{ "Dictionary": { "response": [ { "Name": "Unique Key", "Type": "Integer", "Label": "Unique Identifier", "Description": "A system-generated integer that uniquely identifies each record. All 1,000,000 values are distinct (cardinality equals the number of records)." }, { "Name": "Created Date", "Type": "DateTime", "Label": "Record Creation Timestamp", "Description": "The date and time when the record was created in the system. The most common dates are early 2013–2015, with a mode of January 24 2013 (347 occurrences). Approximately 99.73% of values fall into the top ten dates; the remaining 0.27% are distributed across many other dates." }, { "Name": "Closed Date", "Type": "DateTime", "Label": "Record Closure Timestamp", "Description": "The date and time when the record was closed or finalized. The majority (≈2.86%) of records have a null closure date; otherwise, dates cluster in late 2010–2014 with the mode on November 15 2010." }, { "Name": "Agency", "Type": "String", "Label": "Primary Agency", "Description": "The agency responsible for addressing the issue. NYPD is the most frequent (26.5% of records), followed by HPD, DOT, DSNY, and others." }, { "Name": "Agency Name", "Type": "String", "Label": "Agency Full Name", "Description": "Full official name of the agency handling the complaint. New York City Police Department dominates (26.5%)." }, { "Name": "Complaint Type", "Type": "String", "Label": "Complaint Category", "Description": "High‑level category of the complaint, e.g., Noise – Residential or HEAT/HOT WATER. Noise – Residential is most common (8.94%)." }, { "Name": "Descriptor", "Type": "String", "Label": "Detailed Descriptor", "Description": "Specific description of the issue, such as Loud Music/Party or ENTIRE BUILDING. Loud Music/Party accounts for 9.36%." }, { "Name": "Location Type", "Type": "String", "Label": "Physical Location Type", "Description": "The type of location where the complaint was reported (e.g., RESIDENTIAL BUILDING, STREET). Residential buildings comprise ~25.6%." }, { "Name": "Incident Zip", "Type": "String", "Label": "ZIP Code", "Description": "5‑digit ZIP code of the incident location. The most common codes are 11226 (1.71%) and 10467 (1.45%)." }, { "Name": "Incident Address", "Type": "String", "Label": "Street Address", "Description": "Full street address of the incident. Most addresses are null (17.47%); otherwise, they include typical New York street names." }, { "Name": "Street Name", "Type": "String", "Label": "Primary Street Name", "Description": "Main street where the complaint was filed; BROADWAY is the most frequent (0.97%)." }, { "Name": "Cross Street 1", "Type": "String", "Label": "First Cross Street", "Description": "Primary cross street intersecting at the incident location. The most common value is NULL, followed by BEND and BROADWAY." }, { "Name": "Cross Street 2", "Type": "String", "Label": "Second Cross Street", "Description": "Secondary cross street; NULL dominates (32.36%); BEND and BROADWAY are also common." }, { "Name": "Intersection Street 1", "Type": "String", "Label": "First Intersection Street", "Description": "Primary intersection street. BROADWAY appears in 3.76% of records; NULL is most frequent (76.74%)." }, { "Name": "Intersection Street 2", "Type": "String", "Label": "Second Intersection Street", "Description": "Secondary intersection street; BROADWAY accounts for 3.46%, with NULL again the most common." }, { "Name": "Address Type", "Type": "String", "Label": "Address Category", "Description": "Classification of the address type (e.g., ADDRESS, INTERSECTION). ADDRESS is by far the majority (71%)." }, { "Name": "City", "Type": "String", "Label": "City/Neighborhood", "Description": "City or neighborhood where the incident occurred. Brooklyn is most common (29.6%); NULL values appear in 6.2%." }, { "Name": "Landmark", "Type": "String", "Label": "Nearby Landmark", "Description": "Notable landmark near the incident; most records are null (91.3%)." }, { "Name": "Facility Type", "Type": "String", "Label": "Facility Category", "Description": "Type of facility involved (e.g., DSNY Garage, N/A). DSNY Garage is most frequent (3.23%)." }, { "Name": "Status", "Type": "String", "Label": "Current Status", "Description": "Operational status of the complaint. Closed dominates (95.25%); other statuses include Pending, Open, and In Progress." }, { "Name": "Due Date", "Type": "DateTime", "Label": "Expected Resolution Date", "Description": "Target date for resolution. Null values are common (64.78%). The most frequent non‑null dates are in early 2015–2016." }, { "Name": "Resolution Description", "Type": "String", "Label": "Resolution Narrative", "Description": "Textual description of how the complaint was resolved or the outcome. The leading resolution is a police response with no evidence found (9.14%)." }, { "Name": "Resolution Action Updated Date", "Type": "DateTime", "Label": "Last Resolution Update Timestamp", "Description": "Timestamp of the most recent update to the resolution action. NULL values are common (1.5%); otherwise, dates cluster around 2010–2016." }, { "Name": "Community Board", "Type": "String", "Label": "Community Board Number", "Description": "The community board responsible for the area. The majority of records are null (75%). When present, values range from 0 Unspecified to 12 Queens." }, { "Name": "BBL", "Type": "String", "Label": "Borough‑Block‑Lot Identifier", "Description": "Unique property identifier. Null values appear in 24.3%; otherwise, BBLs are distributed across many properties." }, { "Name": "Borough", "Type": "String", "Label": "NYC Borough", "Description": "The borough where the incident occurred (Brooklyn, Queens, Manhattan, Bronx). Brooklyn is most common (29.6%)." }, { "Name": "X Coordinate (State Plane)", "Type": "Integer", "Label": "X Coordinate (State Plane)", "Description": "East–west coordinate in the State Plane system; null values are 8.53%. The majority of coordinates cluster around 1,020,000." }, { "Name": "Y Coordinate (State Plane)", "Type": "Integer", "Label": "Y Coordinate (State Plane)", "Description": "North–south coordinate in the State Plane system; null values are 8.53%. Typical values range near 200,000." }, { "Name": "Open Data Channel Type", "Type": "String", "Label": "Submission Channel", "Description": "Method used to submit the complaint (PHONE, ONLINE, MOBILE). PHONE accounts for 49.8% of submissions." }, { "Name": "Park Facility Name", "Type": "String", "Label": "Park Facility Name", "Description": "If applicable, the name of the park facility; most records are Unspecified (99.3%)." }, { "Name": "Park Borough", "Type": "String", "Label": "Park Borough", "Description": "Borough where a park facility is located. Brooklyn dominates (29.6%)." }, { "Name": "Vehicle Type", "Type": "String", "Label": "Vehicle Category", "Description": "Type of vehicle involved in the incident. The vast majority are null (99.97%); among those, Car Service is most frequent." }, { "Name": "Taxi Company Borough", "Type": "String", "Label": "Taxi Company Borough", "Description": "Borough where the taxi company operates; nearly all records are null (99.9%)." }, { "Name": "Taxi Pick Up Location", "Type": "String", "Label": "Taxi Pick‑Up Location", "Description": "Location where a taxi was picked up. Null values dominate (99.2%); other locations include airports and intersections." }, { "Name": "Bridge Highway Name", "Type": "String", "Label": "Bridge/Highway Name", "Description": "Name of the bridge or highway; most records are null (99.77%). When present, common names include Belt Pkwy and BQE/Gowanus Expwy." }, { "Name": "Bridge Highway Direction", "Type": "String", "Label": "Bridge/Highway Direction", "Description": "Direction of travel on the bridge/highway; null values dominate (99.77%). Common directions include East/Long Island Bound and North/Bronx Bound." }, { "Name": "Road Ramp", "Type": "String", "Label": "Road Ramp Type", "Description": "Classification of a road ramp; null values are 99.77%. The most common non‑null value is Roadway (17.3%)." }, { "Name": "Bridge Highway Segment", "Type": "String", "Label": "Bridge/Highway Segment", "Description": "Specific segment of a bridge or highway; null values are 99.76%. Common segments include Ramps and Roadways." }, { "Name": "Latitude", "Type": "Float", "Label": "Geographic Latitude", "Description": "Latitude coordinate in decimal degrees; null values account for 25.47% of records. Typical latitudes range from 40.11° to 40.91°." }, { "Name": "Longitude", "Type": "Float", "Label": "Geographic Longitude", "Description": "Longitude coordinate in decimal degrees; null values are 25.47%. Values span from -77.52° to -73.70°." }, { "Name": "Location", "Type": "String", "Label": "Latitude/Longitude Pair", "Description": "Combined latitude and longitude as a string. Null values cover 25.47%; when present, the format is \"(lat, lon)\"." } ], "reasoning": "We need to produce Data Dictionary JSON with fields: Name, Type, Label, Description.\n\nUse Summary Statistics for data type and cardinality etc. Use frequency distribution for label maybe? They want human-friendly label and description. We can derive label from field names cleaned. Description should be full explanation using stats and freq top values. Provide JSON array of objects. Ensure valid JSON surrounded by ```json.\n\nWe have many fields (~70). Might be large but acceptable. We'll craft concise labels: e.g., Unique Key, Created Date etc.\n\nDescription: mention type, cardinality, uniqueness ratio, maybe top value.\n\nWe need to use provided stats for each field: type is from 'type' column. For string types, label as human-friendly.\n\nLet's produce JSON with 70 objects.\n\nWill craft description generically.\n\nWe'll reference frequency distribution: e.g., \"Unique Key\" all unique values; Created Date has most frequent dates; etc.\n\nLet's write code." }, "Description": { "response": "**Description**\n\nThe dataset contains one million records of NYC 311 service requests collected from January 1 2010 to December 23 2020. Each record is uniquely identified by an integer key and includes a creation timestamp, optional closure date, agency handling the request, complaint category, descriptive text, and geographic information (street address, ZIP code, latitude/longitude). The most common agencies are NYPD, HPD, and DOT, while “Noise – Residential” and “HEAT/HOT WATER” dominate the complaint types. Geographic fields show a high proportion of missing or “Other” values, especially for precise street addresses, coordinates, and ZIP codes, reflecting incomplete reporting. The dataset is largely non‑duplicate (unique key) but contains substantial nulls in status‑related fields such as closed date and due date.\n\n**Notable Characteristics**\n\n- **Unique identifiers:** Every record has a distinct `Unique Key`; no duplicate rows exist.\n- **Missingness:** \n - `Closed Date` is null for ~2.9 % of records, while `Due Date` is missing for ~64.8 %. \n ‑ Many geographic fields (`Incident Address`, `Latitude/Longitude`, `ZIP code`) have >70 % “Other” or null values, indicating incomplete location reporting. \n- **High cardinality / sparsity:** Fields such as `Agency`, `Descriptor`, and `Location Type` contain a large number of low‑frequency categories (“Other (N)”), leading to sparse distributions. \n- **Temporal spread:** Creation dates range from 2010‑01‑01 to 2020‑12‑23; the dataset captures an 11‑year period with seasonal peaks in certain complaint types. \n- **Potential outliers:** Extreme latitude/longitude values beyond typical NYC bounds (e.g., coordinates near –77 °) suggest data entry errors or misclassifications. \n- **PII considerations:** Street addresses, ZIP codes, and precise geographic coordinates can be used to approximate individual locations; users should apply de‑identification or aggregation when sharing or publishing this data. \n- **Data quality issues:** The presence of many “Other (N)” entries indicates a need for better standardization in free‑text fields such as `Descriptor` and `Complaint Type`. \n\n---\n\n*Footnote: Generated by qsv's describegpt command using openai/gpt-oss-20b on 2025-08-28T03:03:35.660578+00:00*", "reasoning": "We need to produce description 1-8 sentences about dataset as a whole, not per field. Use summary statistics and frequency distribution data for guidance. Provide Notable Characteristics bullet list: outliers, missing values, duplicates (Unique Key all unique), PII? Possibly addresses and coordinates could be PII. Data quality issues: many nulls in Created Date? Actually Created Date has 0% null. But Closed Date has many null (2.86%). Also many fields have high percentage of other. Many location fields missing or \"Other\" large.\n\nAlso note that Unique Key all unique. The dataset seems to be NYC 311 complaint data. Contains 1,000,000 records.\n\nWe need footnote placeholder: GENERATED_BY_SIGNATURE\n\nLet's craft description: mention size, time span 2010-2020, major agencies, top complaint types, high missingness in closed date and location fields etc.\n\nAlso mention that dataset includes geographic coordinates; addresses are partially missing but can be used for mapping. Provide bullet list of notable characteristics: duplicates? None due to unique key; but many records with null values; \"Other\" categories indicate data sparsity; potential outliers: extreme coordinates or dates beyond range? Created date min 2010, max 2020.\n\nAlso PII: addresses, phone numbers maybe not present. But location addresses could be considered sensitive.\n\nLet's craft final answer." }, "Tags": { "response": [ "nyc_311", "public_complaints", "city_governance", "open_data", "geospatial_data", "noise_complaint", "housing_and_building", "transportation_infrastructure", "environmental_protection", "taxi_service", "consumer_feedback", "time_series_analysis", "data_quality", "urban_planning", "municipal_services" ], "reasoning": "We need to generate no more than 15 most thematic tags in descending order of importance, lowercase only and use underscores.\n\nBased on dataset: NYC 311 complaints dataset. So tags like \"nyc_311\", \"complaint_data\", \"public_services\", \"city_governance\", \"open_data\", \"consumer_complaints\", \"noise_complaints\", \"housing_and_building\", \"transportation_infrastructure\", \"environmental_protection\", \"taxi_service\", \"geospatial_data\", \"data_quality\", \"time_series\".\n\nWe need to consider summary stats and frequency distribution: high cardinalities, missing values, top categories.\n\nBut tags should be thematic; not field names. So 15 tags.\n\nLet's produce JSON array.\n\nMake sure lower case and underscores.\n\nOrder by importance descending.\n\nPotential priority:\n\n1. nyc_311 (most specific)\n2. public_complaints\n3. city_governance\n4. open_data\n5. geospatial_data\n6. noise_complaint\n7. housing_and_building\n8. transportation_infrastructure\n9. environmental_protection\n10. taxi_service\n11. consumer_feedback\n12. time_series_analysis\n13. data_quality\n14. urban_planning\n15. municipal_services\n\nBut maybe we should reflect dataset: majority complaint types noise, heat/hot water, etc.\n\nAlso top agencies: NYPD, HPD, DOT.\n\nSo tags like \"public_safety\", \"housing_and_building\", \"transportation\".\n\nLet's produce final JSON." } }