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Santa Clara County Continuum of Care | Findings
DataDive Santa Clara County Continuum of Care | Findings
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Reminder: Goals of DataDive
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To make homelessness rare, brief, and nonrecurring
Mission: To make homelessness rare, brief, and nonrecurring How do we predict housing demand? How do we determine what resources are necessary based on likely homeless scenarios in coming years?
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Approach
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Look deeper into historical inflow, by SCC key sub- populations.
Estimate unmet demand by equating Emergency Shelter use to ‘wait times.’ Identify trends in unmet demand by key sub-populations. Make predictions where possible....
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Audience Sub-Populations (Over 7-year data set)
Size % of Total Veterans 10k 30% Families with children 8k 20% Youth not part of household 3k 8% Chronically homeless 3.5k 9% Criminal justice involvement Disability but not chronically homeless 1.5k 4% (Any of the above) 19.2k 52% (Entire population) 37k 100% Overlapping Populations
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Analysis
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Inflow Analysis and Next Steps
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Inflow per Sub-population
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After Inflow: How Clients Flow Through SCC Services
Not Shown: Wait Time Analysis...
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Overall Consumption Trends
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Services Effectiveness - Leading Edge of Analysis!
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Services Effectiveness - Leading Edge of Analysis!
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Follow-up Opportunities
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Recommended Changes at SCC re: process / data collection / data management
Capture precisely the date from which clients qualify for a service rather than the date from which they receive service, so wait times can be calculated directly Follow-up Modeling Discrete event simulation modeling, given parameters generated this weekend Look at transitions among services Look at required resources Cohort analysis of recidivists versus non-recidivists Across all housing programs From emergency shelters only ⇒ Complete recommendations for changes in housing resources investments
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Questions?
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