[TRU Research] Web App Data Schema

[Stephen DeSanto]

Victrola is fine! 5:30 works for me. Gives me time to get there after work.

On Mon, Aug 26, 2019 at 3:09 PM Katie Wilson <katie at transitriders.org>
wrote:

> Cool, does Victrola on 15th work again, or is somewhere else better?
> Having an actual meeting, even if a small one, will help me to sit down and
> focus on this instead of getting distracted by other things. 5pm good, or
> 5:30 or 6?
>
> On Aug 26, 2019, at 9:38 AM, Stephen DeSanto <rachidian at gmail.com> wrote:
>
> I'm also available Tues evening. Otherwise I'll keep making updates from
> home. :)
>
> On Sun, Aug 25, 2019 at 4:43 PM Katie Wilson <katie at transitriders.org>
> wrote:
>
>> This is awesome, thank you Stephen. I put some thoughts in-line in red below,
>> and attached a hard-to-interpret spreadsheet with info about Business
>> Choice participants.
>>
>> I will try to schedule some time this week to start completing rows for
>> the businesses I’m sure about based on the info we have. If anyone wants to
>> get together for a spreadsheet workparty this week let me know, I have time
>> Tuesday and Friday evenings.
>>
>> Katie
>>
>> On Aug 25, 2019, at 3:37 PM, Stephen DeSanto <rachidian at gmail.com> wrote:
>>
>> Hi everyone. Made a few updates to the master list of employers
>> <https://docs.google.com/spreadsheets/d/1HmOcG7hJLD1G0unCMPcsDnXr4RIA_PMKEE5ne-hhQR8/edit?usp=sharing>
>> for the upcoming website:
>>
>>    - Added companies and transit benefits raw descriptions from TRU
>>    survey data
>>    - Added "Likely CTR Targets":
>>    https://seattletransitpasses-research.pbworks.com/w/page/133438365/Likely%20Target%20Assessment
>>    - Added "Potential CTR Targets":
>>    https://seattletransitpasses-research.pbworks.com/w/page/133437828/Potential%20CTR%20Targets
>>    - "Likely" / high-profile targets (hotels, banks) are highlighted in
>>    ORANGE
>>    - Added "Potential Poster Children":
>>    https://seattletransitpasses-research.pbworks.com/w/page/133439169/Potential%20Poster%20Children
>>    - Poster children are highlighted in GREEN
>>
>> Highlight colors are just to make it easier to find rows that a) someone
>> said should be included in the list, and b) probably needs benefits data
>> incorporated
>>
>> Things to be done:
>>
>>    - Normalize locations data For this and the “leaf scores” and the
>>    “polluter” columns, I’d be inclined to do this after we’ve got enough
>>    info to check the “publish” box. I could be wrong, but I feel like it will
>>    be less work that way.
>>    - Assign "leaf scores" to all companies that don't have one
>>    - Assign "polluter" etc badges to companies we want to name&shame
>>    - Add all of the hotels?
>>    https://seattletransitpasses-research.pbworks.com/w/page/133666440/Hotels
>>     Yeah let’s go ahead and add them...
>>    - Add Choice participants? Good question. I did get info back from
>>    Metro on what products the choice participants are buying, I can’t remember
>>    whether I shared that with you all. Anyway, it’s attached. It’s actually a
>>    little hard to interpret (I got a tutorial from a Metro staffer) so I can
>>    try to explain by phone or in person if someone wants to dig throughthat.
>>    Maybe it makes sense to look through that info and add businesses
>>    selectively as we feel like we have a grasp on their programs.
>>    - Add column for Commute Seattle participants?
>>    https://seattletransitpasses-research.pbworks.com/w/page/133438167/Commute%20Seattle%20List%20of%20Passport%20Participants I
>>    don’t think we need to do this, because this info is most likely
>>    duplicative with what we learned from Metro about passport participants.
>>    The Commute Seattle list doesn’t tell us how much of a subsidy they
>>    provide, so it’s not going to add much.
>>
>>
>>    - Citations, descriptions of benefits, etc. for companies that need it
>>
>> There's still a lot we're not 100% sure about for employer benefits, but
>> we can do the best we have with what we've got, and make changes as we get
>> new information.
>>
>> My thinking was, we can use the "master employer list" to get as much
>> information about the companies we're interested in. When we're satisfied
>> that a row is finished and ready for publication, check the checkbox in the
>> "__publish" column. Then, when we export this data to the website, we can
>> only get the rows where "__publish" is checked. This hopefully will ensure
>> that someone manually reviewed and verified all the data for an employer
>> before it gets published, and that unfinished rows won't be accidentally
>> exported.
>>
>> Is this helpful? Am I just spinning my wheels in the mud?
>>
>>
>>
>> On Thu, Aug 22, 2019 at 7:06 PM Stephen DeSanto <rachidian at gmail.com>
>> wrote:
>>
>>> FYI, added most of you as editors on the spreadsheet I'm working on, in
>>> case anyone has time for tedious data tasks (or a quick way to do tedious
>>> data tasks). I'm currently adding in data from the TRU survey, from
>>> respondents whose employers offer transit benefits. Eventually, we'll need
>>> these tagged with industry and fix the neighborhoods data? And add in any
>>> other company data we have from the other research spreadsheets on the
>>> wiki? And eventually some subset of this data ends up on the website?
>>>
>>> On Tue, Aug 20, 2019 at 3:03 PM Tom Chartrand <tmchartrand at gmail.com>
>>> wrote:
>>>
>>>> Oh you're right, sorry for the confusion everyone! was just fairly
>>>> hidden in the view i looked at. Column S!
>>>>
>>>> On Tue, Aug 20, 2019 at 3:00 PM Katie Wilson <katie at transitriders.org>
>>>> wrote:
>>>>
>>>>> I think the spreadsheet with PII removed still does include the
>>>>> Employer column, no?
>>>>>
>>>>> Sorry I’m being slow to respond to all this good stuff, I am still
>>>>> digging myself out from being away last week and I’m at an all-day thing
>>>>> today… but I should have time to pay more attention before the end of the
>>>>> week!
>>>>>
>>>>> On Aug 19, 2019, at 6:26 PM, Stephen DeSanto <rachidian at gmail.com>
>>>>> wrote:
>>>>>
>>>>> I have time to go through the survey data and find the reported
>>>>> transit benefits per employer, though I'll need the data set that contains
>>>>> that data. :)
>>>>>
>>>>> Otherwise, I am going to be trying to match CTR neighborhoods to the
>>>>> employers already in our spreadsheet, as well as adding any employers
>>>>> mentioned in other sources/sheets on our wiki.
>>>>>
>>>>> On Mon, Aug 19, 2019 at 6:02 PM Tom Chartrand <tmchartrand at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> This is looking great, Stephen!
>>>>>> I had put myself down to organize the survey data with respect to
>>>>>> employers for this, but I just realized that info was removed as PII (of
>>>>>> course)! So either Mike will need to take that on (I think Mike did the
>>>>>> original PII removal) or we'll need to figure out an appropriate way of
>>>>>> sharing that.
>>>>>> I'm feeling pretty swamped myself lately, so if you (Stephen) were
>>>>>> down to help him with the task that could be great. I can certainly still
>>>>>> take on some of it if needed though, once we get this sorted out.
>>>>>> Katie, maybe you could help coordinate this and make sure Mike sees
>>>>>> this sooner rather than later?
>>>>>>
>>>>>> Also, do let me know if you have any more specific spots in the
>>>>>> report where some backup from the PSRC dataset could be useful!
>>>>>>
>>>>>> On Sun, Aug 18, 2019 at 3:59 PM Stephen DeSanto <rachidian at gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I've added the list of industry categories to the Google Sheet, so
>>>>>>> that should help validate the data we add there, though it's going to
>>>>>>> likely be a manual task to fill in industries for all the employers.
>>>>>>>
>>>>>>> I've also added a "citation" column, which can be the public
>>>>>>> representation of where we got the data to make our claim. We can fuss with
>>>>>>> the wording later.
>>>>>>>
>>>>>>> I should have time this week to go through our survey data and other
>>>>>>> wiki tables to add or modify employers in the Google Sheet. Agree that
>>>>>>> it'll be good to have solid information on our primary targets and
>>>>>>> champions.
>>>>>>>
>>>>>>> On Tue, Aug 13, 2019 at 10:48 PM Harry Maher <harryb.maher at gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Just a quick update with regard to qualitative data analysis: I
>>>>>>>> made a "Commute Survey Qualitative Data Analysis" folder on pbworks and put
>>>>>>>> a doc with some quotes in it for the report. I tried to pull out the main
>>>>>>>> relevant themes that I noticed discussed in the two qualitative questions
>>>>>>>> currently in the file with a couple of quote options for each
>>>>>>>> theme/category of response to the question.
>>>>>>>>
>>>>>>>> -Harry
>>>>>>>>
>>>>>>>> On Sun, Aug 11, 2019 at 12:54 PM Tom Chartrand <
>>>>>>>> tmchartrand at gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Regarding where to have this discussion - I'm just gonna continue
>>>>>>>>> the email chain cause I haven't followed where to put the discussion on the
>>>>>>>>> wiki, but someone feel free to steer it over there if we want to!
>>>>>>>>>
>>>>>>>>> A brief update regarding establishing a larger list of employers
>>>>>>>>> to include in the dataset: basic contact information for all seattle
>>>>>>>>> businesses, sorted by the North American Industry Classification System, is
>>>>>>>>> available at
>>>>>>>>> https://web6.seattle.gov/fas/slimbizsearch/ResultsPage.aspx?NAICList=Top100,
>>>>>>>>> but it's a huge list of course, with no info on number of employees or
>>>>>>>>> revenue to filter out the smaller ones. Still, I did send off an email
>>>>>>>>> about getting a copy of the database just for purposes of cross-referencing
>>>>>>>>> names and such.
>>>>>>>>> On 8/10/19 6:42 PM, Katie Wilson wrote:
>>>>>>>>>
>>>>>>>>> For “neighborhood” I think it makes sense to use the “CTR Network
>>>>>>>>> Areas” as defined here
>>>>>>>>> <https://www.seattle.gov/transportation/projects-and-programs/programs/transportation-options-program/commute-trip-reduction-program/draft-2019-2023-networks-and-targets>
>>>>>>>>> .
>>>>>>>>>
>>>>>>>>> For “industry” I think it makes sense to use the “Employment
>>>>>>>>> Sector” categories listed on Page 12 of this CTR strategic plan.
>>>>>>>>> <https://www.seattle.gov/Documents/Departments/SDOT/TransportationOptionsProgram/CTR_Draft_Strategic_Plan_Jan2019.pdf>
>>>>>>>>>
>>>>>>>>> On the ratings, I think it does make sense to lump "piggy bank"
>>>>>>>>> and "brown tortoise" in the same rating (0), and then add a tortoise badge
>>>>>>>>> for employers that aren’t even doing the pre-tax thing.
>>>>>>>>>
>>>>>>>>> Another simplification option to consider would be to lump
>>>>>>>>> together 3 and 4 leaves. But let’s leave them separate for now and
>>>>>>>>> depending on how things shake out we can easily combine them later.
>>>>>>>>>
>>>>>>>>> We don’t have any major sources of data on what benefits employers
>>>>>>>>> provide other than:
>>>>>>>>> — Metro public disclosure request spreadsheet
>>>>>>>>> <https://seattletransitpasses-research.pbworks.com/w/page/133438080/First%20Public%20Records%20Request>
>>>>>>>>> — Our commute survey
>>>>>>>>> — Info gleaned online from company websites, asking around,
>>>>>>>>> glassdoor etc (what I’ve found I’ve added to the relevant tables
>>>>>>>>> in the wiki
>>>>>>>>> <https://seattletransitpasses-research.pbworks.com/w/page/132177123/Employers>,
>>>>>>>>> on CTR employers and “potential poster children” and “likely target
>>>>>>>>> assessment” and “hotels”)
>>>>>>>>>
>>>>>>>>> Maybe it makes sense to have another string indicating sufficient
>>>>>>>>> certainty — when we have two sources, or one very reliable source, we enter
>>>>>>>>> an X or whatever, and that gives us the green light to display that data.
>>>>>>>>> Also it may not make sense to put a lot of work into categorizing employers
>>>>>>>>> into Network Area and Employment Sector until we have reliable data on what
>>>>>>>>> benefits they’re offering.
>>>>>>>>>
>>>>>>>>> Speaking of Seattle Coffee Works, I spoke with their HR person a
>>>>>>>>> few months ago and actually employees have to pay $20/month (pre-tax $) if
>>>>>>>>> they want an ORCA card. Still a great deal but not 100% subsidy as reported
>>>>>>>>> in the Metro data— which, I then learned, is self-reported by the company.
>>>>>>>>> Metro only knows that all those companies are signed up for the Passport
>>>>>>>>> program. I noted the real situation on this page
>>>>>>>>> <https://seattletransitpasses-research.pbworks.com/w/page/133439169/Potential%20Poster%20Children>.
>>>>>>>>> Anyway, the point is we should probably crosscheck the Metro data as much
>>>>>>>>> as we can with our survey or other sources of information.
>>>>>>>>>
>>>>>>>>> (Also speaking of Seattle Coffee Works they have locations in Capitol
>>>>>>>>> Hill & Cascade too
>>>>>>>>> <https://www.seattlecoffeeworks.com/our-cafes.aspx>. From talking
>>>>>>>>> with the HR person I’m pretty sure all are include in their passport
>>>>>>>>> program, and the employees swap around a lot from location to location.
>>>>>>>>> They probably use the Ballard location as home base for transit pass
>>>>>>>>> purposes since that’s the least expensive zone.)
>>>>>>>>>
>>>>>>>>> One project would be to come up with a list of employers that have
>>>>>>>>> name recognition (or that we are interested in for some other reason) and
>>>>>>>>> put a little work into attaining sufficient certainty. If we posted the
>>>>>>>>> list to a page and put a call out on social media and email I bet we’d get
>>>>>>>>> some answers.
>>>>>>>>>
>>>>>>>>> On Aug 8, 2019, at 5:26 PM, Stephen DeSanto <rachidian at gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> Hi everyone,
>>>>>>>>>
>>>>>>>>> I've taken a first pass at the data schema for showing employer
>>>>>>>>> transit benefits in our upcoming web app. In this draft, each employer
>>>>>>>>> record is represented as follows:
>>>>>>>>>
>>>>>>>>> {
>>>>>>>>>     "employer": string,
>>>>>>>>>     "industry": [string],
>>>>>>>>>     "neighborhood": [string],
>>>>>>>>>     "alias": [string],
>>>>>>>>>     "rating": int,
>>>>>>>>>     "description": string
>>>>>>>>>     "badges": [string]
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>> *Employer* is a plain text string.
>>>>>>>>> *Industry* is a list of strings (or a single string, if we want
>>>>>>>>> to limit one employer = one industry).
>>>>>>>>> *Neighborhood* is treated similarly to industry
>>>>>>>>> *Alias* is a list of other names for the same company. For
>>>>>>>>> example,
>>>>>>>>> *Rating* is a numerical scale that represents the "worker's
>>>>>>>>> monthly cost of an unlimited transit pass". The scale provided during the
>>>>>>>>> meeting went from "4 leaves" to "brown tortoise"; aligning to the leaves,
>>>>>>>>> that gives us a scale of [-1, 0, 1, 2, 3, 4]. We could adjust this up to
>>>>>>>>> 0-5, or lump "piggy bank" and "brown tortoise" in the same rating.
>>>>>>>>> *Description* is a string that describes the employer's transit
>>>>>>>>> benefits, i.e. why they got the rating they did.
>>>>>>>>> *Badges* is a list of strings that represent any additional
>>>>>>>>> categories we want to assign to a company (e.g. "industry leader",
>>>>>>>>> "polluter").
>>>>>>>>>
>>>>>>>>> We can make changes to this schema if it makes it easier to work
>>>>>>>>> with our underlying data visualization platform (e.g. Tableau?
>>>>>>>>> DataTables?), but hopefully this is a suitable starting place.
>>>>>>>>>
>>>>>>>>> As an example, take a hypothetical record for Seattle Coffee Works.
>>>>>>>>>
>>>>>>>>> {
>>>>>>>>>     "employer": "Seattle Coffee Works",
>>>>>>>>>     "industry": ["restaurant"],
>>>>>>>>>     "neighborhood": ["cbd", "ballard"],
>>>>>>>>>     "alias": ["Ballard Coffee Works"],
>>>>>>>>>     "rating": 4,
>>>>>>>>>     "description": "Provides 100% ORCA Passport subsidy."
>>>>>>>>>     "badges": ["leader"]
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>> *Where Our Data Lives (For Now)*
>>>>>>>>>
>>>>>>>>> I've also taken a rough chop at getting started with the data.
>>>>>>>>> Here, I've just taken the raw list of ORCA Business Passport employers and
>>>>>>>>> assigned a score based on their subsidy percentage, as an example:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> https://docs.google.com/spreadsheets/d/1HmOcG7hJLD1G0unCMPcsDnXr4RIA_PMKEE5ne-hhQR8/edit?usp=sharing
>>>>>>>>>
>>>>>>>>> The spreadsheet contains columns for each item of the employer
>>>>>>>>> record, as well as some additional columns to record the raw data we have
>>>>>>>>> on file for that employer, so we can use that data to automatically or
>>>>>>>>> manually determine an employer's rating.
>>>>>>>>>
>>>>>>>>> If we have data from other sources not listed (e.g. survey data,
>>>>>>>>> City of Seattle data), the "source_" columns can be renamed or added to
>>>>>>>>> represent that source's data. For example, if I want to add data from the
>>>>>>>>> TRU survey, I might rename "__source_b" to "__TRU Survey", then include
>>>>>>>>> results from that survey in that column for each company. (The columns
>>>>>>>>> beginning with two underscores are ones I don't expect to be publicly
>>>>>>>>> available.)
>>>>>>>>>
>>>>>>>>> PBworks feels really inadequate for editing large data sets, and I
>>>>>>>>> don't know where else to put it, so it's living in Google Sheets for now.
>>>>>>>>> Set to read-only with the link, for now, but please request editing
>>>>>>>>> permissions so you can add stuff to the sheet.
>>>>>>>>>
>>>>>>>>> Currently, my expectation is that the spreadsheet will be
>>>>>>>>> hand-edited in Google Sheets, and then when we're ready to put live data in
>>>>>>>>> the web app, we can export the sheet to a flat file, which we can then
>>>>>>>>> import into a format appropriate for the website (big ol' JSON file,
>>>>>>>>> database, whatever). Manual process, but probably fine for a project of
>>>>>>>>> this scale; I'm open to alternatives.
>>>>>>>>>
>>>>>>>>> *Things To Do Next*
>>>>>>>>>
>>>>>>>>> Aside from the ORCA Passport data and the data we collected
>>>>>>>>> through TRU survey / legwork (on PBworks), do we have any other data
>>>>>>>>> sources that would provide context for a score?
>>>>>>>>>
>>>>>>>>> For the data sources we have, we'll have to start filling out the
>>>>>>>>> rest of the spreadsheet, I guess?
>>>>>>>>>
>>>>>>>>> Also, we will need to determine:
>>>>>>>>> a) master list of "industries" we want to support, and
>>>>>>>>> b) "industry" field(s) for each employer
>>>>>>>>> c) "neighborhood" field(s) for each employer we don't have one for
>>>>>>>>> (or being more precise than what I have now)
>>>>>>>>> d) which companies get tagged with which badges
>>>>>>>>>
>>>>>>>>> Hope that helps.
>>>>>>>>>
>>>>>>>>> In solidarity,
>>>>>>>>>
>>>>>>>>> Stephen
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>
>>>>>
>>
>
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