# Transport Ridership Analytics: Numeric Row Ordering + Two-Measure Correlation

> Transport Ridership Analytics: Numeric Row Ordering + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).

Computes numeric row ordering and Pearson/Spearman correlation between passenger_count and service_count for up to 200 supplied transport ridership records, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-05-120?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transport-ridership-analytics-numeric-row-ordering-two-measure-8084712e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TfkPqhHaxaNKpdCN-tGGc

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability transport-ridership-analytics-numeric-row-ordering-two-measure-8084712e -d '<json body>'
```

Example prompt: I have a dataset of transit records — can you rank the rows by passenger_count and also compute the correlation between passenger_count and service_count? Here are 4 rows: row-1 group-a 2026-10-01 passengers=10 services=1, row-2 group-b 2026-10-02 passengers=20 services=2, row-3 group-a 2026-10-03 passengers=15 services=3, row-4 group-b 2026-10-04 passengers=30 services=4.

## When to prefer this

Choose this endpoint when you have pre-collected transport ridership tabular data (up to 200 rows) and need both a ranked ordering of rows by a numeric measure AND a statistical correlation between passenger_count and service_count in a single low-cost call. It is ideal for offline/batch analytical workflows where no live data lookup or prediction is needed, and where two reports for $0.01 USDC provides cost efficiency over separate API calls.

## Known failure modes

- Fewer than 1 row supplied — validation error returned
- More than 200 rows submitted — request rejected with item limit error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, region, date) — schema rejection
- Null values in both passenger_count and service_count — correlation report may be undefined or NaN
- Invalid field enum value — request rejected

## How this service works

Transport ridership — Numeric row ordering + Two-measure correlation: Numeric row ordering + Two-measure correlation for supplied transport ridership records (passenger_count, service_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two independent analytical reports: (1) a numeric row ordering of the supplied records ranked by the specified field (passenger_count or service_count), and (2) a correlation analysis between passenger_count and service_count across all submitted rows. All computation is performed locally on the supplied data — no external data sources, predictions, or live lookups are involved.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "region",
     "date",
     "passenger_count",
     "service_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "region": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "service_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "passenger_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "region": "group-a",
     "row_id": "row-1",
     "service_count": 1,
     "passenger_count": 10
    },
    {
     "date": "2026-10-02",
     "region": "group-b",
     "row_id": "row-2",
     "service_count": 2,
     "passenger_count": 20
    },
    {
     "date": "2026-10-03",
     "region": "group-a",
     "row_id": "row-3",
     "service_count": 3,
     "passenger_count": 15
    },
    {
     "date": "2026-10-04",
     "region": "group-b",
     "row_id": "row-4",
     "service_count": 4,
     "passenger_count": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "passenger_count",
    "service_count"
   ],
   "type": "string",
   "default": "passenger_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transport-ridership-analytics-numeric-row-ordering-two-measure-8084712e/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)