Homelessness as an Impediment to Urban Revitalization: the Case of Dallas, Texas
Bibliographic record
Abstract
Homelessness has long been recognized as a serious problem in many American cities, and Dallas in no exception. What’s more, the homeless tend to congregate in the downtown districts (DD) since most service providers are also located in the urban core. Though homelessness is typically considered a social problem, it also has economic consequences. The latest homeless census for the city of Dallas totaled 6,000, and annual outlays by governmental, non-profit, charitable, and faith-based organizations to provide them with services probably exceed $50 million. This estimate doesn’t include thousands of volunteer hours. But the true economic cost of homelessness is much greater. A survey of downtown business owners found that the presence of homeless persons is having a negative affect on their operations and burdening many of them with additional costs for security and cleaning. A majority of retail respondents report that proximity to the homeless was scaring off customers and reducing their sales. An examination of downtown properties using Dallas County Appraisal District (DCAD) records reveals that average values in the southern sector, where most of the homeless are concentrated, are well below those in the northern half of downtown. Consequently, the City of Dallas, Dallas County, and the Dallas Independent School District are losing $2.4 million per year due to valuation disparities from a lack of development in the southern half of the DD. What’s more, we estimate the southern half of downtown can potentially support almost 2.2 million square feet of additional commercial, office and residential space. This development scenario would create more than 5,000 new jobs and generate about $6.6 million per year for local taxing entities. But the revitalization of Dallas’ DD, an avowed goal of the city’s political and business leaders, will not be fully realized until a comprehensive plan for improving homeless services is developed and implemented. Most importantly, the proposed central intake facility should be located away from—but close to—the downtown district. In this regard, the City of Miami can serve as a model. Miami has significantly reduced the visible homeless count and greatly improved the delivery of services. By creating an umbrella agency to oversee all homeless programs—whether provided by government, voluntary or faith-based institutions—the city has avoided duplication and overlap of services. Significantly, Miami has located both of its central intake facilities, known as Homeless Assistance Centers (HACs), away from their downtown district. Miami’s businesses community has recognized that reducing homelessness is a community and economic development issue as well as a social problem, and to that end they have contributed about $50 million over the past decade. The results are tangible, as evidenced by the construction boom currently underway in Miami’s downtown. As with Miami, an effective approach for dealing with Dallas’ homeless population must include greater participation and support by the region’s business leaders. Homelessness has significant economic as well as social consequences for the City of Dallas. While offering our compassion to the homeless, we should also acknowledge that the overwhelming presence of homeless persons on the streets of downtown has negative economic impacts on individual businesses, the prospects for redevelopment, and the city’s finances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".