Description: A -Purpose: Original purpose of the data:This data was created by the San Francisco Department of Public Health (SFDPH) to update the original 2015 Vision Zero High Injury Network dataset. It identifies street segments in San Francisco that have a high number of fatalities and severe injuries. This dataset represents the streets that qualified. SFDPH shares this network with CCSF agencies to help inform where targeted safety measures could save lives and reduce injury severity. B - Methodology:The 2022 Vision Zero High Injury Network is derived from 2017-2021 severe and fatal injury data from Zuckerberg San Francisco General (ZSFG), San Francisco Police Department (SFPD), and the Office of the Medical Examiner (OME). ZSFG patient records and SFPD victim records were probabilistically linked through the Transportation Injury Surveillance System (TISS – contact devan.morris@sfdph.org for more information) using LinkSolv Software. Injury severity for linked SFPD/ZSFG records was reclassified based on injury outcome as determined by ZSFG medical personnel consistent with the Vision Zero Severe Injury Protocol (2022) while unlinked SFPD victim records were not changed. Severe injuries captured by ZSFG but not reported to SFPD were also included in this analysis. Intentional assaults, suicides, and homicides are excluded from the ZSFG-only patient dataset. Fatality data came from OME records that meet San Francisco’s Vision Zero Fatality Protocol.Only transportation-related injuries resulting in a severe injury or fatality were used in this analysis. Each street centerline segment block was converted into ~0.25 mile overlapping corridorized sections using ArcPy. These sections were intersected with the severe/fatal injury data. Only severe/fatal injuries with the same primary street as the corridorized section were counted for that section. The count of severe/fatal injuries was then normalized by the sections mileage to derive the number of severe/fatal injuries per mile. A threshold of ≥10 severe/fatal injuries per mile was used as the threshold to determine if a corridorized segment qualified for inclusion into the network. C - Frequency of updates:This dataset will be updated in 2023 using 2018-2022 injury when available from SFPD, ZSFG, and OME. This update schedule is tentative and pending approval from the SFDPH/SFMTA. Please contact Devan Morris (devan.morris@sfdph.org) for more information.D - Other critical information:The network represents a snapshot in time (2017-2021) where severe and fatal injuries are most concentrated. It may not reflect current conditions or changes to the city’s transportation system. Although prior incidents are often indicative of future incidents the Vision Zero High Injury Network is not a prediction (probability) of future risk. The High Injury Network approach is in contrast to risk-based approaches, which focus on locations determined to be more dangerous with increased risk or danger often calculated by dividing the number of injuries or collisions by vehicle volumes to estimate risk of injury per vehicle. The High Injury Network instead informs agencies of the streets where injuries, particularly severe and fatal, are concentrated in San Francisco based on injury counts; it is not an assessment of whether a street or particular location is dangerous. The 2022 Vision Zero Network is derived from only the worst injury outcomes (severe/fatal injuries) and may not cover locations with high numbers of less severe injury collisions. Hospital and emergency medical service records from which unreported injury and reclassified injury collisions are derived are protected by the Health Insurance Portability and Accountability Act and have strict confidentiality and privacy requirements. As of December 2022, SFPDH is working in conjunction with SFPDH’s Office of Compliance and Privacy Affairs, ZSFG and the SFMTA to determine at what level of aggregation SFDPH can share the data to be compliant with HIPAA. Location specific counts of severe/fatal injuries have been intentionally excluded from this dataset and a full dataset is pending review.A complete methodology is available here: https://www.visionzerosf.org/wp-content/uploads/2022/11/2022_Vision_Zero_Network_Update_Methodology.pdfE - Attributes:objectid: unique id for each recordgeom: GIS datatypecnn_sgmt_pkey: original street segment CNN duel carriage ways are representedstreeet_name: street name of corridorized sectionstreet_type: street name suffixfull_street_name: street name with prefix and suffix if applicableclass_code: generic class code for grouping streets (see DPW street centerline file)gis_length_miles: length of corridorized street segment as calculated in GISduel_car_way_yn: unique identifier for dual carriage waysf_node_cnn_intrsctn_fkey: start of block intersection node identifiert_node_cnn_intrsctn_fkey: end of block intersection node identifiercorrected_length_miles: length of street block accounting for dual carriage waysdirection: direction of traffic flow if street is a dual carriage wayfrom_intersection: starting point of block segmentto_intersection: ending point of block segmentgeom_Length: GIS length
Copyright Text: Devan Morris
devan.morris@sfdph,org
Center for Data Science
Population Health Division
Department of Public Health