hazard event data, such as earthquakes, tsunamis, volcanoes, wildfires, by querying the hazard events databases (i.e. limit by geography, time/year, magnitudes, # fatalities, etc.)  After getting results, copy and paste them (in Excel, select “Paste Special” – HTML.)  Since the event data comes with geographic coordinates (lat/lon), you can map the XY events after cleaning up the header line (one header line only! Avoid space, special characters, long names) in Excel or a text editor.