RoofDignity Counts

OPERATIONAL BLUEPRINT

Data-Driven Homelessness Nonprofit

DOCUMENT: BP-001
VERSION: 1.0
CLASS: STRATEGIC

Scientific Framework for Evidence-Based Intervention

I

Research Design

Purpose

Define explicit, testable research questions and hypotheses.

Example Questions

  • •Which households are at imminent risk of homelessness in the next 3–12 months?
  • •How effective are specific interventions (rental assistance, mental health services, shelters) at preventing homelessness?
  • •Where do systemic gaps exist in housing and social services distribution?
  • •Which populations are disproportionately affected and underserved?

Hypotheses

Each question is tied to a measurable hypothesis.

H₁: Households with rent >50% of income and prior eviction history have >60% likelihood of entering homelessness within 6 months.
H₂: Participation in targeted rental assistance reduces the probability of homelessness by ≥30%.
II

Data Acquisition

A. Data Sources

Government
HUD PIT counts, CoC reports, county eviction filings, ACS rental data
Nonprofits
Shelter utilization, program participation, waitlists
Health/Social Services
ER visits, hospital discharge, mental health/substance intake records
Geospatial
Shelter/service locations, neighborhood characteristics, transit access
Community-Sourced
Surveys, interviews, focus groups

B. Acquisition Methodology

  • →Automated ETL pipelines for structured datasets (API ingestion, scheduled pulls).
  • →Manual validation for unstructured/community datasets.
  • →Metadata capture for source, date, granularity, and reliability.
III

Data Integration and Cleaning

Process

1
Deduplicate entries across datasets using unique identifiers.
2
Standardize variable names, units, and data types.
3
Handle missing values via imputation or flagging for exclusion.
4
Merge datasets via relational joins on household ID or geographic unit.
5
Log all transformations in version-controlled scripts for reproducibility.

Storage

Centralized relational database with spatial indexing (PostgreSQL + PostGIS).
IV

Analytical Pipeline

A. Descriptive Analysis

  • •Compute counts, distributions, averages, trends over time.
  • •Detect anomalies or outliers via statistical tests (e.g., Z-scores).

B. Comparative Analysis

  • •Compare intervention outcomes across populations, geographies, or program types.
  • •Statistical tests: t-tests, chi-square, ANOVA, effect sizes.

C. Predictive Modeling

Purpose
Identify at-risk households
Algorithms
Logistic regression, random forest, gradient boosting
Evaluation
ROC-AUC, precision, recall, cross-validation
Features:
Income, rental cost burden, prior eviction history, health encounters, service utilization.

D. Geospatial Analysis

  • •Hotspot detection using Kernel Density Estimation.
  • •Spatial correlation (Moran's I) to assess clustering of homelessness risk.
  • •Service accessibility mapping via buffer analysis and travel-time modeling.
V

Gap Analysis and Insight Generation

Process

  • •Compare predicted need vs. actual service coverage.
  • •Identify underserved populations or regions.
  • •Quantify misalignment between funding allocation and need.

Example Insight Structure

Evidence:"Neighborhood X has eviction rate in top 10% statewide, but only 2% of households receive rental assistance."
Action:Recommend targeted rental assistance and program expansion.
VI

Visualization & Communication

Visual Products

  • •Dashboards for real-time monitoring of risk and service capacity.
  • •Heatmaps for hotspots and service deserts.
  • •Summary reports with statistical evidence, effect sizes, and confidence intervals.

Audience-Specific Outputs

Policymakers
Actionable, high-level recommendations
Funders
ROI-focused metrics, program efficacy
Service Providers
Operational guidance and targeted interventions
VII

Dissemination & Impact Evaluation

Dissemination

Interactive online platforms, PDF reports, and targeted briefings.

Impact Metrics

  • •Reduction in predicted homelessness risk over time.
  • •Uptake of recommended interventions.
  • •Changes in shelter occupancy, program participation, and emergency service usage.

Feedback Loop

Continuous integration of new data to refine predictive models and update recommendations.
VIII

Quality Control and Scientific Rigor

  • ✓Full documentation and version control for all analyses.
  • ✓Model validation and sensitivity analysis.
  • ✓Data anonymization and compliance with HIPAA and privacy regulations.
  • ✓Peer review of findings before publication.
CLASSIFICATION: STRATEGIC FRAMEWORK
STATUS: ACTIVE
REVISION: 1.0