31:T15f2,md`# July 23rd update The most up to date data that we currently have has data from the original COVID-19 deaths to June 11th. There is a lot of data inaccuracy and ambiguity that still needs to be cleaned up. This is particularly the case for the fields, "death_location_1", "death_location", "notifying_agency", "death_street", "incident_street", and "incident_location", which in many ways are the same idea, though each agency inputs data differently, and certain circumstances require the data to be input a certain way. The death_location and death_location_1 fields are the most important in the immediate, because it's the most consistently populated fields that gives easy to work with aggregate info. These two fields attempt to convey the same information and there's often conflict between the two, including missing information in one or both fields. A lot of this can be supplemented with its surrounding information. These two fields are can be reduced into a single field, but death_location_1's convention will be used, because it uses generic terms such as "hospital" or "nursing home", while the death_location field may use "LOYOLA UNIVERSITY MEDICAL CENTER - INTENSIVE CARE UNIT". To illustrate the discrepancies, these are the counts of death_location and death_location_1 where death_location_1 is "NULL" (43 rows): |count | death_location | death_location_1 | |-------|--------------------------------------------------------|------------------| | 577 | NULL | NULL| | 19 | HOSPITAL ICU | NULL| | 13 | INPATIENT | NULL| | 10 | NH | NULL| | 7 | ICU | NULL| | 5 | HOSPITAL ER | NULL| | 3 | HOSPITAL INPATIENT | NULL| | 3 | HOSPITAL ICU | NULL| | 2 | HOSPICE CARE | NULL| death_location (329 rows), bottom section are examples of hand-typed locations: | count | death_location | |-------|------------------------------------------------------------------------| | 1904 | NULL| | 517 | HOSPITAL| | 369 | HOSPITAL ICU| | 273 | INPATIENT| | 121 | NURSING HOME| | 74 | HOSPITAL | | 55 | RESIDENCE| | 46 | NH| | 38 | ICU| | 32 | HOSPICE| | 24 | HOSPITAL ER| |...|...| | 7 | DECEDENT'S RESIDENCE| | 7 | HOSPICE UNIT| | 6 | MERCY ICU| | 6 | UNIVERSITY OF CHICAGO MEDICAL CENTER - INTENSIVE CARE UNIT| | 6 | LITTLE COMPANY OF MARY HOSPITAL - INTENSIVE CARE UNIT| death_location_1 (26 rows in total): | count | death_location_1 | |-------|--------------------------| | 2684 | HOSPITAL| | 675 | NULL| | 377 | NURSING HOME| | 178 | HOSPICE| | 80 | HOSPITAL-ER| | 63 | RESIDENCE| | 54 | SCENE| | 19 | BEDROOM| | 14 | APARTMENT| | 5 | ASSISTED LIVING FACILITY| | 5 | REHAB FACILITY| | 4 | HOSPITAL-DOA| This location inconsistency is particularly evident with the homeless and where the notifying agency is a police agency. Where the notifying agency is the police, the death location may be a hospital, or it may be the home of the decedent. With the homeless, the "incident location" may be a food bank. ### The row-by-row validation is done through these questions: 1. Is the death address the address of a hospital or nursing home? 2. Does the notifying agency match the death address? If not, why not? 3. Is the incident address the address of a hospital? Is the death address different? #1 and #2 are being done by checking the distance of the death against addresses of hospitals. This seems to work for about 20% of the cases, but a large majority seem to have death addresses that are not hospitals. This requires further investigation, and is difficult to do without completing the row-by-row validation. #3 should help us identify if a transfer happened, but it's ultimately inconsistent. ## Inmate deaths This will need to be updated soon. Between Josh and I, we are finding that there are very few deaths in jails and IDOC, seemingly because of the ages in jail. Reporting around these deaths will likely need to be done on a death-by-death basis. Inmate deaths are identified through FOIA requests that list deaths as well as name comparisons against the medical examiner's data. We found one inmate death as being listed as "accidental" whose cause of death strongly hinted at an overdose. I will pull up more information on this, but the circumstances of this seem strange. ## Investigative Questions 1. What are the deaths where the incident location and death location are identical? 2. What deaths are happening at homeless shelters and food banks? 3. To what extent are police agencies reporting deaths, and if the death is a hospital, why is the hospital not the notifying agency? 4. What is the closest hospital to the death? Is that where they went? 5. Are psychiatric hospitals affected? 6. Does the quality of hospital (through past ratings) have an effect on the number of deaths? 7. What is the average/median distance from incident location to death? 8. Can we figure out where transfers happened by checking if incident_address is not ? 9. Can we determine whether teachers have died from COVID-19? 10. Why is FOIA so hard for this data?`32:T689,md`## Summary of data 1. This data is almost a 1:1 extension of the records found on https://datacatalog.cookcountyil.gov/Public-Safety/Medical-Examiner-Case-Archive/cjeq-bs86/data. 2. The extra columns from this data are: DECEDENT_FIRST_NAME, DECEDENT_LAST_NAME, NOTIFYING_AGENCY, DEATH_LOCATION_1, POST, DEATH_LOCATION, DEATH_LOCATION_1, 3. The medical examiner's open data page uses "LATINO", and the FOIA data uses "Hispanic Y/N". 4. The total #s of death in south side weekly's neighborhood tracker from 04/26 matches #s in our new data: 784. https://web.archive.org/web/20200426115932/https://covid19neighborhoods.southsideweekly.com/ 5. Between ours and SSW's data, the neighborhood counts differ. 6. Data only includes deaths and cannot be used to infer much without additional data. 7. The data spans across all of Cook County medical centers. 8. The data includes records from police departments, and largely consists of on-site recordings. 9. The "hispanic" column is essentially useless, and there is no latino category under the race column. 10. Most columns referencing addresses is likely their home address, though often times the data is the health center they died at. 11. Data is entered by IDPH based on "POST" category, though most of the data isn't transcribed very well. 12. Because of #8 and #7, it's exceptionally difficult to tell which hospital someone died at if there are two hospitals with the same dataset. Very common. 13. The data lags at the very end, heavily suggesting that there are delays in reporting. 14. There are four categories of cause of death and all are overall unreliable as different agencies report wildly differently. `33:T586,md`### Original File Data Dictionary |Fieldname|Description| |----|----| |CASENUMBER|Case number from Medical Examiner's Office in the format of, ME2020-02728. | |DEATH_DATE|Date of Death| |PRIMARY_CAUSE|Primary Cause of death. | |PRIMARYCAUSE_LINEA|Primary cause written on report, first line.| |PRIMARYCAUSE_LINEB|Primary cause written on report, second line.| |SECONDARY_CAUSE|Reported secondary cause.| |DECEDENT_FIRST_NAME|First name of decedent.| |DECEDENT_MIDDLE_NAME|Middle initial of decedent.| |DECEDENT_LAST_NAME|Last name of decedent.| |DECEDENT_AGE|Age of decedent| |GENDER|Gender of decedent. 1 Unknown, 841 Male, 585 Female| |RACE|Race of decedent 646 Black, 36 Other, 677 White, 52 Asian, 15 Unknown.| |Hispanic Y/N|Whether decedent is latino. This field is "Latino" in the medical examiner's data portal.| |INCIDENT_STREET|Street which the agency reported the decedent is from, or where the death occured.| |INCIDENT_CITY|City which the agency reported the decedent is from, or where the death occured.| |INCIDENT_ZIP|ZIP which the agency reported the decedent is from, or where the death occured.| |INCIDENT_COUNTY|County which the agency reported the decedent is from, or where the death occured.| |INCIDENT_STATE|State which the agency reported the decedent is from, or where the death occured.| |NOTIFYING AGENCY|Agency that notified IDPH about death.| |POST|At what point data was entered.| `34:T11df,table([ { "Column Name": "death_date", "Empty #": [0], "Distinct #": [1390], "Top Three": "2020-04-22 08:31:00 (3), 2020-04-11 07:45:00 (2), 2020-04-16 09:50:00 (2)" }, { "Column Name": "secondary_cause", "Empty #": [133], "Distinct #": [822], "Top Three": "None (133), Hypertension (48), Diabetes Mellitus (38)" }, { "Column Name": "decedent_first_name", "Empty #": [0], "Distinct #": [793], "Top Three": "John (25), Robert (23), James (23)" }, { "Column Name": "incident_zip", "Empty #": [29], "Distinct #": [178], "Top Three": "60649 (52), 60620 (37), 60623 (35)" }, { "Column Name": "primary_cause", "Empty #": [0], "Distinct #": [94], "Top Three": "Pneumonia (418), Novel Corona (Covid-19) Virus Infection (178), Acute Respiratory Failure (167)" }, { "Column Name": "decedent_last_name", "Empty #": [0], "Distinct #": [1078], "Top Three": "Johnson (24), Williams (19), Jones (14)" }, { "Column Name": "decedent_age", "Empty #": [0], "Distinct #": [81], "Top Three": "77 (53), 73 (46), 72 (45)" }, { "Column Name": "decedent_middle_name", "Empty #": [1224], "Distinct #": [69], "Top Three": "None (1224), A. (17), L. (17)" }, { "Column Name": "notifying_agency", "Empty #": [0], "Distinct #": [110], "Top Three": "ADVOCATE CHRIST MEDICAL CENTER (88), CHICAGO POLICE DEPARTMENT (75), GLENBROOK HOSPITAL (66)" }, { "Column Name": "incident_street", "Empty #": [6], "Distinct #": [1314], "Top Three": "None (6), 1511 Greenwood Rd (6), 2451 West Touhy Avenue (6)" }, { "Column Name": "gender", "Empty #": [2], "Distinct #": [3], "Top Three": "Male (841), Female (585), None (2)" }, { "Column Name": "race", "Empty #": [3], "Distinct #": [5], "Top Three": "White (677), Black (646), Asian (52)" }, { "Column Name": "primarycause_linea", "Empty #": [297], "Distinct #": [17], "Top Three": "Novel Coronavirus (Covid-19) Viral Infection (947), None (297), pneumonia (167)" }, { "Column Name": "primarycause_lineb", "Empty #": [1270], "Distinct #": [6], "Top Three": "None (1270), Novel Coronavirus (Covid-19) Viral Infection (154), Gram Positive Coccus Bacterial Infection (1)" }, { "Column Name": "casenumber", "Empty #": [0], "Distinct #": [1429], "Top Three": "Me2020-02877 (1), Me2020-03422 (1), Me2020-03183 (1)" }, { "Column Name": "incident_city", "Empty #": [30], "Distinct #": [125], "Top Three": "Chicago (784), Oak Lawn (32), Glenview (31)" }, { "Column Name": "hispanic", "Empty #": [0], "Distinct #": [2], "Top Three": "False (1244), True (185)" }, { "Column Name": "combined_address", "Empty #": [0], "Distinct #": [1337], "Top Three": "1511 Greenwood Rd, Glenview, Il 60026 (6), 2451 W Touhy Ave, Chicago, Il 60645 (5), 2451 West Touhy Avenue, Chicago, Il 60645 (5)" }, { "Column Name": "incident_county", "Empty #": [43], "Distinct #": [7], "Top Three": "Cook County (1325), None (43), Lake County (24)" }, { "Column Name": "incident_state", "Empty #": [52], "Distinct #": [3], "Top Three": "Il (1375), None (52), Ga (1)" }, { "Column Name": "post", "Empty #": [0], "Distinct #": [3], "Top Three": "Records Review (1375), External (52), Autopsy (2)" }, { "Column Name": "death_location_1", "Empty #": [267], "Distinct #": [17], "Top Three": "Hospital (995), None (267), Nursing Home (37)" }, { "Column Name": "death_date_day", "Empty #": [0], "Distinct #": [7], "Top Three": "Wednesday (234), Friday (221), Sunday (220)" }, { "Column Name": "ward2015", "Empty #": [654], "Distinct #": [50], "Top Three": "None (654), 50 (44), 7 (41)" }, { "Column Name": "census_tract", "Empty #": [96], "Distinct #": [622], "Top Three": "None (96), 203.02 (21), 8230.02 (18)" }, { "Column Name": "neighborhood", "Empty #": [654], "Distinct #": [75], "Top Three": "None (654), SOUTH SHORE (51), WEST RIDGE (47)" }, { "Column Name": "latitude", "Empty #": [12], "Distinct #": [1061], "Top Three": "41.70142 (15), 42.082258 (14), 42.011607 (14)" }, { "Column Name": "longitude", "Empty #": [12], "Distinct #": [1062], "Top Three": "-87.77922 (15), -87.692132 (14), -87.838715 (14)" } ])35:Tb37,md`## Data to request: 1. Hospital bed counts from EMResource. Should give an indication of how many beds are available. Propublica had bad luck requesting hospital bed counts: https://www.propublica.org/article/how-we-used-foia-to-track-ventilator-and-hospital-bed-availability-in-illinois. Useful info: https://www.dph.illinois.gov/sites/default/files/resources/Daily-Availability-Update-Procedure-100316.pdf. Mid term research. 2. Amount of treatment drugs available per day, per hospital. This will answer whether treatment was available at Little Hospital of Mary. Mid term research. 3. All available data from CDC, DHS, IDPH, Cook County, CDPH. Requires a privacy waiver. More Below. Long term research. 4. All records from Little Company of Mary. Also requires a privacy waiver. Short Term research. ## Privacy Waiver When we request data related to Victoria Manning, we need to have a privacy waiver signed by the family so that the data can be released to us for use in reporting. I drafted the below waiver based on a privacy waiver created by Wallace Hilke at Community Activism Law Alliance for the Gang Database. The FOIA requests themselves were sent by CALA. While this privacy waiver might work, there is risk that it won't, as the circumstances here are significantly different: the person whose information is being requested is dead, privacy laws are more nuanced with health laws, and we would send the requests ourselves instead of a law firm. At the very least, we should consult with a lawyer to either sign off or write a privacy waiver that we can send for both FOIA and hospital information-release. At best, the lawyer should submit the requests, on behalf of the family and have attached identification. Going back to the family on a failed privacy waiver is not very reasonable to the family, so we should make sure we do this properly from the beginning. ### Privacy Waiver Draft Authorization and Consent to Disclosure of Information and Record _I, [name], authorize the release of the personal information of Victoria Manning contained by any public agency, municipal, state, federal database, or health organization to, The Chicago Reporter and The TRiiBE._ _In signing this document I am waiving the privacy concerns in relation to the disclosure of the information of Victoria Manning to the above-named entity and their agents and representatives that would be exempt for release under (but not limited to) the Health Insurance Portability and Accountability Act, The Communicable Disease Report Act, The Vital Records Act. My signature below authorizes the release of the requested information by the above-named entities, their agents and representatives._ _This authorization remains in effect for the period of one year from the date of this request._ _[signature] [date] [phone number]_ _Signature Date Phone Number_ `36:T757,md`### Hispanic Column The hispanic column is almost positively filled with errors and does not accurately reflect whether somebody is latino or not. Sorting by white-only and filtering out "YES" for hispanic results in many latino last names. It's hard to say what's going on exactly, but this casts an enormous cloud of doubt over the race columns, which matches Maria Zamudios's experiences: https://www.wbez.org/stories/how-many-chicago-latinos-have-died-from-covid-19-there-are-no-up-to-date-numbers/8835a044-05bd-4503-985a-3bb11cf45df7 What's even stranger is that the hispanic column is falls off completely in the last days of data. This doesn't appear to be directly related to a complete lack of reporting by agencies, and I'm led to believe that the column is filled only later. Additionally, deaths reported by CPD don't have any deaths marked as hispanic for the last 5 days of the data. They only had one or two for all previous days, but the sudden lack is interesting. Could be worth looking into this drop. Cook County Medical Examiner's Office explains this: > _The Medical Examiner’s Office relies on data provided by reporting agencies such as hospitals and police departments to provide demographic information about each decedent, including age, gender, race, address and other identifying data. Next of kin provides data related to ethnicity to the funeral home when making final arrangements for the deceased. The Office then adds that information to its database._ > _During the COVID-19 pandemic the Cook County Medical Examiner’s Office has experienced a significant delay in families making final arrangements for decedents under the Office’s jurisdiction. Therefore, this dashboard may not have updated ethnicity information for decedents until several weeks after their death._ > https://datacatalog.cookcountyil.gov/stories/s/ttk4-trbu `37:T79f,chart = { const svg = d3 .select(DOM.svg(width, height)) .style("background", "#fff") .style("width", "100%") .style("height", "auto"); const { nodes, links } = sankey({ nodes: race_data.nodes.map(d => Object.assign({}, d)), links: race_data.links.map(d => Object.assign({}, d)) }); svg .append("g") .selectAll("rect") .data(nodes) .join("rect") .attr("x", d => d.x0 + 1) .attr("y", d => d.y0) .attr("height", d => d.y1 - d.y0) .attr("width", d => d.x1 - d.x0 - 2) .attr("fill", d => { let c; for (const link of d.sourceLinks) { if (c === undefined) c = link.color; else if (c !== link.color) c = null; } if (c === undefined) for (const link of d.targetLinks) { if (c === undefined) c = link.color; else if (c !== link.color) c = null; } return (d3.color(c) || d3.color(color)).darker(0.5); }) .append("title") .text(d => `${d.name}\n${d.value.toLocaleString()}`); const link = svg .append("g") .attr("fill", "none") .selectAll("g") .data(links) .join("g") .attr("stroke", d => d3.color(d.color) || color) .style("mix-blend-mode", "multiply"); link .append("path") .attr("d", d3.sankeyLinkHorizontal()) .attr("stroke-width", d => Math.max(1, d.width)); link .append("title") .text( d => `${d.source.name} → ${d.target.name}\n${d.value.toLocaleString()}` ); svg .append("g") .style("font", "10px sans-serif") .selectAll("text") .data(nodes) .join("text") .attr("x", d => (d.x0 < width / 2 ? d.x1 + 6 : d.x0 - 6)) .attr("y", d => (d.y1 + d.y0) / 2) .attr("dy", "0.35em") .attr("text-anchor", d => (d.x0 < width / 2 ? "start" : "end")) .text(d => d.name) .append("tspan") .attr("fill-opacity", 0.7) .text(d => ` ${d.value.toLocaleString()}`); return svg.node(); }38:T7a7,cpd_chart = { const svg = d3.select(DOM.svg(width, height)) .style("background", "#fff") .style("width", "100%") .style("height", "auto"); const {nodes, links} = sankey({ nodes: cpd_data.nodes.map(d => Object.assign({}, d)), links: cpd_data.links.map(d => Object.assign({}, d)) }); svg.append("g") .selectAll("rect") .data(nodes) .join("rect") .attr("x", d => d.x0 + 1) .attr("y", d => d.y0) .attr("height", d => d.y1 - d.y0) .attr("width", d => d.x1 - d.x0 - 2) .attr("fill", d => { let c; for (const link of d.sourceLinks) { if (c === undefined) c = link.color; else if (c !== link.color) c = null; } if (c === undefined) for (const link of d.targetLinks) { if (c === undefined) c = link.color; else if (c !== link.color) c = null; } return (d3.color(c) || d3.color(color)).darker(0.5); }) .append("title") .text(d => `${d.name}\n${d.value.toLocaleString()}`); const link = svg.append("g") .attr("fill", "none") .selectAll("g") .data(links) .join("g") .attr("stroke", d => d3.color(d.color) || color) .style("mix-blend-mode", "multiply"); link.append("path") .attr("d", d3.sankeyLinkHorizontal()) .attr("stroke-width", d => Math.max(1, d.width)); link.append("title") .text(d => `${d.source.name} → ${d.target.name}\n${d.value.toLocaleString()}`); svg.append("g") .style("font", "10px sans-serif") .selectAll("text") .data(nodes) .join("text") .attr("x", d => d.x0 < width / 2 ? d.x1 + 6 : d.x0 - 6) .attr("y", d => (d.y1 + d.y0) / 2) .attr("dy", "0.35em") .attr("text-anchor", d => d.x0 < width / 2 ? "start" : "end") .text(d => d.name) .append("tspan") .attr("fill-opacity", 0.7) .text(d => ` ${d.value.toLocaleString()}`); return svg.node(); }39:T6fa,md`### Obesity deaths | notif_count | notifying_agency | ratio | |-------------|--------------------------------------|--------------------| | 15 | CHICAGO POLICE DEPARTMENT | 0.2 | | 12 | RUSH UNIVERSITY MEDICAL CENTER | 0.27906976744186 | | 6 | WEISS MEMORIAL HOSPITAL | 0.222222222222222 | | 6 | INGALLS MEMORIAL HOSPITAL | 0.162162162162162 | | 6 | UNIVERSITY OF CHICAGO MEDICAL CENTER | 0.157894736842105 | | 5 | SAINT ANTHONY HOSPITAL | 0.333333333333333 | | 5 | NORTHWESTERN MEMORIAL HOSPITAL | 0.294117647058824 | | 5 | TRINITY HOSPITAL | 0.185185185185185 | | 5 | MACNEAL HOSPITAL | 0.185185185185185 | | 5 | SWEDISH HOSPITAL | 0.108695652173913 | | 4 | JOHN H. STROGER JR. HOSPITAL | 0.133333333333333 | | 4 | LOYOLA UNIVERSITY MEDICAL CENTER | 0.114285714285714 | | 4 | SOUTH SUBURBAN HOSPITAL | 0.111111111111111 | | 4 | ADVOCATE CHRIST MEDICAL CENTER | 0.0454545454545455 | | 3 | SOUTH SHORE HOSPITAL | 0.1875 | | 3 | SAINT ALEXIUS MEDICAL CENTER | 0.1875 | | 3 | GOTTLIEB HOSPITAL | 0.176470588235294 | | 3 | SAINT FRANCIS HOSPITAL | 0.0652173913043478 | | 3 | LITTLE COMPANY OF MARY | 0.0588235294117647 | | 3 | GLENBROOK HOSPITAL | 0.0454545454545455 | select sq.notif_count, nq.notifying_agency, notif_count::float/nq.count::float as ratio from (select count(*) as notif_count, notifying_agency from corona_deaths where lower(secondary_cause) like '%obesity%' or lower(primary_cause) like '%obesity%' or lower(primarycause_linea) like '%obesity%' or lower(primarycause_lineb) like '%obesity%' group by notifying_agency order by notif_count desc) sq, (select count(*), notifying_agency from corona_deaths group by notifying_agency) nq where nq.notifying_agency = sq.notifying_agency and sq.notif_count >= 3order by sq.notif_count desc, ratio desc;`3a:T4e8,viewof cpd_source = { const cpd_textarea = html`

CHICAGO POLICE DEPARTMENT,Null,15
CHICAGO POLICE DEPARTMENT,Residence,12
CHICAGO POLICE DEPARTMENT,Hospital-Er,9
CHICAGO POLICE DEPARTMENT,Apartment,7
CHICAGO POLICE DEPARTMENT,Hospital,7
CHICAGO POLICE DEPARTMENT,Bedroom,6
CHICAGO POLICE DEPARTMENT,Nursing Home,6
CHICAGO POLICE DEPARTMENT,Scene,6
CHICAGO POLICE DEPARTMENT,Kitchen,2
CHICAGO POLICE DEPARTMENT,Emergency Room,1
CHICAGO POLICE DEPARTMENT,Living Room,1
CHICAGO POLICE DEPARTMENT,Decedent'S Home,1
CHICAGO POLICE DEPARTMENT,Basement,1
CHICAGO POLICE DEPARTMENT,Halfway House,1`;
  cpd_textarea.style.display = "block";
  cpd_textarea.style.boxSizing = "border-box";
  cpd_textarea.style.width = "calc(100% + 28px)";
  cpd_textarea.style.font = "var(--mono_fonts)";
  cpd_textarea.style.minHeight = "60px";
  cpd_textarea.style.border = "none";
  cpd_textarea.style.padding = "4px 10px";
  cpd_textarea.style.margin = "0 -14px";
  cpd_textarea.style.background = "rgb(247,247,249)";
  cpd_textarea.style.tabSize = 2;
  cpd_textarea.oninput = () => {
    cpd_textarea.style.height = "initial";
    cpd_textarea.style.height = `${cpd_textarea.scrollHeight}px`;
  };
  yield cpd_textarea;
  cpd_textarea.oninput();
}3b:T5f9,html`<style>
@import url('https://fonts.googleapis.com/css2?family=Fira+Code:wght@400;500&display=swap');

.countries {
  font: 12px/1.4 "Fira Code", sans-serif;
  padding: 0 0 16px;
}
.countries__legend {
  padding: 0 0 4px;
}
.countries__legend,
.country {
  display: flex;
  white-space: nowrap;
}
.countries__legend-day span {
  position: absolute;
  visibility: hidden;
  border-left: 1px solid rgba(0,0,0,.2);
  padding: 0 4px 4px;
}
.countries__legend-day[data-day="1"] span,
.countries__legend-day[data-day="15"] span,
.countries__legend-day:first-child span {
  visibility: visible;
}
.countries__legend-day:nth-last-child(-n+10) span {
  visibility: hidden;
}
.countries__legend-day:nth-last-child(-n+1) span {
  visibility: visible;
  right: 0;
  border-left: none;
  border-right: 1px solid rgba(0,0,0,.2);
}
.country {
}
.country[data-country="World"] {
  border-bottom: 1px solid #fff;
}
.country:hover .country__title {
  color: #e15483;
}
.country__days,
.countries__legend-days {
  flex: 1;
  display: flex;
}
.country__day,
.countries__legend-day {
  flex: 1;
  height: stretch;
  position: relative;
}
.country__day:hover {
  box-shadow: inset 0 0 0 1px rgba(0,0,0,.3);
}
.country__day--max {
  box-shadow: inset 0 0 0 1px rgba(0,0,0,.3);
}
.country__sum {
  flex: 0 0 60px;
  text-align: right;
  padding-right: 8px;
}
.country__title {
  flex: 0 0 110px;
  overflow: hidden;
  white-space: nowrap;
  text-overflow: ellipsis;
  font-weight: 500;
}
.thinsp {
  width: .2em;
  display: inline-block;
}
</style>`3c:T43f,timeseries = countries
  .map(location => ({
    location,
    daysData: daysRange
      .map(date =>
        rawDataEcdc.find(
          d => d.location === location && d.date === dateFormat(date)
        )
      )
      .map(patchDays)
  }))
  .map(d => ({
    ...d,
    maxDeathsPerDay: d3.max(d.daysData, t => (t ? +t.new_deaths : 0)),
    totalDeaths: d3.max(d.daysData, t => (t ? +t.total_deaths : 0))
  }))
  .map(d => ({
    ...d,
    firstDay:
      d.maxDeathsPerDay == 0
        ? Infinity
        : d.daysData.findIndex(d => d && d.new_deaths > 0),
    daysSinceMax:
      d.maxDeathsPerDay == 0
        ? Infinity
        : d.daysData.length -
          d.daysData
            .map(t => (t ? +t.new_deaths : 0))
            .lastIndexOf(+d.maxDeathsPerDay),
    lastValue: d.daysData[d.daysData.length - 1]
      ? d.daysData[d.daysData.length - 1].new_deaths / d.maxDeathsPerDay
      : 0
  }))
  .map(d => ({
    ...d,
    lastValue3dAvg: avg(
      d.daysData
        .slice(d.daysData.length - 3)
        .map(t => (t ? t.new_deaths / d.maxDeathsPerDay : 0))
    )
  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2f:{"title":"Hospital Death Data Breakdown","cells":[{"id":280,"value":"md`# Hospital Death Data Breakdown`","pinned":false,"hidden":false,"mode":"ojs"},{"id":790,"value":"md`### Parsed/Cleaned and Original Files:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":909,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":786,"value":"FileAttachment(\"20-575.xlsx\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":920,"value":"md`## Below this line is old data. Ignore for now, will update soon.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":238,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":236,"value":"md`## Current state of data:\n\n1. Most addresses have been geocoded using the incident_* fields. 1337 distinct addresses in total.\n2. Addresses have been geocoded. All Chicago addresses includes ward, neighborhood. All addresses within Cook County include census tract.\n3. Many hospital addresses can't be geocoded because there are more than one of the same name. I've converted about 80% of reporting agencies to addresses, but the remaining 20% can't be done.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":874,"value":"md`### Data TODO\n\n1. Need to pair the \"incident location\" and \"death location\" to canonical locations. This is a fairly tedious process that requires going row by row by row. This can be done a little bit easier using the google places API.\n2. The death_location and death_location_1 columns are very similar columns, but also very inconsistent. These need to be reduced into a single column. \"death_location_1\" is generally a canonical description of the place of death (\"Home\", \"Hospital\"), while \"death_location\" is more descriptive, but also free-form, (\"SAINT FRANCIS HOSPITAL - INTENSIVE CARE UNIT\", \"HOURBOR HOUSE NH UNDER HOSPICE\")\n3. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":717,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":793,"value":"md`### Column Summaries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":771,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":559,"value":"md`## Victoria Manning\nVictoria Manning died at age 62 from complications of covid-19 at Little Company of Mary Hospital at 1:28 PM on 04/11/2020. \n\nhttps://chicago.suntimes.com/2020/4/21/21223884/victoria-manning-dies-beverly-coronavirus-covid-19-obituaries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":300,"value":"table([\n  {\n    \"case number\": \"ME2020-02517\",\n    \"death date\": \"04/11/20 01:28 PM\",\n    \"primary cause\": \"Bronchopneumonia\",\n    \"primary cause linea\": \"Novel corona (COVID-19) virus infection\",\n    \"primary cause lineb\": \"NULL\",\n    \"secondary cause\":\n      \"chronic obstructive pulmonary disease, hypertension, obesity\",\n    \"first name\": \"VICTORIA\",\n    \"middle name\": \"NULL\",\n    \"last name\": \"MANNING\",\n    age: \"62\",\n    gender: \"Female\",\n    race: \"Black\",\n    hispanic: \"NO\",\n    \"incident street\": \"9946 S. Fairfield\",\n    incident_city: \"Chicago\",\n    incident_zip: \"60655\",\n    incident_county: \"Cook County\",\n    incident_state: \"IL             \",\n    \"notifying agency\": \"LITTLE COMPANY OF MARY\",\n    post: \"Records Review\",\n    death_location_1: \"Hospital\"\n  }\n])","pinned":false,"hidden":false,"mode":"ojs"},{"id":862,"value":"md`### Further Research\nThe big question is asking why Victoria Manning was not able to be transferred between Little Company of Mary to Rush hospital. Rush hospital claims that they did not have enough beds, which is contrary to reporting by the Tribune.  Little Company of Mary is not known to be a good hospital.\n\nAs can be seen here, Little Company of Mary, indeed, has more deaths: (Little Company of Mary deaths # on top and Rush on bottom)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":857,"value":"FileAttachment(\"image@5.png\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":722,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":760,"value":"md`## Details on fields`","pinned":false,"hidden":false,"mode":"ojs"},{"id":763,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":803,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":817,"value":"md`### Hispanic column broken down by agency, showing delay in reporting:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":809,"value":"image = FileAttachment(\"image@1.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":824,"value":"md`### Hispanic and non-hispanic per day, showing dropoff at end.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":812,"value":"FileAttachment(\"image@6.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"md`## Location Death field`","pinned":false,"hidden":false,"mode":"ojs"},{"id":143,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":767,"value":"md`### Notifying Agency`","pinned":false,"hidden":false,"mode":"ojs"},{"id":676,"value":"md`The Notifying Agency of an individual doesn't necessarily match where they died. This can be seen with Chicago Police Department, which shows \"Hospital\" as the death location in many cases.\n\nAs can be seen with the following, the incident_street, notifying_agency, and death_location_1 can be wildly different:\n\n|incident_street           |           notifying_agency            | death_location_1 |\n|---------------------------|---------------------------------------|------------------|\n| 2700 california           | COOK COUNTY DEPARTMENT OF CORRECTIONS | Hospice|\n| 2700 s. california        | COOK COUNTY JAIL                      | Hospital|\n| 2600 s caifornia          | COOK COUNTY DEPARTMENT OF CORRECTIONS | Hospital|\n| 2700 s. california avenue | COOK COUNTY SHERIFF'S POLICE          | Hospital|\n| 2700 s. california        | COOK COUNTY DEPARTMENT OF CORRECTIONS | Hospital|\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":593,"value":"md`### County`","pinned":false,"hidden":false,"mode":"ojs"},{"id":508,"value":"md`County adds a bit of noise as well. As you can see, there are many addresses outside of Cook County:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":497,"value":"md`\n| count |   incident_county |\n|-------|-------|------|\n    | 2 | Mchenry County |\n | 1325 | Cook County |\n |   19 | Du Page County |\n |   14 | Will County |\n |    1 | Lake County/Indiana |\n |   24 | Lake County |\n |    1 | Cook |\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":885,"value":"$39","pinned":true,"hidden":false,"mode":"ojs"},{"id":884,"value":"","pinned":true,"hidden":false,"mode":"ojs"},{"id":333,"value":"md`## Deduping\n\n### Primary Cause\n_Pneumonia_: Pneumonia has several sub-classifications, and for the purpose of data simplification (a majority of mentions of pneumonia are simply labeled as \"pneumonia\", despite sub-classifications), the following have been collapsed into \"pneumonia\": bilateral pneumonia, multifocal, lobar pneumonia, viral pneumonia, bacterial pneumonia, bronchopneumonia\n\n_Primary Cause LineA and LineB_: Occasionally, Line A has more than one primary cause. In all but one record with more than one cause, there is no LineB. In these cases, the second primary cause from LineA is moved to LineB\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":739,"value":"md`## Visualisations of Locations`","pinned":false,"hidden":false,"mode":"ojs"},{"id":749,"value":"viewof filterCountries = select({\n  title: \"Filter Notifying Agencies\",\n  options: [\n    { label: \"All notifying agencies with confirmed deaths\", value: 0 },\n    { label: \"2+ deaths\", value: 2 },\n    { label: \"5+ deaths\", value: 5 },\n    { label: \"10+ deaths\", value: 10 },\n    { label: \"50+ deaths\", value: 50 }\n  ],\n  value: 5\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":753,"value":"viewof sortByMetric = select({\n  title: \"Sort by\",\n  options: [\n    { label: \"Days since max value\", value: \"daysSinceMax\" },\n    { label: \"Day of first value\", value: \"firstDay\" },\n    { label: \"Sum values\", value: \"totalDeaths\" },\n    //    {label: \"Last value\", value: \"lastValue\"},\n    //    {label: \"Last value 3 days average\", value: \"lastValue3dAvg\"},\n    { label: \"Alphabetically\", value: \"location\" }\n  ],\n  value: \"totalDeaths\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":742,"value":"html`\n<div class=\"countries\">\n  ${renderLegend()}\n  ${sortedTimeSeries.map(renderCountry)}\n</div>\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":177,"value":"md`## Ward map`","pinned":false,"hidden":false,"mode":"ojs"},{"id":167,"value":"FileAttachment(\"image.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":209,"value":"md`## Neighborhood`","pinned":false,"hidden":false,"mode":"ojs"},{"id":207,"value":"FileAttachment(\"image@3.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"md`## Census Tracts`","pinned":false,"hidden":false,"mode":"ojs"},{"id":615,"value":"FileAttachment(\"image@7.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":663,"value":"md`## Useful data resources:\n\nhttps://data.illinois.gov/dataset/410idph_hospital_directory/resource/9bdedb85-77f3-490a-9bbd-2f3f5f227981\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":654,"value":"md`## ----- Ignore Everything Below This Line -----`","pinned":false,"hidden":false,"mode":"ojs"},{"id":140,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"cpd_data = {\n  const links = d3.csvParseRows(cpd_source, ([source, target, value, linkColor = color]) => (source && target ? {source, target, value: !value || isNaN(value = +value) ? 1 : value, color: linkColor} : null));\n  const nodeByName = new Map;\n  for (const link of links) {\n    if (!nodeByName.has(link.source)) nodeByName.set(link.source, {name: link.source});\n    if (!nodeByName.has(link.target)) nodeByName.set(link.target, {name: link.target});\n  }\n  return {nodes: Array.from(nodeByName.values()), links};\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"viewof source = {\n  const textarea = html`<textarea spellcheck=\"false\">\nHispanic,White,169\nNot Hispanic,White,508\nHispanic,Black,2\nNot Hispanic,Black,644\nHispanic,Asian,1\nNot Hispanic,Asian,51\nHispanic,Other,10\nNot Hispanic,Other,26\nNot Hispanic,Null,3\nHispanic,Unknown,3\nNot Hispanic,Unknown,12`;\n  textarea.style.display = \"block\";\n  textarea.style.boxSizing = \"border-box\";\n  textarea.style.width = \"calc(100% + 28px)\";\n  textarea.style.font = \"var(--mono_fonts)\";\n  textarea.style.minHeight = \"60px\";\n  textarea.style.border = \"none\";\n  textarea.style.padding = \"4px 10px\";\n  textarea.style.margin = \"0 -14px\";\n  textarea.style.background = \"rgb(247,247,249)\";\n  textarea.style.tabSize = 2;\n  textarea.oninput = () => {\n    textarea.style.height = \"initial\";\n    textarea.style.height = `${textarea.scrollHeight}px`;\n  };\n  yield textarea;\n  textarea.oninput();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"race_data = {\n  const links = d3.csvParseRows(source, ([source, target, value, linkColor = color]) => (source && target ? {source, target, value: !value || isNaN(value = +value) ? 1 : value, color: linkColor} : null));\n  const nodeByName = new Map;\n  for (const link of links) {\n    if (!nodeByName.has(link.source)) nodeByName.set(link.source, {name: link.source});\n    if (!nodeByName.has(link.target)) nodeByName.set(link.target, {name: link.target});\n  }\n  return {nodes: Array.from(nodeByName.values()), links};\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"sankey = d3.sankey()\n    .nodeId(d => d.name)\n    .nodeAlign(d3[`sankey${align[0].toUpperCase()}${align.slice(1)}`])\n    .nodeSort(inputOrder ? null : undefined)\n    .nodeWidth(15)\n    .nodePadding(padding)\n    .extent([[0, 5], [width, height - 5]])","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"width = 400","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"height = 200","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"viewof padding = {\n  const form = html`<form class=\"observablehq--inspect\">padding = <input name=i type=number min=0 value=10 step=1  style=\"padding:2px;margin:-2px 0;width:120px;\"></form>`;\n  (form.oninput = () => form.value = form.i.valueAsNumber)();\n  return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":888,"value":"color","pinned":true,"hidden":false,"mode":"ojs"},{"id":17,"value":"viewof color = {\n  const form = html`<form class=\"observablehq--inspect\">color = <input name=i type=color value=\"#dddddd\" style=\"padding:2px;margin:-2px 0;\"></form>`;\n  (form.oninput = () => form.value = form.i.value)();\n  return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"viewof align = Object.assign(\n  html`<select>\n  <option value=left>Left-aligned\n  <option value=right>Right-aligned\n  <option value=center>Centered\n  <option value=justify selected>Justified\n</select>`,\n  {\n    value: new URLSearchParams(html`<a href>`.search).get(\"align\") || \"justify\"\n  }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"import {rasterize, serialize} from \"@mbostock/saving-svg\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"d3 = require(\"d3@5\", \"d3-sankey@0.12\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":419,"value":"renderLegend = () => html`\n<div class=\"countries__legend\">\n  <div class=\"countries__legend-days\">\n    ${daysRange.map(\n      date => `\n      <div class=\"countries__legend-day\" data-day=\"${date.getDate()}\"><span>${dateFormatNice(\n        date\n      )}</span></div>\n    `\n    )}\n  </div>\n  <div class=\"country__sum\">Total</div>\n  <div class=\"country__title\"></div>\n</div>\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":417,"value":"renderCountry = countryData => html`\n<div class=\"country\" data-country=\"${countryData.location}\">\n  <div class=\"country__days\">${countryData.daysData.map(dayData =>\n    renderDay(dayData, countryData)\n  )}</div>\n  <div class=\"country__sum\">${numFormat(countryData.totalDeaths)}</div>\n  <div class=\"country__title\">${countryData.location}</div>\n</div>\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":414,"value":"renderDay = (dayData, countryData) => {\n  const isMax =\n    dayData &&\n    dayData.new_deaths > 0 &&\n    dayData.new_deaths == countryData.maxDeathsPerDay;\n  const frac = (dayData ? dayData.new_deaths : 0) / countryData.maxDeathsPerDay;\n  return html`\n  <div \n    class=\"country__day ${isMax ? 'country__day--max' : ''}\" \n    style=\"background-color: ${hospitals_color(frac)}\"\n    title=\"${\n      dayData\n        ? `${dateFormatNice(new Date(dayData.date))}\\n${\n            countryData.location\n          }\\n${dayData.new_deaths} deaths`\n        : ''\n    }\"\n  ></div>\n`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":412,"value":"colorInterpolator = d3.interpolateRgbBasis(['#fef4b3', '#ffa579', '#e15483'])","pinned":false,"hidden":false,"mode":"ojs"},{"id":410,"value":"hospitals_color = d => (d ? colorInterpolator(d) : '#badad0')","pinned":false,"hidden":false,"mode":"ojs"},{"id":408,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":406,"value":"rawDataEcdc = d3.csvParse(await FileAttachment(\"cumsum@1.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":404,"value":"dateRange = [new Date('2020-03-10'), d3.max(rawDataEcdc, d => new Date(d.date))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":401,"value":"daysRange = d3.timeDays.apply(null, dateRange)","pinned":false,"hidden":false,"mode":"ojs"},{"id":399,"value":"countries = [...rawDataEcdc.reduce((s, d) => (s.add(d.location), s), new Set())]","pinned":false,"hidden":false,"mode":"ojs"},{"id":397,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":395,"value":"sortedTimeSeries = timeseries\n  .filter(d => d.totalDeaths >= filterCountries)\n  .sort(sorters[sortByMetric])\n  .sort((a, b) =>\n    a.location === 'World' ? -1 : b.location === 'World' ? 1 : 0\n  )","pinned":false,"hidden":false,"mode":"ojs"},{"id":393,"value":"sorters = ({\n  daysSinceMax: (a, b) => a.daysSinceMax - b.daysSinceMax,\n  lastValue: (a, b) => b.lastValue - a.lastValue,\n  lastValue3dAvg: (a, b) => b.lastValue3dAvg - a.lastValue3dAvg,\n  totalDeaths: (a, b) => b.totalDeaths - a.totalDeaths,\n  location: (a, b) => (a.location > b.location ? 1 : -1),\n  firstDay: (a, b) => a.firstDay - b.firstDay\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":391,"value":"patchDays = d => {\n  // China 2020-04-17 addition\n  if (d && d.location === 'China' && d.date === \"2020-04-17\") {\n    return { ...d, new_deaths: 0, patched: true };\n  }\n  return d;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":386,"value":"dateFormat = d3.timeFormat(\"%Y-%m-%d\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":384,"value":"dateFormatNice = d3.timeFormat(\"%b %d\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"formatLocale = d3.formatLocale({\n  thousands: '<span class=\"thinsp\"></span>',\n  grouping: [3]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":380,"value":"numFormat = formatLocale.format(\",\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":378,"value":"avg = arr =>\n  arr.length === 0 ? 0 : arr.reduce((a, b) => a + (b || 0), 0) / arr.length","pinned":false,"hidden":false,"mode":"ojs"},{"id":376,"value":"d3_hospitals = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":370,"value":"import { select } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":524,"value":"import { table } from '@tmcw/tables/2'","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"viewof inputOrder = {\n  const form = html`<form><label><input name=i type=checkbox checked> Preserve input order</label>`;\n  form.i.onclick = () => (form.value = form.i.checked, form.dispatchEvent(new CustomEvent(\"input\")));\n  form.value = form.i.checked;\n  return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":896,"value":"align","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W3d","stdlib":"1","titling":"auto"}
</article></body></html>