nyc311 0.3.0

Python toolkit for reproducible NYC 311 complaint analysis via a typed SDK and CLI.

nyc311

Python toolkit for reproducible NYC 311 complaint analysis via a typed SDK and CLI.

Authored by Blaise Albis-Burdige.

What this package does

nyc311 is the stable 0.3.x toolkit for turning NYC 311 service-request data into reproducible complaint-intelligence outputs and publication-quality statistical analyses.

It pairs a thin CLI with a typed SDK so the same workflow can run in batch jobs, scripts, notebooks, and consumer packages.

The current release line provides:

• load filtered NYC 311-style records from local CSV extracts or the live Socrata API
• derive deterministic first-pass topic labels for supported complaint types
• aggregate complaint topics by borough or community district
• measure topic-rule coverage and summarize resolution gaps
• score anomalies over aggregated topic summaries
• export CSV tables, boundary-backed GeoJSON, and markdown report cards
• expose the workflow through both a thin CLI and a composable functional SDK
• compose domain-specific factor pipelines over geographic units
• build balanced temporal panels with treatment-event modeling and inverse-distance spatial weights
• run interrupted-time-series, PELT changepoint, STL decomposition, Moran's I / LISA, and panel fixed/random-effects regressions
• causal inference: synthetic control, staggered difference-in-differences, event-study plots, regression discontinuity
• spatial econometrics: spatial lag and error models, geographically weighted regression
• equity analysis: Oaxaca-Blinder decomposition, Theil index, reporting-rate adjustment, latent reporting-bias EM
• diagnostics: seasonality-adjusted anomaly detection, power analysis / MDE calculator
• Bayesian: BYM2 small-area smoothing (behind nyc311[bayes])
• point processes: Hawkes self-exciting process for complaint contagion
• bulk-fetch full-city extracts split per borough with .meta.json integrity sidecars

Geography layer

nyc311.geographies is the 311-facing compatibility layer over nyc-geo-toolkit.

Use nyc311 when you want packaged NYC boundaries inside the 311 workflow. Use nyc-geo-toolkit directly when you only need the generic geography assets, normalization helpers, and boundary loaders.

Install

Choose the dependency footprint that matches your workflow:

pip install nyc311

For the full turnkey experience:

pip install "nyc311[all]"

For pandas-backed conversion helpers:

pip install "nyc311[dataframes]"

For geopandas-backed geography and spatial helpers:

pip install "nyc311[spatial]"

For plotting helpers:

pip install "nyc311[plotting]"

For plotting and exploratory analysis without the geospatial stack:

pip install "nyc311[science]"

For statistical modeling (interrupted time series, changepoints, STL, Moran's I, panel regressions):

pip install "nyc311[stats]"

For BYM2 small-area smoothing (PyMC):

pip install "nyc311[bayes]"

Why this exists

NYC 311 data is one of the richest public records of neighborhood quality-of-life complaints in the country, but much of the useful signal is locked inside short text fields such as complaint descriptors.

nyc311 turns those records into reusable outputs for civic analysis, journalism, and research through an explicit, testable workflow.

Core workflow

The current stable workflow is:

1. load records from a local CSV extract or a filtered Socrata slice
2. filter by date, geography, and complaint type
3. assign a first-pass topic label using explicit keyword rules
4. aggregate counts by borough or community district
5. export a CSV summary table or boundary-backed GeoJSON artifact

Supported topic extraction

The current rules-based topic extractor is implemented for the complaint types returned by nyc311.models.supported_topic_queries() (nine high-volume types including noise, rodents, street condition, heat/hot water, sanitary, and abandoned vehicles).

This is intentionally described as first-pass topic extraction, not clustering or advanced NLP.

Time series

Use nyc311.dataframes helpers for DatetimeIndex complaint counts and panel layouts:

from nyc311 import pipeline, presets
from nyc311.dataframes import to_timeseries, to_panel

records = pipeline.fetch_service_requests(
    filters=presets.brooklyn_borough_filter(
        start_date="2024-01-01",
        end_date="2024-12-31",
        complaint_types=("Noise - Residential", "Rodent"),
    ),
    socrata_config=presets.large_socrata_config(),
    cache_dir="./cache",
)

ts = to_timeseries(records, freq="W")
ts.plot(title="Weekly complaint volume")

panel = to_panel(records, freq="ME", geography="borough")
panel.xs("BROOKLYN")["Noise - Residential"].plot()

Data surface

• Socrata: dataset erm2-nwe9 (NYC 311 Service Requests from 2010 onward; tens of millions of rows). Use presets.large_socrata_config() for bulk pagination (default 5,000 rows per HTTP request) and nyc311.io.cached_fetch to stream pages to CSV without holding the full history in memory.
• Boundaries: borough, community district, council district, NTA, census tract, and ZCTA layers ship through nyc311.geographies (built on nyc-geo-toolkit).
• Caching: pass cache_dir and optional refresh / max_cached_records to pipeline.fetch_service_requests or io.load_service_requests so repeated runs reuse deterministic CSV snapshots under cache_dir.

Quick links

Docs: Home, Getting Started, CLI Reference, SDK Guide, Examples, Architecture, Contributing, Releasing, Changelog

Example

from datetime import date
from pathlib import Path

from nyc311 import analysis, export, models, pipeline

records = pipeline.fetch_service_requests(
    filters=models.ServiceRequestFilter(
        start_date=date(2025, 1, 1),
        end_date=date(2025, 1, 31),
        geography=models.GeographyFilter("borough", models.BOROUGH_BROOKLYN),
        complaint_types=("Noise - Residential",),
    ),
    socrata_config=models.SocrataConfig(page_size=250, max_pages=1),
)

export.export_service_requests_csv(
    records,
    models.ExportTarget("csv", Path("brooklyn-noise-snapshot.csv")),
)

assignments = analysis.extract_topics(records, models.TopicQuery("Noise - Residential"))
summary = analysis.aggregate_by_geography(assignments, geography="community_district")
export.export_topic_table(
    summary,
    models.ExportTarget("csv", Path("brooklyn-noise-topics.csv")),
)

CLI equivalent:

nyc311 fetch \
  --output brooklyn-noise-snapshot.csv \
  --complaint-type "Noise - Residential" \
  --geography borough \
  --geography-value BROOKLYN \
  --start-date 2025-01-01 \
  --end-date 2025-01-31 \
  --page-size 250 \
  --max-pages 1

nyc311 topics \
  --source brooklyn-noise-snapshot.csv \
  --complaint-type "Noise - Residential" \
  --geography community_district \
  --output brooklyn-noise-topics.csv

Live-data snapshot workflow:

nyc311 fetch \
  --output brooklyn-rodent-snapshot.csv \
  --complaint-type "Rodent" \
  --geography borough \
  --geography-value BROOKLYN \
  --start-date 2025-01-01 \
  --end-date 2025-01-31 \
  --page-size 500 \
  --max-pages 1

Factor pipeline

nyc311.factors composes domain-specific metrics over geographic units:

from datetime import date

from nyc311.factors import (
    ComplaintVolumeFactor,
    EquityGapFactor,
    FactorContext,
    Pipeline,
    ResponseRateFactor,
    SpatialLagFactor,
    TopicConcentrationFactor,
)

contexts = [
    FactorContext(
        geography="community_district",
        geography_value=cd,
        complaints=tuple(complaints),
        time_window_start=date(2024, 1, 1),
        time_window_end=date(2024, 12, 31),
    )
    for cd, complaints in records_by_cd.items()
]

result = (
    Pipeline()
    .add(ComplaintVolumeFactor())
    .add(ResponseRateFactor())
    .add(TopicConcentrationFactor())
    .run(contexts)
)
df = result.to_dataframe()  # one row per CD, one column per factor

See the SDK guide for the matching temporal-panel, statistical-modeling, and bulk-download examples.

Data assumptions

load_service_requests() currently supports:

• local CSV files
• live Socrata loading via SocrataConfig

CSV inputs use these columns:

• unique_key
• created_date
• complaint_type
• descriptor
• borough
• community_district or community_board

resolution_description is optional and loaded when present. It is currently used by the resolution-gap and report-card helpers, while topic extraction remains descriptor-driven.

Public package surface

The public API is organized around explicit namespaces:

• nyc311.models for dataclasses, constants, and configs
• nyc311.io for CSV and Socrata loading
• nyc311.analysis for topic extraction, coverage, gaps, and anomalies
• nyc311.geographies for the 311-facing compatibility layer over nyc-geo-toolkit
• nyc311.samples for packaged sample records and sample-aligned boundaries
• nyc311.export for CSV, GeoJSON, and report exports
• nyc311.pipeline for one-call workflow helpers
• nyc311.dataframes for optional pandas conversions
• nyc311.spatial for optional geopandas helpers
• nyc311.plotting for optional plotting helpers
• nyc311.presets for reusable filter and Socrata config builders
• nyc311.factors for the composable factor pipeline and built-in domain factors (including SpatialLagFactor and EquityGapFactor)
• nyc311.temporal for balanced panel datasets, treatment events, and inverse-distance spatial weights
• nyc311.stats for ITS, PELT changepoints, STL, Moran's I / LISA, panel fixed/random-effects regressions, synthetic control, staggered DiD, event study, RDD, spatial lag/error, GWR, Oaxaca-Blinder, Theil, reporting-bias adjustment, BYM2, Hawkes, anomaly detection, and power analysis
• nyc311.cli with the topics and fetch subcommands

Documentation

The hosted docs site is the canonical reference: nyc311.readthedocs.io.

If you are browsing in GitHub, the source docs live in docs/, including index.md, getting-started.md, cli.md, sdk.md, examples.md, api.md, architecture.md, and contributing.md.

Runnable examples live in examples/ as self-contained consumer projects. The examples/case_studies/ directory holds two complete research analyses on real NYC 311 data:

• Rat Containerization -- Evaluates the 2024 NYC containerization mandate using 81K real rodent complaints, the factor pipeline, STL decomposition, Moran's I, Theil inequality, synthetic control, staggered DiD, event study, RDD, and power analysis across 70 community districts.
• Resolution Equity -- Investigates whether resolution times vary by neighborhood demographics using 1M real 311 requests, two-way FE regression, Oaxaca-Blinder decomposition with ACS census data, spatial autocorrelation, ITS, and latent reporting-bias estimation.

For local preview:

make docs
make docs-build

Development

uv sync
uv sync --all-groups --all-extras
uv run --all-extras pytest -m "not integration"
uv run ruff check .
uv run ruff format --check .
uv run mypy
uv run mkdocs serve
uv run mkdocs build --strict
uv run python scripts/audit_public_api.py
uv run pytest -m "fetch and not integration"

License

MIT.