2c:null 2d:["$","title",null,{"children":"Body Camera Analysis | Matt Chapman | Observable"}] 32:T88e,md`## Disclaimer The data used here is ugly and filled with transcription errors, so any interpretation + reporting of this analysis *must* be done with this in mind. The body camera data itself is likely the most reliable timing information because it's computer generated, but times of arrests or times of uses of force times are hand written, and are much less reliable. Despite this, I believe that the analysis here is solid - the timing of events is entirely relative, which makes this analysis possible. This analysis does NOT look at metadata from vehicles, nor from POD cameras.. still some left to do (particularly around race), but we can talk about that next week ### BWC Metadata The Body Worn Camera (BWC) metadata consists of: start time, end time, duration in seconds, employee number (not their star), and size of file. This data is useful to for finding the closest time that a body camera was used. In total, there are 659,429 bodycam recordings. Undercover cops have their identifying info redacted. #### Cop Matching See section "Data Loading" section below for how data is loaded. CPD did not provide the star number tied to each BWC, and instead gave the employee number (on accident). Because most other data is by cops' badges, birth year, and apponitment date, this means the cops have to be matched in other ways. This was done in three stages: 1. Matching against Invisible Institute's complaint register data, which includes the employee number. Only works for cops with complaints. 2. Match against CPD roster data, based on whether a cop's first and last name is unique. Name match between BWC data and roster data = ID match. 3. Match against CPD attendance and assignment data for cops with the same first/last name. If a date is shared between two same-named cops, ignore that date, and count how many times a body cam was used by each cop with that same name. If the # of body camera usage is significantly higher than the other, then it's a likely match. This left about 200 cops unmatched -- about 5% of the total cops. Would take a very long time to match these, so I'm calling it good enough. ### Arrest Data The Arrest data consists of `33:T4b3,md`## Best evidence of systemic lack of BWC usage. The below two charts shows the time from an arrest that there was BWC footage. CPD directives require that all arresting cops turn on their body camera during an arrest, and this identifies how many cops apear to not follow this. To be on the absolute safest side, the first chart buckets by 30 minutes. In other words, if a cop hasn't used their body camera within 30 minutes of an arrest, they will show up in the 30 minute bucket. If their next or last BWC recording was 120 minutes away from the arrest, then that cop will show up in the 120 minute bucket. The Second chart shows how many times any cop didn't use a bodycam on a given day. Both of these charts only show cops that have body cameras, and all others are ignored. Note: If an arrest has two cops without BWC, that will show up twice in these charts. Similarly, if a cop was involved in two arrests and didn't use their BWC then, that cop will show up twice. ### What these charts do not show: 1. How many non-BWC arrests per-cop. 2. Cops who don't have BWC. 3. If a cop has a BWC but hasn't used it in months. This is done to prevent false positives (ie, if a cop lost theirs). `34:T9e9,rendered_hists = { let map_field = geojson_lookups[map_group]['map_field'] ; let brush_buckets = vl.selectInterval('closest_delta').encodings('x') ; let brush_time = vl.selectInterval('day').encodings('x') ; function hist(field) { const x = vl.x().fieldQ(field) .bin({'maxbins': 20, 'step':30}) .title('Minutes away from last BWC recording') ; return vl.layer( vl.markBar().select(brush_time) .encode(x, vl.color().value('transparent')), vl.markBar({"tooltip": {"content": "data"}}).transform(vl.filter(brush_buckets)) .encode( x, vl.y().count().title('Number of arrests with at least one BWC missing, per time group'), vl.color().value('lightgray') .if(brush_time, vl.color().field(time_since_group_by))) ).width(width - 220) } function ts(field, crossfilter) { const x = vl.x().fieldT('day').timeUnit('monthdate') return vl.layer( vl.markBar({"tooltip": {"content": "data"}}).select(brush_buckets) .encode(x, vl.color().value('transparent')), vl.markBar({"tooltip": {"content": "data"}}).transform(vl.filter(brush_time)) .encode( x, vl.y().count(), vl.color().value('lightgray') .if(brush_buckets, vl.color().field(time_since_group_by))) ).width(width - 220).height(100) } function choro() { var map_mark ; if (map_type == 'Choropleth') { var map_mark = vl.markGeoshape({"tooltip": {"content": "data"}}) .data(geojson.features) .encode( vl.color().fieldQ('count'), vl.tooltip([map_field, 'count'])) .transform(vl.lookup(map_field).from(vl.lookupData(group_counts).key('map_field').fields('count'))) .project(vl.projection('mercator')) .height(700) .width(700) ; } else if (map_type == 'Points') { var map_mark = vl.markPoint({'size': 3, "tooltip": {"content": "data"}}) .encode( vl.latitude().fieldQ('lat'), vl.longitude().fieldQ('lng'), vl.color().field(time_since_group_by) ) ; } return vl.layer( map_mark, vl.markGeoshape({stroke: '#000000', strokeWidth: .5}) .data(geojson.features).encode(vl.color().value('transparent'))).width(width - 220).height(800) } return vl.vconcat( hist('closest_delta'), ts('day', brush_buckets,brush_time), choro(brush_buckets) ).config({'background':'#FFFFFF'}).data(filtered_time_from_last_data).render() } ;35:T2e06,md`## ETL Process (Open code block to view) ` /*1. Download accused_export.xlsx from InvisInst's complaints_2000-2018_2018-07_18-060-294 dir: https://uca20ec2196ce0678a0ea61091fb.dl.dropboxusercontent.com/cd/0/get/BDfW8G-_9GUt5N_-INQRrCcyZ6Gx_IwokiTS-nwLeZgegC0kS7h9-HoNrcfVlJLHNj5K39TJ_FbcBcTqVDGQ8jvI1W5FdT9FyRcFpK0bu8WzauUJW2yymBQtmBQSs3tgkHs/file?_download_id=73324558962593517156651554481813095437279145210838667909256243931&_notify_domain=www.dropbox.com&dl=1 CREATE TABLE complaints ( LOG_NO TEXT, OFFICER_FIRST_NAME TEXT, OFFICER_LAST_NAME TEXT, MIDDLE_INITIAL TEXT, RACE_CODE_CD TEXT, SEX_CODE_CD TEXT, BIRTH_YEAR INT, STAR_NO INT, EMPLOYEE_NO INT, EMPLOYEE_POSITION TEXT, ALLEGATION_CATEGORY TEXT, ALLEGATION_CATEGORY_CD TEXT, INV_CURRENT_CATEGORY TEXT, INV_CURRENT_CATEGORY_CD TEXT, EMPLOYEE_POSITION_1 TEXT, EMPLOYEE_ASSIGN_UNIT TEXT, EMPLOYEE_DETAIL_UNIT TEXT, ACCUSED_ARRESTED TEXT, EMPLOYEE_ON_DUTY TEXT, INJURY_CONDITION TEXT, FINDING_CODE TEXT, FINDING_NARRATIVE TEXT, ACCUSATION_ID TEXT, PENALTY_ID TEXT, PENALTY_CODE TEXT, NO_OF_DAYS TEXT, NARRATIVE TEXT, IAD_OPS TEXT, CR_REQUIRED TEXT ) ; COPY complaints FROM '/home/matt/git/tcr/complaints/data/accused_export.csv' WITH (format csv, delimiter ',', QUOTE'"', HEADER); CREATE TABLE axon_metadata ( title TEXT, created_date_record_start TIMESTAMP, date_record_end TIMESTAMP, size_mb FLOAT, duration_seconds FLOAT, owner_first_name TEXT, owner_last_name TEXT, owner_badge_id TEXT ) ; --https://www.dropbox.com/s/kpm4fjzhx70rck6/axon_data.20200921.csv?dl=0 COPY axon_metadata FROM '/home/matt/git/tcr/bodycameras/data/axon_data.20200921.csv' WITH (FORMAT CSV, DELIMITER ',', QUOTE '"', HEADER); ALTER TABLE axon_metadata ADD COLUMN ID SERIAL PRIMARY KEY; ALTER TABLE axon_metadata ADD COLUMN owner_middle_initial TEXT ; ALTER TABLE axon_metadata ADD COLUMN owner_race TEXT ; ALTER TABLE axon_metadata ADD COLUMN owner_sex TEXT ; ALTER TABLE axon_metadata ADD COLUMN owner_birth_year INT ; ALTER TABLE axon_metadata ADD COLUMN owner_star_no INT ; ALTER TABLE axon_metadata ADD COLUMN officer_id INT ; --convencience columns ALTER TABLE axon_metadata ADD COLUMN owner_name TEXT ; ALTER TABLE axon_metadata ADD COLUMN started_date TIMESTAMP ; ALTER TABLE axon_metadata ADD COLUMN ended_date TIMESTAMP ; --Cleanups UPDATE axon_metadata SET date_record_end = (created_date_record_start + (duration_seconds * interval '1 second')) ; UPDATE axon_metadata SET started_date = date_trunc('day', created_date_record_start) ; UPDATE axon_metadata SET ended_date = date_trunc('day', date_record_end) ; UPDATE axon_metadata SET owner_first_name = TRIM(UPPER(owner_first_name)) ; UPDATE axon_metadata SET owner_last_name = TRIM(UPPER(owner_last_name)) ; UPDATE axon_metadata SET owner_name = CONCAT(owner_first_name, ' ', owner_last_name) ; --Link with other datasets UPDATE axon_metadata b SET owner_first_name = c.officer_first_name, owner_last_name = c.officer_last_name, owner_middle_initial = c.middle_initial, owner_race = c.race_code_cd, owner_sex = c.sex_code_cd, owner_birth_year = c.birth_year, owner_star_no = c.star_no FROM (SELECT officer_first_name, officer_last_name, middle_initial, race_code_cd, sex_code_cd, birth_year, star_no, employee_no FROM complaints) as c WHERE b.owner_badge_id::text = c.employee_no::text ; --update axon_metadata using first/last name from badge data where there are no other similar names WITH results as ( with unidentified as ( SELECT DISTINCT(owner_badge_id) owner_badge_id, CONCAT(owner_first_name, ' ', owner_last_name) owner_name FROM axon_metadata WHERE owner_star_no IS NULL ), badges AS ( SELECT DISTINCT CONCAT(first_name, ' ', last_name) AS badge_name, first_name, last_name, middle_initial, appointed_date, star_number, year_of_birth, race, sex FROM cop_badges WHERE end_date IS NULL ) SELECT owner_name, owner_badge_id, b.*, (select count(bs.*) FROM badges bs WHERE b.appointed_date != bs.appointed_date AND SIMILARITY(bs.badge_name, b.badge_name) = 1) AS count_same_name FROM unidentified, badges b WHERE SIMILARITY(owner_name, badge_name) = 1 ORDER BY count_same_name ) UPDATE axon_metadata SET owner_first_name = results.first_name, owner_last_name = results.last_name, owner_middle_initial = results.middle_initial, owner_race = results.race, owner_sex = results.sex, owner_birth_year = results.year_of_birth, owner_star_no = results.star_number FROM results WHERE count_same_name = 0 AND results.owner_badge_id = axon_metadata.owner_badge_id ; update axon_metadata set officer_id = 33611 where owner_badge_id = 122353::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 12077 where owner_badge_id = 117558::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 21454 where owner_badge_id = 115186::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 32925 where owner_badge_id = 121404::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 21167 where owner_badge_id = 118962::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 11083 where owner_badge_id = 119657::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 29402 where owner_badge_id = 109196::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 21254 where owner_badge_id = 112134::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 22285 where owner_badge_id = 105436::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 8021 where owner_badge_id = 119297::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33023 where owner_badge_id = 120402::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33387 where owner_badge_id = 121832::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 19553 where owner_badge_id = 115884::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33561 where owner_badge_id = 122485::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 4134 where owner_badge_id = 126626::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33273 where owner_badge_id = 122001::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 28744 where owner_badge_id = 119366::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33227 where owner_badge_id = 122503::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33165 where owner_badge_id = 122076::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 16730 where owner_badge_id = 114014::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33349 where owner_badge_id = 126009::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 32740 where owner_badge_id = 120360::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 17567 where owner_badge_id = 117980::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 24085 where owner_badge_id = 126002::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 32724 where owner_badge_id = 124243::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 10241 where owner_badge_id = 119647::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 10312 where owner_badge_id = 117553::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 11060 where owner_badge_id = 113770::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 11063 where owner_badge_id = 115794::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 17568 where owner_badge_id = 118518::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 19958 where owner_badge_id = 115180::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 23371 where owner_badge_id = 119813::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 24471 where owner_badge_id = 119930::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33076 where owner_badge_id = 122633::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33464 where owner_badge_id = 121816::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 30930 where owner_badge_id = 118037::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 4454 where owner_badge_id = 119876::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 6841 where owner_badge_id = 118917::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 22264 where owner_badge_id = 123797::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33553 where owner_badge_id = 122482::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33605 where owner_badge_id = 122310::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 24383 where owner_badge_id = 119348::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33347 where owner_badge_id = 121769::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 32660 where owner_badge_id = 120857::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 23380 where owner_badge_id = 116888::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 23372 where owner_badge_id = 118970::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33222 where owner_badge_id = 122657::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33483 where owner_badge_id = 122309::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33518 where owner_badge_id = 122409::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 32610 where owner_badge_id = 121058::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33482 where owner_badge_id = 122292::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33648 where owner_badge_id = 122598::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33083 where owner_badge_id = 122625::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33465 where owner_badge_id = 121776::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33213 where owner_badge_id = 116225::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 2975 where owner_badge_id = 119278::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33238 where owner_badge_id = 122489::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 18810 where owner_badge_id = 112953::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 25014 where owner_badge_id = 119931::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 17572 where owner_badge_id = 106128::text and owner_badge_id ~ '^[0-9]+'; update axon_metadata set officer_id = 33408 where owner_badge_id = 121750::text and owner_badge_id ~ '^[0-9]+'; UPDATE axon_metadata am set owner_middle_initial = o.middle_initial, owner_race = o.race, owner_sex = o.gender, owner_birth_year = o.birth_year, owner_star_no = o.current_badge::int FROM data_officer o WHERE am.officer_id is not null AND am.officer_id = o.id ; */36:T8bb,function Scrubber( values, { format = value => value, initial = 0, delay = null, autoplay = true, loop = true, alternate = false } = {} ) { values = Array.from(values); const form = html``; let timer = null; let direction = 1; function start() { mutable animation_state = "running"; form.b.textContent = "Pause"; timer = delay === null ? requestAnimationFrame(tick) : setInterval(tick, delay); } function stop() { mutable animation_state = "paused"; form.b.textContent = "Play"; if (delay === null) cancelAnimationFrame(timer); else clearInterval(timer); timer = null; } function tick() { mutable animation_state = "running"; if (delay === null) timer = requestAnimationFrame(tick); if ( form.i.valueAsNumber === (direction > 0 ? values.length - 1 : direction < 0 ? 0 : NaN) ) { if (!loop) return stop(); if (alternate) direction = -direction; } form.i.valueAsNumber = (form.i.valueAsNumber + direction + values.length) % values.length; form.i.dispatchEvent(new CustomEvent("input", { bubbles: true })); } form.i.oninput = event => { if (event && event.isTrusted && timer) form.b.onclick(); form.value = values[form.i.valueAsNumber]; form.o.value = format(form.value, form.i.valueAsNumber, values); }; form.b.onclick = () => { if (timer) return stop(); direction = alternate && form.i.valueAsNumber === values.length - 1 ? -1 : 1; form.i.valueAsNumber = (form.i.valueAsNumber + direction) % values.length; form.i.dispatchEvent(new CustomEvent("input", { bubbles: true })); start(); }; form.i.oninput(); if (autoplay) start(); else stop(); disposal(form).then(stop); return form; }37:[] 30:{"id":"9f09764dbbdfc4b5","slug":null,"title":"Body Camera Analysis","thumbnail":"b7400c47f1d47633db35318f9392ee7a06c72b91b99efd7068a97cdd6a007dca","access_level":"unlisted","create_time":"$D2020-12-08T20:51:19.880Z","update_time":"$D2022-10-29T08:28:45.469Z","view_time":null,"first_public_time":"$D2022-10-29T08:16:08.838Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"af316d725f98b16e","login":"red-bin","name":"Matt Chapman","avatar_url":"https://avatars-next.observableusercontent.com/15c3124ff8dce9bb222558db1d9d9f940c9cad1d6449cee8418dfffafeb0e768","tier":"starter_2024"},"version":7558,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"02f90c4e875fe866","login":"red-bin","name":"Matt Chapman","avatar_url":"https://avatars-next.observableusercontent.com/15c3124ff8dce9bb222558db1d9d9f940c9cad1d6449cee8418dfffafeb0e768","tier":null}],"members":[{"type":"individual","id":"02f90c4e875fe866","login":"red-bin","name":"Matt Chapman","avatar_url":"https://avatars-next.observableusercontent.com/15c3124ff8dce9bb222558db1d9d9f940c9cad1d6449cee8418dfffafeb0e768","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Body Camera Analysis","cells":[{"id":0,"value":"md`# Body Camera Analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1211,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1254,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6996,"value":"md`## Snippets\n1. There were 51 primary and secondary cops that did not use their body camera for 5 or arrests.\n2. There were 876 primary and secondary cops that did not use their body camera for one or more arrest.\n3. The day with the fewest recorded arrests was June 1, where 45% of arrests had no cop with a recording bodycamera. With that said, from 68% of arrests, half of all involve cops had no body camera turned on.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1291,"value":"\"Number of cops potentially in violation, based on these filters: \\n\" + new Set(filtered_time_from_last_data.map(d=> d.first_name + ' ' + d.last_name + d.cpd_star_no)).size","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6042,"value":"md`## Data Filters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6105,"value":"html`\n\n\n

\n${viewof time_since_group_by} ${viewof map_group} ${viewof map_type}\n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5369,"value":"viewof min_date = date({\n title: \"Minimum Date\",\n min: \"2020-05-25\",\n max: \"2020-6-05\",\n value: \"2020-05-29\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5372,"value":"viewof max_date = date({\n title: \"Maximum Date\",\n min: \"2020-05-25\",\n max: \"2020-08-18\",\n value: \"2020-08-18\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":782,"value":"viewof last_from_slider = rangeSlider({\n min: 0\n,\n max: 10000,\n precision: 1, // how many decimal places\n title: 'Time since last bodycam',\n step: 30,\n value: [60,3000]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":827,"value":"viewof time_since_roles = checkbox(\n {options: \n [\n {value: \"First Arresting Officer\", label: \"First Arresting Officer\"},\n {value: \"Second Arresting Officer\", label: \"Second Arresting Officer\"},\n {value: \"Assisting Arresting Officer\", label: \"Assisting Arresting Officer\"}\n ],\n value: [\"First Arresting Officer\", \"Second Arresting Officer\"],\n title:\"Filter on Officer role\"\n}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7385,"value":"viewof protest_looting_filter = checkbox(\n {options: \n [\n {value: \"protest\", label: \"Only Protests Related\"},\n {value: \"looting\", label: \"Only Looting Related\"},\n {value: \"\", label: \"All Others\"},\n ],\n value: [\"protest\",\"looting\", \"\"],\n title:\"Select Protest or Looting only\"\n}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1591,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6790,"value":"md`### Number of cops with instances`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5898,"value":"viewof num_incidents = rangeSlider({\n min: 1,\n max: 100,\n precision: 1, // how many decimal places\n title: 'Range of # of times w/o body camera per cop',\n step: 1,\n value: [1,100]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6909,"value":"md`## Chart of above map`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3947,"value":"vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(filtered_time_from_last_data)\n .encode(\n vl.x().field(map_group).sort('-y'),\n vl.y().count(),\n vl.color().field(time_since_group_by)\n )\n .title('Chart of above map')\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7483,"value":"vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(filtered_time_from_last_data)\n .encode(\n vl.y().field(time_since_group_by).sort('-x'),\n vl.x().count(),\n )\n .width(700)\n .height(250)\n .title('Chart of above map')\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6914,"value":"md`## Instances per cop`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6811,"value":"table(cop_histo_data.sort((a, b) => (a.count < b.count) ? 1:-1))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7000,"value":"cop_histo_data.sort((a, b) => (a.count < b.count) ? 1:-1).filter(d=> d.count >= 5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5913,"value":"vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(cop_histo_data)\n .encode(\n vl.x().fieldQ('count').bin({'maxbins': 3, 'step':1}).sort('ascending').title('non-bwc arrests per cop'),\n vl.y().count().title('number of cops')\n ).title('Occurances per cop')\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5805,"value":"md`# TRR Data\n\nThe below data is based directly on use of force records.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"trr_raw_chart = vl.markBar({\"tooltip\": {\"content\": \"data\"}, \"orient\": \"horizontal\"})\n .data(trr_data.filter(\n d=> (trr_inclusions.includes(d.video_bwc_i)) \n && d.subject_injured == 'Yes'\n //&& (!injury_exclusions.includes(\"only_cop_injured\") || d.member_injured == 'Yes')\n && d3.timeParse(d.day) <= max_date && d3.timeParse(d.day) >= min_date))\n .encode(\n vl.x().count(),\n vl.y().field('sub_race').sort('-x'),\n vl.color().field('video_bwc_i')\n\n )\n .width(600).render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7010,"value":"[min_date, max_date]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7003,"value":"white hispanic:4/14\nblack: 36/118","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":399,"value":"viewof trr_inclusions = checkbox(\n {options: \n [\n {value: \"Y\", label: \"With Body Camera\"},\n {value: \"N\", label: \"Without Body Camera\"},\n {value: \"\", label: \"Unmarked\"}\n ],\n value: [\"Y\", \"N\", \"\"],\n title:\"Filter on BWC usage\"\n}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":525,"value":"viewof injury_exclusions = checkbox(\n {options: \n [\n {value: \"only_arrestee_injured\", label: \"Arrestee Injured\"},\n {value: \"only_cop_injured\", label: \"Cop Injured\"},\n ],\n title:\"Filter on Injuries\"\n}\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6838,"value":"md `## Manual Checks\n\nThese are notes from manually checking the cops where BWC was missing the most.\n\nWhat stands out from these is that most of their arrests seem to be in mass arrests. What is also interesting about them is that they don't have bodycamera recordings during 6/01, though they do for the days before and after.\n\n### Kristen Martin\n\nKristen Martin was involved in 19 arrests between 5/29 and 6/05. Of those, 15 were on 6/01. The data shows that she used no body camera on 06/01.\n\n### Michael Baciu\nSame thing with Martin Baciu.\n\n### Humberto Cruz\nSame thing with Martin Baciu.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3672,"value":"cop_profiles = {\n const cop_map = {};\n filtered_time_from_last_data.map(d=>[d.first_name + ' ' + d.last_name + ' (' + d.cpd_star_no + ')']).forEach(item => {\n \n if(cop_map[item]){\n cop_map[item]++;\n }else{\n cop_map[item] = 1;\n }\n }\n )\n return cop_map;\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5881,"value":"cop_histo_data = Object.entries(cop_profiles).filter(d=>d[1] <= num_incidents[1] && d[1] >= num_incidents[0]).map(d=>({'cop': d[0], 'count':d[1]}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3287,"value":"geojson = geojson_lookups[map_group]['geojson'] ;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1221,"value":"minutes_since_charts = {\n const brush_time = vl.selectInterval('day').encodings('x') ;\n const brush_buckets = vl.selectInterval('day').encodings('x') ;\n \n const time_since_by_buckets = vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .encode(\n vl.y().count().title('Number of arrests, per cop'),\n vl.x().fieldQ('closest_delta').bin({'maxbins': 20, 'step':30}).title('Minutes away from last BWC recording'),\n vl.color().field(time_since_group_by),\n ).title(\"# of minutes since last BWC recording from an arrest\")\n .width(800) ;\n \n const time_since_by_date = vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .transform(vl.filter(brush_buckets))\n .encode(\n vl.x().fieldT('day'),\n vl.y().count(),\n vl.color().field(time_since_group_by)\n )\n .title('Sum of # of cops per arrest without BWC')\n .width(800) \n \n return vl.vconcat(time_since_by_buckets)\n .data(filtered_time_from_last_data) ;\n} ;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3573,"value":"group_counts = {\n const distinct_grouping = new Set(filtered_time_from_last_data.map(d=>d['id'])) ;\n const arr = Array.from(distinct_grouping) ;\n\n const group_map = {};\n filtered_time_from_last_data.map(d=>d[map_group]).forEach(item => {\n if(group_map[item]){\n group_map[item]++;\n }else{\n group_map[item] = 1;\n }\n })\n var results = [];\n return Object.entries(group_map).map(d=> ({'map_field': d[0], 'count': d[1]}))\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7073,"value":"group_counts","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1411,"value":"md`### Cops found in above filters`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1355,"value":"md`## Ratio of cops without body cam per arrest\n\nThis chart shows the ratio of cops *per arrest* that don't use a body camera. On the first few days of the GF protests, about half of arrests did not have any cops recording. Similar to the above charts, this was done by checking against the last time a recording was made.\n\nFull disclosure: this is not as fool-proof as the above charts. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"dataset_view = 'https://bodycamviz.s3.us-east-2.amazonaws.com/arrests_w_bwc_2020-05-31_2020-08-31_60m.csv'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7479,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7259,"value":"\"The day with the fewest recorded arrests was June 1, where 45% of arrests had no cop with a recording bodycamera. With that said, from 68% of arrests, half of all involve cops had no body camera turned on.\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6636,"value":"bwc_percent_chart = vl.markBar()\n .data(bwc_percent)\n .encode(\n vl.x().fieldT('time').timeUnit('monthdate').title('Date'),\n vl.y().fieldQ('percent'),\n vl.tooltip(['percent', vl.x().fieldT('time').timeUnit('monthdate')])\n )\n .title(\"Arrests with \"+ (100*ratio_slider[0])+\"% to \"+(100*ratio_slider[1])+\"% cops w/bwc\")\n .width(800).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"viewof ratio_slider = rangeSlider({\n min: 0.0,\n max: 1.0,\n value: [0,.5],\n precision: 1, // how many decimal places\n title: 'Percent of cops with BWC footage'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7018,"value":"[d3.median(bwc_percent.map(d=>d.percent)),\n d3.sum(bwc_percent.map(d=>d.percent))/bwc_percent.map(d=>d.percent).length]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"cr_graph = vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(time_breakdown_data)\n .encode(\n vl.x().fieldT('arrest_day').timeUnit('monthdate'),\n vl.y().count(),\n vl.color().fieldO('cop_w_bwc_no_ass_ratio')\n )\n .width(800).render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7146,"value":"raw_bwc_arrests_data.map(d=>parseInt(d.cop_count))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6685,"value":"viewof min_max_cops = rangeSlider({\n min: 1,\n max: d3.max(raw_bwc_arrests_data.map(d=>parseInt(d.cop_count_no_ass))),\n precision: 0, // how many decimal places\n title: 'Min/Max Cops Per Arrest'\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6392,"value":"bwc_percent = {\n const raw_arrest_day_totals = {}\n const arrest_day_totals = {} ;\n \n raw_bwc_arrests_data.map(d=> raw_arrest_day_totals[d.arrest_day] = 0)//parseInt(d.daily_arrests)) ;\n raw_bwc_arrests_data.map(d=> raw_arrest_day_totals[d.arrest_day]++) ;\n \n \n time_breakdown_data.map(d=> arrest_day_totals[d.arrest_day] = raw_arrest_day_totals[d.arrest_day])\n \n let bwc_day_totals = Object.fromEntries(Object.keys(arrest_day_totals).map(d=> [d,0]))\n time_breakdown_data.map(d=> bwc_day_totals[d.arrest_day]++)\n\n return Object.entries(arrest_day_totals).map(d=> ({time: d[0], percent: 100*parseInt(bwc_day_totals[d[0]]) / parseInt(d[1])})) ;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7281,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7150,"value":"raw_bwc_arrests_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"time_breakdown_data = raw_bwc_arrests_data.filter(\n d=>\n parseInt(d.cop_count_no_ass) >= min_max_cops[0] && parseInt(d.cop_count_no_ass) <= min_max_cops[1] &&\n (d.cop_w_bwc_no_ass_ratio >= ratio_slider[0] &&d.cop_w_bwc_no_ass_ratio <= ratio_slider[1])\n && (d3.timeParse(d.arrest_day) < max_date && d3.timeParse(d.arrest_day) > min_date))\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7390,"value":"protest_looting_filter\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7129,"value":"bwc_percent","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7133,"value":"ratio_slider","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7137,"value":"min_max_cops","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6753,"value":"time_breakdown_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1384,"value":"md`## Number of Minutes/Median per day of recorded video`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"viewof lens_view = {\n return select({\n title: \"Dataset used (sum = total minutes, median = median per cop)\",\n options: [\n 'https://bodycamviz.s3.us-east-2.amazonaws.com/bwc_lens_2020-05-25_2020-08-31_sum_day.csv',\n 'https://bodycamviz.s3.us-east-2.amazonaws.com/bwc_lens_2020-05-25_2020-08-31_sum_hour.csv',\n 'https://bodycamviz.s3.us-east-2.amazonaws.com/bwc_lens_2020-05-25_2020-08-31_median_day.csv',\n 'https://bodycamviz.s3.us-east-2.amazonaws.com/bwc_lens_2020-05-25_2020-08-31_median_hour.csv']\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":217,"value":"lens_chart = vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(raw_bwc_lens_data)\n .encode(\n vl.x().fieldT('group_col').title('Date').timeUnit('monthdate'),\n vl.y().fieldQ('agg').title('Total minutes')\n )\n .title('Number of recorded minutes per day')\n .width(800).render()\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1515,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1513,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1497,"value":"bodycam_times_per_cop = d3.csv('https://bodycamviz.s3.us-east-2.amazonaws.com/bodycam_times_per_cop.csv')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7168,"value":"raw_counts_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1400,"value":"md`## Count of arrests, TRRs, bodycam footage, and attendance per day`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":282,"value":"viewof counts_view = {\n return select({\n title: \"Select category of counts\",\n options: [\n 'trr_count',\n 'bodycam_count',\n 'attendance_count',\n 'arrest_count']\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":272,"value":"counts = vl.markBar({\"tooltip\": {\"content\": \"data\"}})\n .data(raw_counts_data)\n .encode(\n vl.x().fieldT('trunc_1440_minute').timeUnit('monthdate'),\n vl.y().fieldQ(counts_view)\n )\n .width(800).render()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7184,"value":"raw_counts_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7177,"value":"raw_counts_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5723,"value":"raw_counts_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1404,"value":"md `## Breakdown per TRR, broken down by race for whether video was recorded\n\nIn the data, TRRs that don't have a Y or N are likely to NOT have video. Further research is needed on this.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6353,"value":"d3.max(filtered_time_from_last_data.map(d=>d.lat))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1985,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5818,"value":"md`## Datasets Loading`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":334,"value":"trr_data = d3.csv('https://bodycamviz.s3.us-east-2.amazonaws.com/trr_bwc_list.csv')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7417,"value":"time_from_last_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":805,"value":"filtered_time_from_last_data = {\n var minmax_dateParse = d3.timeParse('%Y-%m-%d') ;\n var filtered_data = time_from_last_data.filter(\n d=>\n d.arrest_date_time >= minmax_dateParse(min_date) && d.arrest_date_time <= minmax_dateParse(max_date)\n && parseFloat(d.closest_delta) >= last_from_slider[0] \n && parseFloat(d.closest_delta) <= last_from_slider[1]\n && time_since_roles.includes(d.officer_role)\n && d.lat < 42.03 && d.lat > 41.64\n && protest_looting_filter.includes(d.protest_looting)\n )\n \n \n //if ('by_officer' == 'by_officer') {\n //filtered_time_from_last_data.map(d=> d.first_name + ' ' + d.last_name + d.cpd_star_no)\n return filtered_data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":747,"value":"time_from_last_data = {\n const csv = d3.csv('https://bodycamviz.s3.us-east-2.amazonaws.com/times_since_last_recording.csv') ;\n \n csv.then(function (csv_data) {\n csv_data.map(function(ele) {\n ele.arrest_date_time = dateParse(ele.arrest_date_time);\n ele.day = dateParse(ele.day);\n ele.hour = dateParse(ele.hour);\n ele.closest_time = dateParse(ele.closest_time)\n ele.lat = parseFloat(ele.lat)\n ele.lng = parseFloat(ele.lng)\n })\n })\n //csv.forEach((item, i) => csv[i]['arrest_date_time'] = dateParse(csv[i]['arrest_date_time'])) ;\n \n return csv\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5550,"value":"dateParse = d3.timeParse(\"%Y-%m-%d %H:%M:%S.%L%L\") ;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"raw_counts_data = d3.csv('https://bodycamviz.s3.us-east-2.amazonaws.com/bwc_ts_counts_2020-05-31_2020-08-31.day.csv')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7175,"value":"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":212,"value":"raw_bwc_lens_data = d3.csv(lens_view)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"raw_bwc_arrests_data = d3.csv(dataset_view) ;","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3590,"value":"mutable animation_state = \"running\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6804,"value":"md`### dropdowns`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6772,"value":"viewof map_type = {\n return select({\n title: \"Map Type\",\n options: ['Choropleth', 'Points']\n })\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6967,"value":"viewof map_group = {\n return select({\n title: \"Boundaries\",\n options: [\n 'community',\n 'beat_num',\n 'ward',\n 'census_tract',\n 'dist_num']\n })\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6949,"value":"viewof time_since_group_by = {\n return select({\n title: \"Grouping\",\n options: ['race', 'sex','officer_role', 'cop_sex', 'cop_race','cop_birth_year']\n })\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6871,"value":"md`### end dropdowns`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3051,"value":"geojson_lookups = ({\n 'community': ({\n 'geojson': d3.json('https://chapcagogeojsons.s3.us-east-2.amazonaws.com/neighborhoods.geojson'), \n 'map_field': 'properties.community'\n }),\n 'beat_num': ({\n 'geojson': d3.json('https://chapcagogeojsons.s3.us-east-2.amazonaws.com/chicago_police_beats.geojson'),\n 'map_field': 'properties.beat_num'\n }),\n 'ward': ({\n 'geojson': d3.json('https://chapcagogeojsons.s3.us-east-2.amazonaws.com/Boundaries+-+Wards+(2015-).geojson'),\n 'map_field': 'properties.ward'\n }),\n 'census_tract': ({\n 'geojson': d3.json('https://chapcagogeojsons.s3.us-east-2.amazonaws.com/Boundaries+-+Census+Tracts+-+2010.geojson'),\n 'map_field': 'properties.geoid10'\n }),\n 'dist_num': ({\n 'geojson': d3.json('https://chapcagogeojsons.s3.us-east-2.amazonaws.com/Boundaries - Police Districts (current).geojson'),\n 'map_field': 'properties.dist_num'\n})\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3583,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3585,"value":"import { disposal } from \"@mbostock/disposal\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"import { select,radio,checkbox, slider } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"d3 = require('d3', 'd3-array', 'd3-svg-annotation')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":584,"value":"import { table } from '@tmcw/tables/2'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":105,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3651,"value":"import { date } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 31:"$30:body"