---
license: mit
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- nonprofit
pretty_name: hopecorner
size_categories:
- 10K<n<100K
configs:
- config_name: bicycle_repairs
  data_files:
  - split: train
    path: data/bicycle_repairs_rows.csv
- config_name: blocked_slots
  data_files:
  - split: train
    path: data/blocked_slots_rows.csv
- config_name: daily_notes
  data_files:
  - split: train
    path: data/daily_notes_rows.csv
- config_name: donations
  data_files:
  - split: train
    path: data/donations_rows.csv
- config_name: guest_proxies
  data_files:
  - split: train
    path: data/guest_proxies_rows.csv
- config_name: guests
  data_files:
  - split: train
    path: data/guests_rows.csv
- config_name: haircut_visits
  data_files:
  - split: train
    path: data/haircut_visits_rows.csv
- config_name: laundry_bookings
  data_files:
  - split: train
    path: data/laundry_bookings_rows.csv
- config_name: meal_attendance
  data_files:
  - split: train
    path: data/meal_attendance_rows.csv
- config_name: shower_reservations
  data_files:
  - split: train
    path: data/shower_reservations_rows.csv
---

# Dataset Card for Hope's Corner

Anonymized operational data from [Hope's Corner](https://hopescorner.org/), a non-profit in Mountain View, CA that provides meals, laundry, showers, haircuts, and bicycle repair to unhoused and low-income community members. The dataset covers guest visits and service records across ten relational tables, linked by anonymized guest IDs.

## Dataset Details

### Dataset Description

Hope's Corner runs drop-in services for community members who are unhoused or low-income. Staff log each service interaction in an internal system, recording which guests attended, what services they used, and operational notes. This dataset is an anonymized export of those records, structured as relational CSV files. It covers meal attendance, laundry and shower bookings, haircut visits, bicycle repairs, donations received, and guest demographic information.

The two main research questions this dataset is designed to support are forecasting daily meal demand and identifying gaps or inequities in service access across different guest populations.

- **Curated by:** Hope's Corner, Mountain View, CA
- **Language(s) (NLP):** English
- **License:** MIT

### Dataset Sources

- **Source:** Internal operational records from Hope's Corner
- **Repository:** `data/` directory, one CSV per table

## Uses

### Direct Use

- **Meal demand forecasting:** Predict daily attendance using historical check-in records, and explore external predictors like weather (rain, extreme temperatures) and calendar events (federal holidays, local events).
- **Service gap analysis:** Identify which guest populations use which services, spot underserved groups, and examine differences in wait times or access across demographics and housing statuses.
- **Operational planning:** Understand slot utilization for laundry, showers, and haircuts to inform scheduling decisions.
- **Donation tracking:** Analyze the volume and type of donations over time relative to service demand.

### Out-of-Scope Use

- Should not be used to re-identify individual guests. Records are anonymized and must remain so.
- Not suitable for any use that could be used to surveil, profile, or target unhoused individuals.
- Not a representative sample of all unhoused individuals in Mountain View or the broader region; it reflects only guests who visited Hope's Corner.

## Dataset Structure

### Tables

The dataset is organized as ten relational CSV files in the `data/` directory, all linked by anonymized guest UUIDs.

| Table | File | Description |
|---|---|---|
| `guests` | `guests_rows.csv` | One row per guest; core demographic and status fields |
| `guest_proxies` | `guest_proxies_rows.csv` | Records when one guest picks up services on behalf of another |
| `meal_attendance` | `meal_attendance_rows.csv` | Each meal service transaction, including meal type and quantity |
| `laundry_bookings` | `laundry_bookings_rows.csv` | Laundry slot reservations with date, slot label, and status |
| `shower_reservations` | `shower_reservations_rows.csv` | Shower slot reservations with date, slot label, and status |
| `haircut_visits` | `haircut_visits_rows.csv` | Individual haircut service visits with timestamps |
| `bicycle_repairs` | `bicycle_repairs_rows.csv` | Bicycle repair requests with repair types and status |
| `donations` | `donations_rows.csv` | Donation records by type, item, weight, and donor |
| `daily_notes` | `daily_notes_rows.csv` | Staff notes by date and service type |
| `blocked_slots` | `blocked_slots_rows.csv` | Dates and times when service slots were unavailable |

### Schema

**guests**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `external_id` | string | External reference ID |
| `housing_status` | string | Guest's housing situation |
| `age_group` | string | Age bracket |
| `gender` | string | Gender |
| `location` | string | General location or neighborhood |
| `notes` | string | Staff notes |
| `bicycle_description` | string | Description of guest's bicycle, if any |
| `ban_reason` | string | Reason for any service ban |

**guest_proxies**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest receiving the service |
| `proxy_id` | uuid | Guest picking up on their behalf |

**meal_attendance**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest served |
| `picked_up_by_guest_id` | uuid | Guest who physically picked up the meal (may differ from guest_id) |
| `meal_type` | string | Type of meal (e.g., breakfast, lunch) |
| `quantity` | integer | Number of meals |
| `served_on` | date | Date of service |

**laundry_bookings**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest who booked |
| `scheduled_for` | date | Booking date |
| `slot_label` | string | Time slot identifier |
| `status` | string | Booking status (e.g., completed, no-show, cancelled) |

**shower_reservations**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest who reserved |
| `scheduled_for` | date | Reservation date |
| `slot_label` | string | Time slot identifier |
| `status` | string | Reservation status |

**haircut_visits**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest served |
| `served_at` | datetime | Timestamp of service |
| `service_date` | date | Date of service |

**bicycle_repairs**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `guest_id` | uuid | Guest requesting repair |
| `requested_at` | datetime | Timestamp of request |
| `repair_types` | string | Types of repairs requested |
| `status` | string | Repair status |

**donations**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `donation_type` | string | Category of donation |
| `item_name` | string | Specific item donated |
| `weight_lbs` | float | Weight in pounds |
| `donor` | string | Donor name or identifier |
| `donated_at` | datetime | Timestamp of donation |

**daily_notes**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `note_date` | date | Date the note was written |
| `service_type` | string | Service the note refers to |
| `note_text` | string | Staff note content |

**blocked_slots**

| Field | Type | Description |
|---|---|---|
| `id` | uuid | Primary key |
| `service_type` | string | Service type affected |
| `slot_time` | string | Time slot that was blocked |
| `date` | date | Date of the blocked slot |

### Entity Relationship Diagram

```mermaid
erDiagram
    GUESTS {
        uuid id PK
        string external_id
        string housing_status
        string age_group
        string gender
        string location
        string notes
        string bicycle_description
        string ban_reason
    }
    GUEST_PROXIES {
        uuid id PK
        uuid guest_id FK
        uuid proxy_id FK
    }
    MEAL_ATTENDANCE {
        uuid id PK
        uuid guest_id FK
        uuid picked_up_by_guest_id FK
        string meal_type
        int quantity
        date served_on
    }
    LAUNDRY_BOOKINGS {
        uuid id PK
        uuid guest_id FK
        date scheduled_for
        string slot_label
        string status
    }
    SHOWER_RESERVATIONS {
        uuid id PK
        uuid guest_id FK
        date scheduled_for
        string slot_label
        string status
    }
    HAIRCUT_VISITS {
        uuid id PK
        uuid guest_id FK
        datetime served_at
        date service_date
    }
    BICYCLE_REPAIRS {
        uuid id PK
        uuid guest_id FK
        datetime requested_at
        string repair_types
        string status
    }
    DONATIONS {
        uuid id PK
        string donation_type
        string item_name
        float weight_lbs
        string donor
        datetime donated_at
    }
    DAILY_NOTES {
        uuid id PK
        date note_date
        string service_type
        string note_text
    }
    BLOCKED_SLOTS {
        uuid id PK
        string service_type
        string slot_time
        date date
    }

    GUESTS ||--o{ GUEST_PROXIES : "guest_id / proxy_id"
    GUESTS ||--o{ MEAL_ATTENDANCE : "guest_id / picked_up_by_guest_id"
    GUESTS ||--o{ LAUNDRY_BOOKINGS : "guest_id"
    GUESTS ||--o{ SHOWER_RESERVATIONS : "guest_id"
    GUESTS ||--o{ HAIRCUT_VISITS : "guest_id"
    GUESTS ||--o{ BICYCLE_REPAIRS : "guest_id"
```

## Dataset Creation

### Curation Rationale

Hope's Corner staff collect service data as part of daily operations, but that data has rarely been used for analysis. This dataset was exported and anonymized to support two specific research questions: forecasting how many meals will be needed on a given day, and understanding whether all guest populations have equal access to services. Both have direct operational value for a small non-profit with limited staff and resources.

### Source Data

#### Data Collection and Processing

Records were exported from Hope's Corner's internal management system. Guest identifiers were replaced with random UUIDs prior to export. No names, contact information, or other direct identifiers are present in the dataset. Each table was exported as a separate CSV file.

#### Who are the source data producers?

Data were recorded by Hope's Corner staff during normal service operations in Mountain View, CA.

### Annotations

#### Annotation process

No annotations have been applied. All fields reflect operational records as entered by staff.

#### Who are the annotators?

N/A

#### Personal and Sensitive Information

This dataset covers a vulnerable population. Although guest records are anonymized with UUIDs, the combination of housing status, age group, gender, location, and service history could in principle narrow down individuals in a small community. Handle with care:

- Do not attempt to re-identify guests.
- Do not share individual-level records outside of research or analysis contexts covered by your data use agreement with Hope's Corner.
- The `notes` and `ban_reason` fields in `guests` may contain sensitive staff observations and should be treated accordingly.

## Bias, Risks, and Limitations

- **Self-selected population:** This dataset only covers guests who visited Hope's Corner. It does not represent all unhoused or low-income individuals in Mountain View.
- **Recording gaps:** Not every interaction may have been logged, particularly during busy service periods. Attendance counts may undercount actual demand.
- **Demographic fields are categorical:** `housing_status`, `age_group`, and `gender` use a fixed set of categories that may not capture the full range of guest situations.
- **Proxy pickups:** Meal attendance records can reflect a proxy picking up for multiple guests, which requires care when counting unique individuals served.
- **Seasonal and event variation:** Meal demand is affected by weather, holidays, and local events. Models that don't account for these factors will likely underperform.

### Recommendations

Join `meal_attendance` to `guests` through `guest_id` to segment attendance by demographics. When modeling demand, incorporate external data sources such as weather records and a holiday calendar. Use `blocked_slots` and `daily_notes` to identify and exclude or annotate days with unusual operational circumstances.

## Citation [optional]

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary

| Term | Definition |
|---|---|
| **UUID** | Universally Unique Identifier, used here as an anonymized guest key |
| **Proxy** | A guest who picks up a meal or service on behalf of another registered guest |
| **Slot label** | The identifier for a specific time block within a service day (e.g., "9:00 AM") |
| **Housing status** | A categorical field describing a guest's current living situation (e.g., unhoused, transitional, low-income housed) |
| **Blocked slot** | A time slot that was unavailable for bookings due to staffing, closures, or other operational reasons |

## More Information

### Loading the Dataset

Each table is available as a separate configuration:

```python
from datasets import load_dataset

# Load guest records
guests = load_dataset("org/hopescorner", "guests")

# Load meal attendance
meals = load_dataset("org/hopescorner", "meal_attendance")

# Load laundry bookings
laundry = load_dataset("org/hopescorner", "laundry_bookings")
```

### Joining Tables with pandas

```python
import pandas as pd

guests = pd.read_csv("data/guests_rows.csv")
meals = pd.read_csv("data/meal_attendance_rows.csv")
laundry = pd.read_csv("data/laundry_bookings_rows.csv")

# Meal counts per guest with demographics
meal_counts = (
    meals.groupby("guest_id")["quantity"]
    .sum()
    .reset_index()
    .rename(columns={"quantity": "total_meals"})
)
guest_meals = guests.merge(meal_counts, left_on="id", right_on="guest_id", how="left")

# Daily meal totals (for demand forecasting)
meals["served_on"] = pd.to_datetime(meals["served_on"])
daily_totals = meals.groupby("served_on")["quantity"].sum().reset_index()
daily_totals.columns = ["date", "total_meals"]
```

## Dataset Card Authors

Hope's Corner, Mountain View, CA

## Dataset Card Contact

[More Information Needed]