In a recent Wall Street Journal article Heather Mac Donald, of the conservative think-tank “The Manhattan Institute”, claims that poverty, racism, and social injustice are not root-causes of crime. Mac Donald argues that under conventional left-wing wisdom, crime should be increasing due to the current economic recession. Mac Donald cites 2009 FBI crime statistics that reportedly show national decreases in crime. She writes, “The recession of 2008-09 has undercut one of the most destructive social theories that came out of the 1960s: the idea that the root cause of crime lies in income inequality and social injustice.”

Mac Donald criticizes government social service programs, including “after-school programs, social workers, and summer jobs”, suggesting that they are not effective in decreasing crime.

Is Mac Donald’s analysis too simplistic? Does a decrease in some crimes really demonstrate that crime is not linked to poverty, race, or social injustice? Even if one were to accept Mac Donald’s arguments, is it really better for individuals and society for the government to eliminate needed social service programs?

The San Francisco Chronicle provided an update in an article today on a case involving minority residents of Section 8 housing in Antioch, California, that was first written up by the Race Equity Project E-Newsletter a year ago.  The subject of E-Newsletter 3.6 was the intersection of criminal law, race, and poverty law practice.  The specific case was described in the article titled, “Targeted Enforcement of Section 8 Participants in Antioch.”  The case, brought by Bay Area Legal Aid and Public Advocates, Inc. on behalf of primarily African-American Section 8 tenants in the city of Antioch, alleged that the City’s special police enforcement division, called, the “Community Action Team” (CAT), had systematically targeted Section 8 tenants for police enforcement (“over-policing”) in an effort to drive those tenants out of Antioch and, in so doing, had violated those tenants’ civil rights.

The SF Chronicle reports that criminologist Barry Krisberg’s recent study confirmed that “Antioch’s police Community Action Team … has disproportionately concentrated on subsidized Section 8 housing for the poor, and even more so on black tenants.”

The CAT website says that the CAT’s goal is to protect the right it asserts Antioch residents have “to feel safe in their homes and neighborhoods…“  The purported right to be free from fear has yet to be codified in California law.  Based on what Social Cognition science tells us about how our mind’s implicit associations are primed to be unconsciously fearful of, especially, people of African descent by such things as watching the local evening news (see Jerry Kang’s article, “Trojan Horses of Race“), residents of Antioch, Section 8 tenants included, are likely caught in a vicious cycle of unfounded fears confirmed, in many of their minds, by the experience and reporting of targeted enforcement of low-income, African-American households.  Maybe what is needed, at least in part, is some anti-bias training for fearful residents of Antioch and its police officers in order to raise the impact of unconcious biases to the conscious level where they may be dealt with openly and Constitutionally.

Professor Bruce Ralston, the programmer who created my favorite 2000 Census data extraction tool (SF3toTable Pro), just released AFF Mapper, a freeware program that enables users to create KML files (files that can be used with Google Earth and other online mapping applications) from Tigerline data and tables available through American FactFinder. We haven’t tried the software yet but, based on Professor Ralston’s previous work, our expectations are high.

Data-philes are abuzz with the recent launch of Data.gov a website created “to increase public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government” according to the site description. Users can search the vast Data.gov catalog by category (Income, Expenditures, Poverty and Wealth; Law Enforcement Court and Prisons, etc.) and/or agency (Centers for Disease Control and Prevention or the Department of Defense, for example). The site offers two ways to access data: 1) the raw data catalog consisting of instant view/download of platform-independent, machine readable datasets in a variety of formats, and 2) a Tool Catalog with application-driven access to Federal data with hyperlinks and featuring data-mining and extraction tools and other services.

Feeling somewhat befuddled by these descriptions? No worries if you are, like me, technically-challenged. Using the handy Data.gov tutorial, I was able to determine with a five minute search that between 1999 and 2004, the lung cancer mortality rate in California for every 100,000 African-Americans was 55, significantly higher than for Whites at 44 per 100,000 (noting that this last figure probably includes Latinos because of the way in which the census categories Hispanics or Latinos). Incidentally, the link that Data.gov sent me to for these statistics, the CDC’s Wide-ranging Online Data for Epidemiological Research or WONDER, is a fantastic resource in its own right. Finally, Data.gov’s authors describe the website as a work in progress and invite users to suggest additional data sets and site enhancements to make the site an even more comprehensive tool for access to federal government data.

The Sacramento Bee posted two articles today, Dan Walters: Stanford study of exit exam shows fallacy and Some graduation rates worse with high school exit exam, on how the California High School Exit Exam instituted in 2006 has a disproportionate effect on graduation rates of minorities and young women. These findings are based on a study by the Institute for Research on Education Policy & Practice at Stanford University (IREPP) , “the Stanford study looked at graduation rates for students who stayed in school all four years – both before and after California initiated the exit exam. Since the test became a requirement, the study found, a disproportionate number of those who didn’t graduate because of the test are minorities and girls.” Interestingly, the study attributes these findings to what the IREPP researches call the “stereotype threat.” Essentially, this threat is defined as the extra stress on nonwhite and female students to do well on these exams, so as not to confirm negative stereotypes about their group.

On April 13, 2009, the Race Equity Project gave a presentation on the use of mapping and GIS in public interest advocacy at the UC Davis Law School. Clicking on the slide show image below will take you to the REP’s Presentation page where you can view this presentation.

On March 20, 2009, the Williams Institute of UCLA Law released a study demonstrating  that gay, lesbian, and bisexual (LGB) couples of color experience markedly higher rates of poverty then heterosexual and White LGB couples. “For example, white gay men in same-sex couples have poverty rates of 2.7%, compared to 4.5% of Asian or Pacific Islander, 14.4% of black and 19.1% of Native American gay men. While just under 6% (5.7%) of non-Hispanic lesbians are poor, that rate is more than tripled (19.1%) for Hispanic lesbians in couples.” Take a look at the complete study.

The California Dropout Research Project,  a project based at the the UC Santa Barbara Gevitz School of Education, recently released a report that indicates Californian Latino and Black high school students graduate with alarmingly low numbers when compared to Asian and White students.

“Graduation rates for African American and Hispanic students are much lower at 60%, or more than 20 percentage points below the rate for Asian and White students.” In case you dislike data without proposed solutions, the California Dropout Research Project released a guide entitled Solving California’s Drop Crisis in 2008.

A recently published report by the Institute of Race and Poverty, Communities in Crisis:  Race and Mortgage Lending in the Twin Cities, examines the history of the home ownership in America through the lens of race and poverty.   The report substantiates that even when controlling for income and FICO scores that people of color are denied prime mortgages at much higher rates.

“The denial rate for blacks with incomes above $157,000 was 25%, while it was just 11% for Whites making less than $39,250.”

The report is  accessible despite going into  detail about the prime and sub-prime mortgage lending systems.   An interesting finding is that the growth in home ownership in communities of color started well before the emergence of sub-prime lenders in 2003, which supports that an increase in home ownership in our communities can be sustained independent of the sub-prime market.

The Use of Geographic Information Systems in Poverty Advocacy is the cover article in this month’s Clearinghouse Review. The authors are Jason Reece, a senior researcher at the Kirwin Institute for the Study of Race and Ethnicity and (drum roll, please) our own Eric Schultheis, principal editor and GIS guru at LSNC’s Race Equity Project. (Looking for more about GIS? Check out LSNC’s GIS Mapping Resources and Statistics | Data pages and the REP’s Mapping Race and Data|Demographics pages.)

Obtaining historical census data and boundary files is a huge pain but luckily it just got a whole lot easier. The National Historical Geographic Information System (NHGIS)  “provides, free of charge, aggregate census data and GIS-compatible boundary files for the United States between 1790 and 2000. ”

NHGIS will be realigning historical bounds to 2010 bounds (once those bounds are released) over the next few years. The wealth of data and shp files makes this a must stop for data hounds searching for free resources.  For those of you without desktop GIS capacities, NHGIS has partnered with Social Explorer to provide any one with an internet connection the ability to visualize and analyze the historical Census data they provide.

Take a look at these articles about NHGIS:

• NHGIS… A National Treasure
• Nearly Two Centuries of Data – Historical Boundary and Statistical Datasets Available from NHGIS

When faced with multiple site location possibilities or with potential site closures, one important question to answer is the level of client access using public transportation to the various siting options. For instance, siting a public health clinic several miles from the nearest public transportation access point will likely preclude clients without private vehicles from accessing the services. Similarly, if you are facing proposed site reductions, you may want to evaluate access to each of the possible site closures to make sure that you advocate that the site with the highest level of access remain open.

The days of wading through public transportation schedules and transit station lists to determine site access are over.* Let me introduce you to Google Transit. Google Transit allows you to determine whether travel between two locations is possible using public transportation. It also gives you estimated travel time, travel cost, and the ability to search travel possibilities based on the time of the day.

The point to point feature limits your ability to determine overall accessibility. However, Google Transit is particularly useful if you want to evaluate access to a site from a series of set locations. For instance, let’s assume that a city is closing a public health clinic, a clinic that is primarily accessed by homeless persons. You could use Google Transit to determine public transit access to the remaining clinic locations by determining travel routs to the remaining clinics from area shelters and encampments.  You could also use Google Transit to determine the affect of a proposed clinic closure on travel time to access services. Take a look at a map we created using Google Transit and ArcView to depict this hypothetical scenario.

* Authors note: Google Transit covers over 75 US areas including cities, counties, metropolitan areas, and states from Atlanta to Walla Walla. Take a look if you area is covered.

I hate it when I’m trying to link a dbf to a shp file and, while I’m working with the data, Excel decides to drop the leading zeros in the FIPS code making it impossible to link the demographic data to the correct geography. One possible cause of this “dropping zeros” problem is that Excel has formated the cells containing the FIPS data using its default settings. I often remedy this issue by changing the cell formatting for the FIPS entries to a number custom setting with five zeros. Another solution is to convert the FIPS numbers into string data. Here is a great tutorial on how to do this.

Tired of relying on 2000 Census data but hesitant to use ACS data due to its high margins of error? Thankfully the Census Bureau just released 3-year data for cities and places at least 20,000 persons in size. This data has a significantly lower margin of error then the normal 1-year ACS survey data.

“New data released today by the U.S. Census Bureau give more than 2,500 midsized counties, cities and towns nationwide (those with populations between 20,000 and 64,999) their first statistical “portrait” since the 2000 Census on a wide range of key socioeconomic and housing topics.

These are the first American Community Survey (ACS) estimates that combine three years of survey responses (2005-2007) to produce data. The technique makes it possible to release a new set of multiyear estimates annually for smaller geographic areas. The three-year data can produce
estimates for areas with populations as small as 20,000.”

“The 2005-2007 ACS estimates are based on three years of data collected nationwide from about 250,000 addresses per month. In addition, approximately 20,000 group quarters across the United States are surveyed each year, comprising approximately 200,000 residents annually.

The population threshold for which geographic areas have three-year estimates available is 20,000. For nation, states, counties, cities, and towns, the estimated population used to satisfy that threshold is the July
1, 2007, Census Bureau Population Estimates. For other areas, the 2005–2007 ACS three-year estimates of total population are used. For example, since the Census Bureau’s Population Estimates are not available
for school districts, three-year ACS estimates of total population are used to determine if a school district meets the 20,000 population threshold.

As is the case with all surveys, statistics from sample surveys are subject to sampling and nonsampling error.”

Excerpt quoted from Census Bureau Public News Alert issued on December 9, 2008.

We were excited to see a slew of presentations on applied mapping and data analysis in poverty advocacy at the 2008 NLADA Annual Conference. The REP participated in two mapping/data sessions: Using Mapping, Data, and Databases for Advocacy and Program Improvement: An Introduction; Using Mapping, Data, and Databases (II): Developing and Implementing Mapping and Data Capacities.

The presenters for the first session were Eric Schultheis (aka ElectroMoose) from the Race Equity Project, Ann Joyner from the Cedar Grove Institute for Sustainable Communities, and Julie Aguilar Rogado from the Health Rights Hotline. The presenters for the second session were Eric Schultheis, Ann Joyner, and Jason Reece from the Kirwan Institute on the Study of Race and Ethnicity.

Eric Schultheis’s presentation slides are available below in JPEG format as self-extracting ZIP files:

• Slides from introduction session
• Slide from second session discussing map design

(If you use images or content from these slides please provide the following attribution: The Race Equity Project, Legal Services of Northern California, www.lsnc.net/equity)

DataPlace unveiled its new Neighborhood Metrics system today. Neighborhood Metrics allows users to “create custom metrics (or indices) by aggregating existing dataset indicators.” Lets say you want to identify neighborhoods of low-income persons at high risk for foreclosure. You could create a metric that “combined high interest loans for both refinance and home purchase, to low-income borrowers in 2006″ and then map the metric. Instead of dealing with multiple layers, DataPlace now allows you to create and display a single easy to understand layer based on the user defined metric. We still haven’t gotten a chance to really explore Neighborhood Metrics but, at least at first glance, it seems to have some real promise.

Here’s the situation: you sent out a bunch of FOIA or discovery requests and you get pages of data in PDF format.  After dreaming of federal or state regulations requiring data to be sent in XLS or DBF format,  what will you do? Fear not, we recently found a free web application that will save you from manually copying the data into an Excel spreadsheet.  Zamzar allows you to convert PDFs into a variety of more useful formats. If you need to convert a PDF into XLS follow the following steps: (1) convert the file into a TXT file using Zamzar and (2) import that text file into Excel (you will probably want to select a “fixed width” import if the data in the original PDF was in columns). Viola! It’s that easy. (Although probably not worth mentioning, if you need the data in DBF format for use with GIS software, simply save the XLS file as a DBF in Excel.)

The REP has been a big fan of online mapping applications for some time now. How could anyone be against user-friendly applications that allow advocates to document the variety of socio-economic issues facing low-income households? One drawback, however, to these applications has been a limited set of mappable indicators. It appears that the folks at KnowledgePlex have made some serious strides to solving this problem.
KnowledgePlex just launched their new beta version of DataPlace. DataPlace was already a wonderful mapping application. The new version is even better! We are still playing with the beta but users will be able to map almost 4,000 different indicators once the new version is fully functional. Wow!

Is GIS an effective advocacy tool or is it just hype? You probably already know how the REP will answer that question. We believe that GIS and data analysis can be used as an amazingly effective advocacy tool. Not only does GIS allow advocates to capture and demonstrate the spatial component of poverty but GIS and data analysis offer advocates a method to capture the rare ground of being perceived as objective. positions.

We’ve noticed, however, that there is a dearth of examples of how GIS has been used to support advocacy projects. There is also precious little discussion on how design choices are critical to effective “persuasive mapping.” Most poverty attorneys have no problem agonizing over word choice but we rarely extend the same considerations to map design.

In an effort to remedy these problems, we have a spanking-new application of GIS in poverty advocacy to share with you.

The Stage Is Set

In California, county’s are required to provide “last resort” medical services to indigent residents. Cal. Welf. & Inst. Code § 17000. Sacramento County provides these services via the County Medically Indigent Services Program (“CMSISP”). Recently, Sacramento County proposed severe cuts to CMISP. Pursuant to state law, the County was required to provide a public hearing on the proposed reductions with fourteen days prior notice. Cal. Health & Safety § 1442.5. The County proposed cutting CMISP by 52.7 full-time employees. Several of the positions proposed for elimination were RN and public health nurse position. Interested in a complete listing of the proposed reductions? Take a look a look at the proposed reductions.

The Maps

The following maps were submitted to the Board of Supervisors with the Race Equity Project’s written statements regarding the proposed reductions. Several of the maps were also presented to the Board during the Race Equity Project’s oral testimony.

This map depicts the locations of the proposed cuts, the proposed staff reductions, and describes the proposed reductions. The map was purposefully designed to look crowded with call-out boxes describing describing the proposed reductions to highlight the scope and breadth of the proposed cuts (i.e. there are so many cuts that text descriptions of the cuts fill the entire map).

This map depicts the CMISP eligible population within the City of Sacramento and the current location of CMISP clinics. One dot equals one CMISP eligible person. The map was designed to highlight the large number of CMISP service consumers in the city. To accomplish this purpose we used a one to one relationship between dots and CMISP eligible persons and a large dot size and choose a dot color that was visually overpowering thus making the dots the fist thing you notice when you see the map.

This map was designed to highlight the disparate impact of proposed reductions at a specific clinic would have on persons with limited English proficiency. The color scheme for the two categories of persons (i.e. the dots) was chosen to highlight the category of limited English proficient persons.

This map depicts the CMISP eligible population by race/ethnicity (White or Of Color). The dot color scheme was chosen to accentuate the CMISP eligible population of color.

This map depicts the CMISP eligible population that resides in proximity to three CMISP clinics where reductions were proposed. The dot color scheme was chosen to accentuate the CMISP eligible population of color and the disparate impact of the proposed reductions.

This map depicts the CMISP eligible population that resides in proximity to a single CMISP clinics were reductions were proposed. The dot color scheme was chosen to accentuate the CMISP eligible population of color and the disparate impact of the proposed reductions.

This map depict the the difference in expected travel time to receive medical services if a specific CMISP clinic was closed as proposed. The location of the “fictional” CMISP client was obtained from actual CMISP client records. An intuitive color scheme was chosen to depict the routes. The route color scheme (green and red) also suggests the “goodness”/undesirability of a particular route.

All maps were produced by Eric Schultheis, Staff Attorney, using desktop GIS software and Census data.

The Race Equity Project was able to design and produce these maps in under ten days. We believe that the unpredictable and often fast-paced nature of poverty advocacy and the varied types of poverty advocacy scenarios where maps can be used argue in favor of developing in-house GIS capacity. Absent in-house capacity, it is, at best, questionable whether the Race Equity Project could have used GIS and maps to advocate against the above-discussed healthcare reductions.

This post was authored by Allan M. Parnell, Ph.D., Cedar Grove Institute for Sustainable Communities.

We were asked by Reed Colfax of Relman and Dane, PLLC to determine whether there was an association between race and access to public water services in the Coal Run area in Muskingum County, Ohio. The Coal Run area is just outside of Zanesville, Ohio. In particular, Mr. Colfax was interested in having the patterns mapped. My colleagues in this analysis were Ben Marsh, Professor of Geography and Environmental Studies at Bucknell University, and Ann Moss Joyner, my colleague at the Cedar Grove Institute of Sustainable Communities. The results of our analysis were shown in a set of maps shown to the jury.

Census data clearly were not going to be useful because of the small size of the neighborhood. The core of the neighborhood is in two census blocks, but census data was not helpful because of the scale and distribution of residents within the blocks. Within each block, the northern part is predominantly white and the southern part is predominantly non-white. One plaintiff lived in a third census block, and the residents on the north side of Adamsville Road are in a fourth census block. There was no clear way to use Census data to show the whether race is a factor in access to public water. I proposed a house-by-house analysis within the neighborhood.

We obtained public Geographic Information Systems (GIS) data from Muskingum County. The key data were the parcel data, which identified all occupied houses in the study areas, the location of water lines with dates of construction, Zanesville’s city limits, and the street locations. Relman and Dane obtained water billing data giving the addresses of all houses with public water service. I field-checked the parcel data confirming that each property identified in the parcel data was an occupied house. We also geocoded the location of the water plant.

We knew the race of plaintiffs, but we did not know the race of the residents in the other houses. I designed a short survey asking the number of residents, the race of each resident and how long they had lived at that address. Under my supervision, two employees of Relman and Dane went door-to-door collecting the household data. The survey took place over two days. If one resident in a household was non-white, the house was coded as non-white. In the very few cases where we were unable to speak to any resident, the house was coded as unknown. Using the public GIS data, the household survey information, the plaintiff information and the addresses of houses with billed water service, Ben Marsh built the GIS layers for the maps showing the clear pattern of racial discrimination. I wrote the expert report using the maps, the survey information, and information from the plaintiffs.

Reed Colfax and John Relman decided to build the maps before the jury, layer by layer, adding information to the base map, explaining where the information in each layer came from.

Map 1 is the base map, showing the location of the Coal Run area relative to the city and the water plant and the roads.

Map 2 adds the location of public water lines as of the date when the case was filed.

Map 3 adds the location of occupied houses, showing the proximity of these houses to water lines. Note, however, that having a water line in front of your house does not necessarily mean that you have public water.

Map 4 shows which houses had billed water service. Note that one house south of the water lines had billed water service. This was an “special arrangement” made with a private line run to that house.

Map 5 introduces the race of the household with the water lines. The water line down Langen Lane clearly ends where non-white residences begin.

Map 6 adds billed water service again (the dark blue dots) confirming that most non-white houses did not have public water while most white houses did have water service.

While we believe that color schemes should be intuitively obvious (and thus white and black make sense here), it is difficult to use true black and have any internal symbol (the dark blue dot meaning billed water service) show up well. Thus, to designate houses of minority residents, a dark orange or light brown might have been more informative. Regarding the maps as shown, the attorneys chose the color scheme of the houses to indicate race.

A final map shows how far the public water lines extended. Note the location of the Coal Run area in blue.

The defense used GIS to try and make the case that race did not affect who had water service and that some areas with African American residents had water service so there was no pattern of discrimination.

The defense map pictured left shows the Coal Run area and surrounding area. Census blocks are coded by the number of African American residents. Water lines are shown in blue. The defense expert argued that all residents of a census block had water if a water line intersected any part of the block. This is demonstrably false, and he had difficulty with this argument in his testimony. The defense expert also pointed to the census block southwest of the Coal Run area that borders I-70 with 34 African American residents.

I had examined the census data for the block and found that while there were 34 African American residents in 2000, there were no African American households. Individuals in the census are classified as living either in households or in group quarters, and the African American residents all lived in group quarters. And they were all elderly. Clearly, they live in a nursing home that is 88% white. The water line in question services a health care facility that had no residents (black or white) when it was built.

This post was authored by Allan M. Parnell, Ph.D., Cedar Grove Institute for Sustainable Communities.

Webmaster’s note: Plaintiffs in this case received a near 11 million dollar jury verdict (attorneys fees reserved).

It’s rare that I’m this excited about a data/GIS resource. It’s equally rare that someone creates an online data/GIS resource as bug-free and comprehensive as the National Map. The USGS has outdone themselves with this nifty application.

The application, at first glance, looks like a typical online map viewer with a slightly more-complicated-than-usual interface. Don’t be fooled…this application packs a real punch. We used National Map to download 1-meter DOQ imagery for use in an ongoing advocacy project. After a little tinkering, I selected the five block area I wanted the DOQ imagery for and began the download. Voila…twenty seconds later I had a GeoTIFF file with imagery of the selected area that is ready for use with ArcGIS.

The National Map offer a lot more than DOQs. Interested in topographic layers? LANDSAT7 imagery? Geology layers? Public land records layers? Transportation layers? National Map has you covered!

Mark Twain had it wrong. Data and statistics aren’t our enemies. In an effort to promote understanding between people and statistics, we want to share the following data resources with you:

United States Department of Agriculture – Economic Research Service

• Site Summary: ERS conducts research in five major areas: (1) a competitive agricultural system; (2) a safe food supply; (3) a healthy, well-nourished population; (4) harmony between agriculture and the environment; (5) an enhanced quality of life for rural Americans

• Suggested Uses: The REP doubts that you will ever need the US Carrot Statistics or the Hamburger Doneness and Consumer Preferences, however. ERS also offers access to a bevy of more germane data related to health, poverty, and food including Food Security statistics.

California Secretary of State – Reports of Registration

• Site Summary: The California Secretary of State is responsible for producing a statistical reports detailing voter registration levels throughout California several times per year.

• Suggested Uses: The data available here can help you measure and compare civic involvement in California communities.

Office of the Attorney General – State of California – Department of Justice

• Site Summary: The California Attorney General collects, analyzes, and reports statistical data, which provide valid measures of crime and the criminal justice process to government and the citizens of California.
  This site contains more than 5,000 statistical tables, 59 reports, 29 publications, links to federal state, and local agency statistics, and links to other criminal statistics services.

• Suggested Uses: Do you need arrest, domestic violence, or crime statistics for California? This is the place to go.

California Department of Corrections and Rehabilitation

• Site Summary: The California Department of Corrections and Rehabilitation publishes a variety of reports that range from statistical summaries of its offender populations to evaluation research reports on innovative rehabilitative treatment programs. Of particular interest, Adult Operations (formerly CDC) Population Reports and Statistics contain extensive data on characteristics of the institution and parole populations, historical trends, summaries of population movements and parole outcome (including recidivism).

• Suggested Uses: Looking for prison population or demographic reports? Searching for parole population or demographic reports? You will find them here.

Rural Healthcare Policy Council

• Site Summary:The mission of the California Rural Health Policy Council (RHPC) is to formulate and establish rural health policy for the State of California and to provide a focal point for discussion of rural health issues within the California Health and Human Services Agency.

• Suggested Uses: A good site for California rural population demographics and rural access to healthcare services information.

Office of Statewide Health Planning and Development

• Site Summary: The Mission of Office of Statewide Health Planning and Development (OSHPD) is to promote healthcare accessibility through leadership in analyzing California’s healthcare infrastructure, promoting a diverse and competent healthcare workforce, providing information about healthcare outcomes, assuring the safety of buildings used in providing healthcare, insuring loans to encourage the development of healthcare facilities, and facilitating development of sustained capacity for communities to address local healthcare issues.

• Suggested Uses: The REP found the statistics on the disparities along racial and ethnic lines regarding quality of hospitals particularly useful.

Department of Social Services

• Site Summary: A comprehensive source for data and statistics on government benefits and their consumer populations

• Suggested Uses: We like the food stamp, children’s benefits, and the disability and adult program statistics.

US Department of Transportation – Bureau of Transportation Statistics

• Site Summary: This site provides comprehensive transportation statistics for geographic areas within the United States.

• Suggested Uses: Access to public transportation and the effectiveness of the available public transportation is an effective indicator for issues ranging from access to medical care providers to environmental justice to access to employment opportunities.

Latinos are the largest and fastest-growing minority group in the U.S. according to new estimates from the U.S. Census Bureau released Wednesday. Latinos account for 15% of the U.S. population, while people of color overall comprise 34%. As minority populations continue to grow in the U.S. it will be interesting to see how our society deals with race, racism, and racial distinctions in the law. It is going to be important for all of us to take a hard look at the role of domestic and international law in promoting and/or demolishing racism.

The Census Bureau answers the question, why does the Census Bureau need to ask about race on its questionnaires?

We made a few new additions to our resources today that we think you should take a look at:

The Kirwan Institute for the Study of Race and Ethnicity

• Site Summary: The Kirwan Institute for the Study of Race and Ethnicity’s “ultimate goal is to stimulate and facilitate transformative change to bring about a society that is fair and just for all people.” It approaches this goal “with the understanding that all communities of people are interconnected and that by fostering positive relationships through research, scholarship and collaboration, we will deepen the possibility for change.” The Kirwan Institute is a true pioneer in the fight to achieve equitable results for communities of color and its research and advocacy, particularly around opportunity mapping, represents some of the most innovative and effective advocacy currently being undertaken by advocates for race equity. The Institute also posts all of their PowerPoint presentations on their site. Available presentations cover topics ranging from GIS to framing to structural racism.

HealthyCity

• Site Summary: HealthyCity offers perhaps the most comprehensive access to community resources, demographic/health data, and cutting edge online GIS mapping technology that the REP has ever seen. For the time being, the site only offers geographic coverage for Los Angeles county. We hope that HealthyCity will be going statewide soon but until that time we will have to stew in our jealousy of the wonderful online mapping and data analysis tools that residents of the city of angels have access to.

• Suggested Uses: If you have any mapping or data analysis needs related to Los Angeles county and you are not adverse to free, powerful, user-friendly online mapping and data analysis tools than HealthyCity is for you.

The REP recently had the opportunity to use EPA data in a mapping project for CORE. We wanted to share with you two nifty datasets that we found.

The first data set is the CERCLIS Superfund database. This database contains information on all NPL and non-NPL superfund sites in the country. Don’t bother trying to geocode the non-NPL sites as the street data is horrible. The REP suggests using the latitude and longitude data to locate the sites in the CERCLIS database. If the Superfund data leaves you a little confused, take advantage of the marvelous Superfund Info.Line (703-284-8214).

The second data set is the 2006 Toxic Release Inventory (TRI). This database includes every facility in the country that stores or releases toxic chemicals. Be aware that the six different dbf. files that compose the database can be a bit tricky to link as a single facility can have multiple entries. However, almost all the information that you need is in the first dbf. file. The REP recommends locating the facilities using latitude and longitude as the street data element is less than perfect.

Good news on the 2010 Census front. The Bureau just published the final criteria for 2010 block groups in the Federal Register. Despite early rumors that the minimum population for a  block group would be raised, the Bureau decided to keep it at the 2000 level of 600 persons.

Moose, why are you sharing this inane information with us? Let me explain. First, data and mapping folks (myself included) need to know what to expect from the 2010 Census so that we can continue expose inequities. Second, an increase in the minimum population of a block group would have the effect of hiding small and insular communities (the communities we serve) so the news means that the Census will remain a uniquely powerful and complete data source for poverty advocates. Lastly, I figured we could all use some good news to mollify our sadness about the demise of the “long form.”

The demise of the census long form is common knowledge amongst Bureau fanboys and girls. However, we all could use a little more information about the big switch. For instance, did you know that the sample rate will change from 1 in 6 to 1 in 40? The current issue of ArcUser has a niffty article comparing the long form and the new ACS.

I also liked the article “Data Evaluation Tips: Buliding more accurate demographic databases.” Who isn’t interested in the pycnophulactic and dasymetric methods for converting data between different geographic levels? These methods’ scary names belie their practical applications in mapping poverty, race and opportunity.

LSNC and other Legal Services corporations have been inundated with foreclosure related issues in recent months. As most poverty attorneys who have handled a foreclosure case will attest, prevention and early intervention are key to preventing foreclosure. Of course, before you can deliver education and community outreach to LSC eligible homeowners you need to know were they live. You also need to identify where populations of LSC eligible homeowners of color live. In many areas, due to the greater frequency of asset accumulation of Whites, if you do not include a separate analysis of race and ethnicity you will end up serving mostly White LSC eligible homeowners and excluding homeowners of color.

Lets conduct this analysis for Los Angeles County, California, using free online mapping software and free census data extraction tools so that any Legal Services program can replicate this process for their service area if it has a computer, Microsoft Office, and an internet connection. The online mapping software we will use is DataPlace and the free census extraction tool is GIStools. For purposes of space, this tutorial assumes that you have created an account with DataPlace and have downloaded the software and relevant datasets for GIStools.

First, we need to identify what Census data to use. Since we want to identify areas where foreclosure services outreach will have the greatest impact, we need to find the areas in Los Angeles county where there is a high percentage of homeownership and where there is a high percentage of LSC eligible clients. Since we also want to make sure that our outreach effectively reaches all LSC eligible homeowners we also want to find areas where there is a high percentage of homeowners of color and a high percentage of LSC eligible persons.

My suggestion is to use three datasets from the 2000 Census, SF3, to identify areas for possible outreach – P088 (Population by ratio of income to poverty), H007 (Tenure), and H013 (Tenure, White alone not Hispanic). Since we will be using DataPlace to map the data, we need to obtain the data at the Census Tract summary level (the smallest unit of Census geography that DataPlace can map). If I was going to map the data using ArcGIS, I would probably want to obtain the data at the Block Group summary level (the smallest unit of Census geography SF3 data is available at). Using GIStools extract the P088 and H013 data for California at the Census Tract summary level. Open the dbf. files (one for P088, one for H007, and one for H013) using Excel and copy and paste the all the data sets into one Excel spreadsheet (view example). Delete all rows for Census tracts not in Los Angeles county. You can tell which rows are for tracts in Los Angeles County by looking at the FIPSCO column. The first two numbers in the entry (“06″) represent the state code for California. The next three numbers are the FIPS codes for the various coutys in California. Los Angeles county’s FIPS code is “037.” Simply delete all rows where the value of the FIPSCO entry is not “06037″ (view example).

Second, we need to combine some of the data fields to simplify our spread sheet. Columns P088001 – 10 represent the various data fields of P088. Take a look at the Cenus Bureau’s technical documentation of the 2000 Census SF3 file to see what each of these columns represent. Since we want to know the count of person’s below 200% of the poverty level (our approximation of LSC client eligibility), we will create a new column (LSCPOP) that is the sum of P088002 – 09. The values of LSCPOP give us the count of potentially LSC services eligible persons for every Census tract in Los Angeles county (view example). Since it would also be helpful to know what percentage of the population is LSC eligible, lets create a new column (PERLSC) and use Excel to divide LSCPOP by P008001 giving use the percentage of the population that is LSC eligible for every Census tract in Los Angeles county (view example).

Columns H007001 -03 and H013001-03 represent the various data fields of H007 and H013 respectively. Take a look at the Cenus Bureau’s technical documentation of the 2000 Census SF3 file to see what each of these columns represent. We are going to use these columns to calculate the count of homeowners, the count of homeowners of color, and various percentages related to these categories.

The count of homeowners in a Census tract is simply H007002. We need to create a new column (PEROWN) to calculate the percentage of the population in each Census tract that is a homeowner. The values for this column are obtained by dividing H007002 by H007001 (view example).

The count of White homeowners is simply H013002. We need to calculate a new column (PERWHITE) to calculate the percentage of homeowners who are White in a Census tract. The values for this column are obtained by dividing H013002 by H007002 (view example).

We need to create a new column (HOCOLOR) to calculate the count of homeowners of color in a given Census tract. The values for this column are obtained by subtracting H013002 from H007002 (view example). We also need a new column (PERHOCO) to calculate the percentage of homeowners who are homeowners of color in a Census tract. The values for this column are obtained by dividing HOCOLOR by H007002 (view example).

Before we move on to step three, lets “clean up” our spread sheet by deleting columns that we no longer need. Before we do this, copy the whole spreadsheet and “Paste Spacial” it into a new spreadsheet. Paste “Values” only. Take a look at our the “cleaned up” spreadsheet showing the columns that we need to keep (view example). You should also replace all “#DIV/0!” with a null value and delete any extra sheets.

Third, we are going to identify the census tracts in Los Angeles county that will likely be the best areas to provide foreclosure related services to. We need to set the a criteria for identifying the tracts. What criteria you set is, of course, fairly subjective. When I create criteria like this I tend to select criteria that are restrictive enough to eliminate the majority of tracts but not so restrictive that I the pool of identified tracts is too small to be useful. For the purpose of this tutorial, our criteria will that tracts must have (1) 200 or more units that are owner occupied and (2) have a population that is 70% or more LSC eligible.

Create a new column (IMPACT). Using the Excel “If function” calculate whether each tract meets the criteria discussed above. Our “If Function” will read “=IF(PERLSC<.7,"",IF(H007002<200,"",1))"(view example). This formula will return a one in the IMPACT field if the tract meets our criteria and a null value if it does not.

Create a new column (RIMPACT). Using the Excel “If function” calculate whether each tract meets the criteria discussed above. Our “If Function” will read “=IF(PERLSC<.7,"",IF(H0COLOR<200,"",1))"(view example). This formula will return a one in the RIMPACT field if the tract meets our criteria and a null value if it does not.

Fourth, lets map! Save the Excel file as “Foreclosure Data.” Logon to Dataplace. Select “My Dataplace.” Select “Create New Project.” Title your project and select “Save.” Select “Upload the file from your computer.” Browse to the “Foreclosure Data” and upload it. Select “Extract Tables.” Select “Create Dataset.” Enter the required information. You may leave the default dates or enter todays date. The source of the data is “SF3 2000 Census.”Select “OK.” Select the entry “SFID.” Unselect “Indicator” and select “Part of Region.” Select “Update.” Select “Generate Indicators.” (It will take a few minutes for DataPlace to generate the indicators…be patient.) Lastly, map the indicator of your choice (IMPACT or RIMPACT) and zoom in to the appropriate level so that you can see those Census Tracts were large populations of low-income homeowners live.

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