Project tips learned while reporting on the deaths of homeless people in Santa Clara

For me, numbers tell me what’s happening and make me go, “Wow!” Of course, a good story does, too. A few things stand out from reporting on the deaths of homeless people in Santa Clara County, California, which was really my first attempt at a data journalism project. 

Homeless people die young. They die 25 to 30 years younger than people who remain housed, according to the doctors who treat them, and the data given to me by the county medical examiner. In Santa Clara County, homeless deaths are skyrocketing while the homeless population has not substantially changed. Deaths were up about 350% over a 19-year period, partly due to many Baby Boomers who have become unhoused.

Third, more homeless people are dying in the county’s largest city San Jose than anywhere else, and in one ZIP code the numbers are striking. The Rev. Scott Wagers of CHAM Deliverance Ministry in San Jose said the homeless people in ZIP code 95112 are living — and dying — along the creek beds, hiding because they don’t want to be swept up. 

“They’re cut off from health care out there,” Wagers said. “It’s apocalyptic.”

Wagers’ ministry provides food and other necessities to homeless people by meeting them where they are, by the creeks. Nearly 100 people died homeless in 95112 over a 19-year-period, excluding the number that die in hospitals. About 400 died in hospitals in the county over that period. 

I would offer the following recommendations for others taking on a similar project:

1. Stay organized. It helped me immensely to keep a notebook with important results from the data analysis part of the project. I kept a separate folder on my computer for files related to the project. If you’re working in a programming language, keep a record of the code you’ve written. RStudio for example, has a window to write the code before executing it and you can save the code in RStudio. 

2. Stay motivated. Believe the work matters. I lost motivation after the first of three stories was published. I would recommend finding encouragement from somewhere or someone. Only after I received encouragement from a friend did I get going again. 

3. Find a good data source who is willing to work with you. I was fortunate to have found that Santa Clara County’s medical examiner was willing and able to provide data. Offices in some of the other Bay Area counties said they do not track homeless deaths. 

4. Seek help when you need it. Accept help when you need it. The 2020 California Fellowship offered a great deal of help that made my project possible. From the beginning, when I received the data in PDF format, I was able to talk to a data expert who told me I could transform the data into a spreadsheet. Later I received guidance from a data journalist who provided more suggestions that proved helpful. 

5. Plan for delays. The data analysis I did took longer than expected. Editing took longer than I expected, too. I would have finished more quickly had I planned for potential delays.  

6. Don’t give up on anything. Be tenacious. Be fearless. I tried numerous times to get in touch with Pastor Wagers, and thankfully I did not give up. After several attempts by email I finally found a phone number and with a little trepidation gave him a call. He was more than willing, even excited, to work with me. 

7. Check and recheck your work. Fortunately, as I was completing the second story, I noticed a discrepancy in data I tabulated. Those numbers helped make up a map of homeless deaths by ZIP code. Checking the data and the prose gave me peace of mind.  

8. Trust your intuition. I made a mistake that I could have caught had I followed this rule. Several infants died while homeless in Santa Clara County over the 19-year period I looked at, and one of those was identified by the medical examiner as a 12-year-old. The 12-year-old died from heat in a car, which didn’t make much sense. Had I trusted my intuition and called the medical examiner to parse this out earlier, I would have saved myself some frustration and embarrassment. 

9. Be prepared to be stretched. I thought I knew enough to analyze the dataset I received, but I was wrong, and it took much longer than I expected. Also, I tried my hand at data visualization, which was new to me and a great deal of fun. Plus, I got to start learning a valuable new skill.