Dataset Card for Hope's Corner

Anonymized operational data from Hope's Corner, 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

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]

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APA:

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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:

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

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

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