45:["$","title",null,{"children":"Trends in Police Stops | Caroline Cullinan | Observable"}] 4d:T4a3,html` As a result of California’s Racial and Identity Profiling Act of 2015 (RIPA) (i.e., Assembly Bill (AB) 953) state and local agencies concerned with law enforcement must now annually report all stop incident data to the Attorney General. Such development has been made with the intent of eliminating racial and identity profiling and improving diversity and racial and identity sensitivity in law enforcement. However, in order to best address issues related to such racial and identity profiling issues, demographic data related to police stops must not just be recorded, but also analyzed. In doing so, law enforcement behavior insights can drive future decisions that shape criminal justice practice and policy.

As such, this Observable notebook takes a preliminary look at the Oakland Police Department’s (OPD) stopping behavior for the 2019 year. In analyzing demographic data as it relates to reasons for police stops, instances of handcuffs being used during stops, and instances of drugs being found during stops, a deeper understanding of OPD’s policing behaviors can be developed, thus ultimately informing more equitable policing practices.

`4e:T879,html` Section 1.0 of this Observable notebook outlines visualizations pertaining to the exploratory analysis of this work. More specifically, section 1.1 highlights the different reasons for the 2019 police stops (i.e., all, consensual encounter resulting in search, investigation to determine whether a person was truant, knowledge of outstanding arrest warrant or wanted person, parole/probation/PRCS/mandatory supervision, possible conduct under education code, reasonable suspicion, traffic violation, and unidentified reason for stop) by demographic information (i.e., race/ethnicity, gender, age, English fluency level, disability, and LGBT identity). Section 1.2 also considers demographic information while outlining whether or not the 2019 police stop instances resulted in an individual being handcuffed or not. In section 1.3, whether or not drugs were found during the 2019 police stops is explored via demographics. Section 2.0 provides an interpretation of the exploratory analysis and provides answers to the posed questions. Finally, in section 3.0, recommendations for future data collection and analysis are made.

Please see the Appendix for access to the code behind this analysis and for a complete explanation of assumptions, data (including explanation of identified errors/anomalies present), and analysis methods.

Many alternative visualization options have been made available for each section 1 subsection (i.e., 1.1, 1.2, 1.3). For each subsection, please be sure to interact with the buttons located at the top of the subsection. In doing so, you can switch between different metrics and modify visualization elements for all figures in that section.

In addition, please hover over all figures in order to access deeper insights.

`4f:T421,{ const total = d3.sum(stop_explore_view_english_fluency_level, d=>d.value) const plot = Plot.plot({ marginLeft: 200, width: 1000, height:180, color: { range: tol.QualLight }, y: { label:"Level of English Fluency" }, x: { label:normalize?"Proportion of Police Stops (%)":"Police Stop Count", // tickFormat: normalize?d=>d+"%":d=>d, domain:normalize?[0,100]:[0, d3.max(stop_explore_view_english_fluency_level, d=>d.value)], nice:true }, caption:"Oakland Police Department's 2019 stopping behavior by stop reason and perceived level of English fluency", marks: [ Plot.barX(stop_explore_view_english_fluency_level, { x: d=>getValue(d, total, normalize), y: "variable", fill:"variable", sort: {y: "x", reverse: true}, title: (d) => `${Math.round(getValue(d, total, normalize))}${normalize?"%":""} \n police stops` // \n makes a new line }), Plot.ruleX([0]) ], tooltip: { fill: "", stroke: "black", "stroke-width": "3px", } }) return plot }50:T571,{ const data = demographicHC!=="All"?handcuff_explor_tidy.filter(d=>d.category===demographicHC&&getSpecialFilter(d,d.category)) :d3.rollups(handcuff_explor_tidy, v => d3.sum(v, d => d.value), d => d.ads_handcuffed).map(d=>{return { ads_handcuffed:d[0], value:d[1] }}) const total = d3.sum(data, d=>d.value) const plot = Plot.plot({ marginLeft: demographicHC!=="All"?150:20, width: 1000, height:300, color:{legend:true, range: [tol.QualLight[1], tol.QualLight[6]]}, x: { label:normalizeHandcuffs?"Proportion of Police Stops (%)":"Police Stop Count", // tickFormat: normalize?d=>d+"%":d=>d, domain:normalizeHandcuffs?[0,100]:[0, d3.max(data, d=>d.value)], nice:true }, y: { label:demographicHC==="All"?"All Demographics":"", // tickFormat: normalize?d=>d+"%":d=>d, }, marks: [ Plot.barX(data, { y: "variable", x: d=>getValue(d, total, normalizeHandcuffs), fill:"ads_handcuffed", sort: {y: "x", reverse: true}, title: (d) => `${Math.round(getValue(d, total, normalizeHandcuffs))}${normalizeHandcuffs?"%":""} \n police stops` // \n makes a new line }), Plot.ruleX([0]) ], tooltip: { fill: "", stroke: "black", "stroke-width": "3px", } }) return plot }51:T53e,{ const data = demographic!=="All"?drug_explor_tidy.filter(d=>d.category===demographic&&getSpecialFilter(d,d.category)) :d3.rollups(drug_explor_tidy, v => d3.sum(v, d => d.value), d => d.ced_drugs).map(d=>{return { ced_drugs:d[0], value:d[1] }}) const total = d3.sum(data, d=>d.value) const plot = Plot.plot({ marginLeft: demographic!=="All"?150:20, width: 1000, height:300, color:{legend:true, range: [tol.QualLight[2], tol.QualLight[0]]}, x: { label:normalizeDrugs?"Proportion of Police Stops (%)":"Police Stop Count", // tickFormat: normalize?d=>d+"%":d=>d, domain:normalizeDrugs?[0,100]:[0, d3.max(data, d=>d.value)], nice:true }, y: { label:demographic==="All"?"All Demographics":"", // tickFormat: normalize?d=>d+"%":d=>d, }, marks: [ Plot.barX(data, { y: "variable", x: d=>getValue(d, total, normalizeDrugs), fill:"ced_drugs", sort: {y: "x", reverse: true}, title: (d) => `${Math.round(getValue(d, total, normalizeDrugs))}${normalizeDrugs?"%":""} \n police stops` // \n makes a new line }), Plot.ruleX([0]) ], tooltip: { fill: "", stroke: "black", "stroke-width": "3px", } }) return plot }52:Tfae,html`In answering PPIC’s exercise prompt with regard to: the reasoning for said 2019 police stops; demographic information of people stopped by police in 2019; how many 2019 police stops lead to an individual being handcuffed; and how many 2019 police stops involved drugs being found, the following information can be observed.

Reason for Police Stops

Of all the 24,395 recorded police stops made by OPD in 2019, about 56% of these stops were conducted on the basis of reasonable suspicion. The remaining OPD stops made in the 2019 year were concerning traffic violation (~38%), knowledge of an outstanding arrest warrant or wanted person (~4%), parole/probation/PRCS/mandatory supervision (~1%), and consensual encounter resulting in search (~1%).

Demographics of Police Stops

Considering who was stopped by OPD in 2019 for all potential reasons, it can be observed that people with Black or African American identities were predominantly stopped by the police, accounting for about 53% of all reasons for stop. The remaining police stops were conducted on people with Hispanic or Latino (~25%), white (~13%), Asian (~5%), Middle Eastern or South Asian (~2%), Multiracial (~1%), and Pacific Islander (~1%) identities. Notably, only 29 people who were stopped by OPD in 2019 had Native American identities (i.e., making up less than 1% of all stop instances).

Further considering demographic information and all potential reasons to be stopped by the police, it can be noted that in the year 2019, OPD stops involved people with male genders roughly 71% of the time. People with female genders were stopped by OPD roughly 29% of the time for this same year. Only 20 instances of OPD 2019 police stops involved people with gender nonconforming identities (i.e., making up less than 1% of all stop instances).

With regard to OPD stops and age, a majority of 2019 stops were conducted on people who were younger than 40 years old. More specifically, people aged between 10 and 20 years accounted for roughly 19% of stops, people aged between 21-30 years accounted for roughly 38% of stops, and people aged between 31-40 years accounted for roughly 22% of stops. Respectively, people aged between 41-50 years, 51-60 years, and 61-70 years accounted for about 13%, 5%, and 1% of 2019 OPD stops. People older than 71 accounted for only 132 OPD stops for the 2019 year (i.e., making up less than 1% of all stop instances). Notably, of these 132 OPD stops concerning older individuals, 111 instances involved people aged between 71-80 years and 21 instances involved people aged between 81-90 years.

With regard to fluency in the English language, it can also be observed that about 93% of people stopped by OPD in 2019 were fully fluent in English. The remaining roughly 7% of people stopped had limited fluency in English.

Further considering different demographic attributes and all possible reasons to be stopped by police, it can be concluded that roughly 11% of people who experienced police stops by OPD in 2019 had some sort of disability. Such instances involve people with mental health conditions, speech impairments, developmental disabilities, multiple disabilities, and other unidentified disabilities.

Finally, considering OPD police stops and LGBT identity, it can be observed that an overwhelming majority of people stopped in 2019 did not have LGBT identities (~99%). Of the people stopped by OPD in 2019, only about 1% had an LGBT identity.

Police Stops and Handcuffs

Data indicates that for all police stop reasons and considering all demographics, handcuffs were used 40% of the time by OPD in 2019.

Police Stops and Drugs Found

With regard to drugs and all OPD 2019 stops, overall and considering all demographics, it can be observed that drugs were found only 6% of the time.

`53:T4e1,html`In diving deeper into the data and considering individual reasons for police stops, instances of handcuffs being used, and instances of drugs being found with regard to different demographic characteristics, a richer understanding of the data can be developed. As such, it is recommended that further exploratory analysis, involving further interpretation of the provided figures in this Observable notebook, is conducted. In addition, it is highly recommended that U.S. Census Bureau data related to the demographics of Oakland, California also be considered in further exploratory analysis. In doing so, potential discrepancies and issues of concern in OPD’s police stopping behavior relative to the city population could be detected.

In addition, and as outlined in the Appendix, while the provided data gathered through California’s Racial and Identity Profiling Act of 2015 (RIPA) (i.e., Assembly Bill (AB) 953) is comprehensive in theory, much of the recorded data concerning unique IDs for police stop instances and person stopped is incomplete. This serves as a limitation for more rigorous future analysis, and as such and if possible, it is recommended that more complete data be requested from OPD.

`54:T189a,html`1.0 Assumptions

In conducting this preliminary analysis, no assumptions have been made.

2.0 Data

All data used to conduct this analysis was provided by PPIC and outlines collected police stop data according to the Racial and Identity Profiling Act of 2015 (RIPA) (i.e., Assembly Bill (AB) 953) in the state of California for the 2019 year.

Notably, all demographic data included in this dataset is "perceived", meaning that individuals who were involved in police stops were not able to self-identify. Instead, the officer involved with the police stop recorded each demographic element as they perceived the stopped individual. This poses as a limitation for this study.

2.1 Errors / Anomalies in the Raw Data Provided

1) As indicated by the provided RIPA Data Documentation, the DOJ_RECORD_ID data element is intended to represent "a unique system-generated incident identification number". Given this information, it would be assumed that each of the unique 25,228 entries in the raw data would each have a corresponding unique DOJ_RECORD_ID. However, upon a preliminary inspection, only 21,820 unique DOJ_RECORD_ID's exist in the raw data. Given that every single entry has a corresponding value for DOJ_RECORD_ID, it is evident that the DOJ_RECORD_ID elements are not unique to each police stop incident. Some DOJ_RECORD_ID elements are used multiple times for different police stop incidents. Upon further inspection, it is evident that 2,402 DOJ_RECORD_ID values appear more than once in the raw data. Thus, this presents as an error in the recording of this data. This may also serve as a limitation for more rigorous future analysis. As such, it is recommended that the DOJ_RECORD_ID values are re-recorded properly.

2) As also indicated by the provided RIPA Data Documentation, the PERSON_NUMBER data element is supposed to represent "a unique system-generated person identification number". As such, it would be assumed that each person (i.e., each citizen stopped by police) would each have their own unique value for this element. However, upon a preliminary inspection of the data, only 10 unique PERSON_NUMBERs exist. This clearly represents an error in the recording of this data, because it is highly unlikely that only 10 individuals were ever stopped by the police, each roughly 2,500 times, in a single year. Such instance also acts as a limitation for deeper analysis. In order to enrich future studies, proper recording of PERSON_NUMBER is highly recommended.

3) Notably, while the analysis requested concerns only OPD’s 2019 stopping behavior and while the raw data provided is named “RIPA-Oakland PD-2019.csv”, this data contains information concerning not just OPD but also agencies related to San Diego and the California High Patrol. Only 24,395 of the 25,228 entries concern OPD. It is clear that the addition of other agencies was not intentional, and in order to compensate for this fact, these entries have been dropped so that they are not included in the analysis.

4) An error in the raw data exists relative to the recorded perceived gender (i.e., G_FULL). According to the RIPA Data Documentation, potential values in the raw data that correspond with the data element G_FULL are 1, 2, 3, 4, and 5. However, there are 13 instances of the value "99" present in the raw data. As such, an error is clearly present, and 13 police stop incidents do not have an associated perceived gender. In order to compensate for this, an additional value related to undefined gender has been included in the analysis. However, this data recording error serves as a limitation.

5) Similarly, an error in the raw data exists relative to recorded reason for the police stop (i.e., REASON_FOR_STOP). According to the RIPA Data Documentation, potential values in the raw data that correspond with the data element REASON_FOR_STOP are 1, 2, 3, 4, 5, 6, 7, and 8. However, there are 13 instances of the value "99" and 6 instances of the value "999" present in the raw data. As such, an error is clearly present, and a total of 19 police stop incidents do not have an associated reason for the stop. In order to compensate for this, an additional value related to undefined reason for stop has been included in the analysis. However, this data recording error serves as a limitation.

6) With regard to the AGE data element in the raw data, outliers, which likely represent errors, are present. As indicated by the RIPA Data Documentation, the values recorded for this data element represent the perceived age of the person stopped by police. However, values of both "1 year old" and "150 years old" are present. Seeing as 1) babies are not usually stopped by the police and 2) people do not often live to be older than 100 years old, these outliers are anomalies that likely represent errors in the recording of that data. As such, these data points have been excluded from the analysis. Regardless, however, such error serves as a limitation.

3.0 Analysis

In order to analyze this data, the Python and JavaScript programming languages were used. Observable Plot was used to compile all analysis and visualizations in an Observable notebook.

3.1 Exploratory Analysis

With Python, data was wrangled, analyzed, and summarized. Exploratory data analysis was also performed in order to visually identify relationships between different demographic characteristics, the reason for different types of police stops, whether or not the citizen concerned in a police stop was handcuffed, and whether or not drugs were found during police stops. JavaScript was used to create the accompanying data visualizations that outline this analysis.

Click here to access the code for the analysis.

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Trends in Police Stops

Identifying Trends in Oakland Police Department Stop Incident Data for the 2019 Year

Prepared for Public Policy Institute of California (PPIC) by Caroline Cullinan
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Total Police Stop Incidents

Reason for Police Stop Incidents by Race / Ethnicity

Reason for Police Stop Incidents by Gender

Reason for Police Stop Incidents by Age

Reason for Police Stop Incidents by English Fluency Level

Reason for Police Stop Incidents by Disability

Reason for Police Stop Incidents by LGBT Identity

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"{\n\nconst total = d3.sum(stop_explore_view_lgbt, d=>d.value)\n\nconst plot = Plot.plot({\n marginLeft: 200,\n width: 1000,\n height:180,\n color: {\n range: [tol.QualLight[7], tol.QualLight[1]],\n },\n y: {\n label:\"LGBT Identity\"\n },\n x: {\n label:normalize?\"Proportion of Police Stops (%)\":\"Police Stop Count\",\n // tickFormat: normalize?d=>d+\"%\":d=>d,\n domain:normalize?[0,100]:[0, d3.max(stop_explore_view_lgbt, d=>d.value)],\n nice:true\n },\n caption:\"Oakland Police Department's 2019 stopping behavior by stop reason and perceived LGBT identity\",\n marks: [\n Plot.barX(stop_explore_view_lgbt, {\n x: d=>getValue(d, total, normalize), \n y: \"variable\", \n fill:\"variable\", \n sort: {y: \"x\", reverse: true},\n title: (d) =>\n `${Math.round(getValue(d, total, normalize))}${normalize?\"%\":\"\"} \\n police stops` // \\n makes a new line\n }),\n Plot.ruleX([0])\n ],\n tooltip: {\n fill: \"\",\n stroke: \"black\",\n \"stroke-width\": \"3px\",\n }\n})\n return plot\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":944,"value":"md`### 1.2 Exploring Instances of Police Stops and Individuals Being Handcuffed by Demographics `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":965,"value":"viewof demographicHC = Inputs.select([\n \"All\",\n \"Race\",\n \"Gender\",\n \"Age Group\",\n \"Disability\",\n \"LGBT\",\n \"English Fluency Level\"], {label: \"Demographic:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":968,"value":"viewof normalizeHandcuffs = Inputs.select([\n true,\n false,\n \n], {label: \"Show Police Stops as Proportions:\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":962,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":940,"value":"md`### 1.3 Exploring Instances of Drugs Found During Police Stops by Demographics `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":725,"value":"viewof demographic = Inputs.select([\n \"All\",\n \"Race\",\n \"Gender\",\n \"Age Group\",\n \"Disability\",\n \"LGBT\",\n \"English Fluency Level\"], {label: \"Demographic:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":808,"value":"viewof normalizeDrugs = Inputs.select([\n true,\n false,\n \n], {label: \"Show Police Stops as Proportions:\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"md`## 2.0 Interpreting the Overall Analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1071,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"md`## 3.0 Recommendations`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1061,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"md`#### Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"data_oakland = await FileAttachment(\"data_oak.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"data_oakland","database":"$undefined"},{"id":154,"value":"stop_explor = await FileAttachment(\"stop_ex.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"stop_explor","database":"$undefined"},{"id":151,"value":"stop_explor_tidy = await FileAttachment(\"stop_ex_tidy.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"stop_explor_tidy","database":"$undefined"},{"id":147,"value":"handcuff_explor = await FileAttachment(\"handcuff_ex.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"handcuff_explor","database":"$undefined"},{"id":143,"value":"handcuff_explor_tidy = await FileAttachment(\"handcuff_ex_tidy.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"handcuff_explor_tidy","database":"$undefined"},{"id":139,"value":"drug_explor = await FileAttachment(\"drug_ex.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"drug_explor","database":"$undefined"},{"id":135,"value":"drug_explor_tidy = await FileAttachment(\"drug_ex_tidy.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"drug_explor_tidy","database":"$undefined"},{"id":843,"value":"md`### Variables`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1044,"value":"\nstop_explore_view_all = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"Police Stop Reason\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"Police Stop Reason\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"stop_explore_view_race = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"Race\"&& d['variable']!==\"BIPOC\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"Race\"&& d['variable']!==\"BIPOC\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":422,"value":"stop_explore_view_gender = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"Gender\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"Gender\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":543,"value":"stop_explore_view_age_group = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"Age Group\"&& d['variable']!==\"less than 10 years\"&& d['variable']!==\"more than 100 years\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"Age Group\"&& d['variable']!==\"less than 10 years\"&& d['variable']!==\"more than 100 years\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":444,"value":"\nstop_explore_view_english_fluency_level = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"English Fluency Level\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"English Fluency Level\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":600,"value":"stop_explore_view_disability = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason &&d[\"category\"] === \"Disability\"&& d['variable']!==\"disabled\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"Disability\"&& d['variable']!==\"disabled\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":561,"value":"stop_explore_view_lgbt = stop_reason!==\"All\"?\n stop_explor_tidy.filter(d=>d[\"reason for stop\"]===stop_reason && d[\"category\"] === \"LGBT\").sort((a,b)=>d3.descending(+a.value,+b.value))\n :\n d3.rollups(stop_explor_tidy.filter(d=>d[\"category\"] === \"LGBT\").sort((a,b)=>d3.descending(+a.value,+b.value)), v => d3.sum(v, d => d.value), d => d.variable).map(d=>{return {\n variable:d[0],\n value:d[1]\n}})\n \n //","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":743,"value":"md`### Functions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":747,"value":"getValue = (x, total, norm) => {\n return norm?x[\"value\"]/total*100:x['value']\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":797,"value":"getSpecialFilter = (d, category) => {\n let filter = category === 'Disability' ? d['variable']!==\"disabled\"\n :category === \"Age Group\" ? d['variable']!==\"less than 10 years\"&& d['variable']!==\"more than 100 years\"\n :category === \"Race\" ? d['variable']!==\"BIPOC\": d === d\n\n return filter\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":768,"value":"getFilter = (d, category) => {\n let filter = category === 'Disability' ? d[\"category\"] === \"Disability\"&& d['variable']!==\"disabled\"\n :category === \"LGBT\" ? d[\"category\"] === \"LGBT\"\n :category === \"Age Group\" ? d[\"category\"] === \"Age Group\"&& d['variable']!==\"less than 10 years\"&& d['variable']!==\"more than 100 years\"\n :category === \"English Fluency Level\" ? d[\"category\"] === \"English Fluency Level\"\n :category === \"Gender\" ? d[\"category\"] === \"Gender\"\n :category === \"Race\" ? d[\"category\"] === \"Race\"&& d['variable']!==\"BIPOC\":null\n\n return filter\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"md`### Modules`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"// import {Plot, d3} from \"@observablehq/plot\"\nimport {Plot} from \"@mkfreeman/plot-tooltip\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"import { tol } from '@jotasolano/paul-tol-schemes'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"md`### Styling`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"html`\n