Overall Stats API #

The Overall Stats API provides project-wide statistics about Project Sidewalk's data collection efforts in San Pedro Garza García, México, including total distance covered, label counts by type, user participation metrics, and data quality indicators.

Overall Stats API Preview #

Below is a live preview of the Overall Stats API data for San Pedro Garza García, México:

Endpoint #

Retrieve overall statistics for the entire Project Sidewalk dataset.

GET /v3/api/overallStats

Quick Download #

Download overall statistics data directly in your preferred format:

Query Parameters#

This endpoint accepts the following optional query parameters.

Responses#

Success Response (200 OK)#

On success, the API returns an HTTP 200 OK status code and the requested data in the specified filetype format.

JSON Format (Default) #

Returns a JSON object with comprehensive project statistics:

{
  "launch_date": "2021-06-15T00:00:00Z",
  "avg_timestamp_last_100_labels": "2023-09-25T14:32:47Z",
  "km_explored": 1834.26,
  "km_explored_no_overlap": 1523.75,
  "km_explored_multiple_users": 412.08,
  "km_explored_single_user": 1111.67,
  "km_explorable": 1987.40,
  "km_by_status": {
    "open": 1987.40,
    "no_imagery": 142.13,
    "closed": 38.55,
    "disabled": 6.20
  },
  "user_counts": {
    "all_users": 4287,
    "labelers": 3892,
    "validators": 895,
    "registered": 3456,
    "anonymous": 831,
    "turker": 214,
    "researcher": 42
  },
  "labels": {
    "label_count": 183427,
    "label_count_with_severity": 162320,
    "avg_label_timestamp": "2021-05-10T20:20:25.147504Z",
    "avg_age_of_image_when_labeled": "672 days",
    "stddev_label_timestamp": "188 days",
    "stddev_age_of_image_when_labeled": "543 days",
    "CurbRamp": {
      "count": 72964,
      "count_with_severity": 61837,
      "severity_mean": 1.2,
      "severity_sd": 0.5
    },
    "NoCurbRamp": {
      "count": 35682,
      "count_with_severity": 31245,
      "severity_mean": 3.8,
      "severity_sd": 0.9
    },
    // Some label types like NoSidewalk and Signal don't have severity ratings, so severity fields are null.
    "NoSidewalk": {
      "count": 61837,
      "count_with_severity": null,
      "severity_mean": null,
      "severity_sd": null
    },
    ... // Remaining label types
  },
  // Validation stats are split into three parallel blocks: "combined" (all votes), "human" (non-AI votes only),
  // and "ai" (AI votes only). Each block has the same structure. AI votes are included in "combined".
  "validations": {
    "combined": {
      "total_validations": 125834,
      "Overall": {
        "validated": 151196,
        "agreed": 127564,
        "disagreed": 23632,
        "accuracy": 0.84,
        "has_a_validation": 162974
      },
      "CurbRamp": {
        "validated": 54321,
        "agreed": 48923,
        "disagreed": 5398,
        "accuracy": 0.90,
        "has_a_validation": 59422
      },
      ... // Remaining label types
    },
    "human": {
      "total_validations": 119032,
      "Overall": {
        "validated": 142880,
        "agreed": 120115,
        "disagreed": 22765,
        "accuracy": 0.84,
        "has_a_validation": 154219
      },
      ... // Same label-type structure as "combined"
    },
    "ai": {
      "total_validations": 6802,
      "Overall": {
        "validated": 8316,
        "agreed": 7449,
        "disagreed": 867,
        "accuracy": 0.90,
        "has_a_validation": 8755
      },
      ... // Same label-type structure as "combined"
    }
  },
  "ai_stats": {
    "Overall": {
      "human_majority_vote": {
        "ai_yes_human_concurs": 37,
        "ai_yes_human_differs": 3,
        "ai_no_human_differs": 2,
        "ai_no_human_concurs": 37
      },
      "admin_majority_vote": {
        "ai_yes_human_concurs": 127,
        "ai_yes_human_differs": 54,
        "ai_no_human_differs": 27,
        "ai_no_human_concurs": 134
      }
    },
    "CurbRamp": {
      "human_majority_vote": {
        "ai_yes_human_concurs": 20,
        "ai_yes_human_differs": 0,
        "ai_no_human_differs": 0,
        "ai_no_human_concurs": 2
      },
      "admin_majority_vote": {
        "ai_yes_human_concurs": 42,
        "ai_yes_human_differs": 2,
        "ai_no_human_differs": 6,
        "ai_no_human_concurs": 48
      }
    },
    ... // Remaining label types
  }
}

JSON Field Descriptions #

The response includes the following fields:

CSV Format #

If filetype=csv is specified, the response body will be CSV data with key-value pairs. Each row represents a different statistic or metric:

Launch Date,2021-06-15
Recent Labels Average Timestamp,2023-09-25T14:32:47Z
KM Explored,1834.26
KM Explored Without Overlap,1523.75
Total User Count,4287
Explore User Count,3892
Validate User Count,895
Registered User Count,3456
Anonymous User Count,831
Turker User Count,214
Researcher User Count,42
Total Label Count,183427
Total Label Count With Severity,162320
Average Label Timestamp,2021-05-10T20:20:25.147504Z
Average Age of Image When Labeled,672 days
CurbRamp Count,72964
CurbRamp Count With Severity,61837
CurbRamp Severity Mean,1.2
CurbRamp Severity SD,0.5
...
Combined Total Validations,125834
Combined Overall Labels Validated,151196
Combined Overall Agreed Count,127564
Combined Overall Disagreed Count,23632
Combined Overall Accuracy,0.84
Combined Overall Labels With a Validation,162974
Combined CurbRamp Labels Validated,54321
...
Human Total Validations,119032
Human Overall Labels Validated,142880
...
AI Total Validations,6802
AI Overall Labels Validated,8316
...
Overall AI Yes and Human Majority Vote Concurs,37
Overall AI Yes but Human Majority Vote Differs,3
Overall AI No but Human Majority Vote Differs,2
Overall AI No and Human Majority Vote Concurs,37
Overall AI Yes and Admin Majority Vote Concurs,127
Overall AI Yes but Admin Majority Vote Differs,54
Overall AI No but Admin Majority Vote Differs,27
Overall AI No and Admin Majority Vote Concurs,134
CurbRamp AI Yes and Human Majority Vote Concurs,20
CurbRamp AI Yes but Human Majority Vote Differs,0
CurbRamp AI No but Human Majority Vote Differs,0
CurbRamp AI No and Human Majority Vote Concurs,2
CurbRamp AI Yes and Admin Majority Vote Concurs,42
CurbRamp AI Yes but Admin Majority Vote Differs,2
CurbRamp AI No but Admin Majority Vote Differs,6
CurbRamp AI No and Admin Majority Vote Concurs,48
...

CSV Format Description #

In CSV format, each row represents a specific metric in a key-value format:

• Top-level statistics are represented directly as named rows (e.g., "KM Explored,1834.26")
• Nested statistics like user_counts and labels are flattened into multiple rows with descriptive names
• For each label type, label severity stats are presented as four rows: Count, Count With Severity, Severity Mean, and Severity SD
• For each label type, validation stats are presented as four rows: Labels Validated, Agreed Count, Disagreed Count, and Accuracy
• This flat structure makes the data easy to parse and analyze in spreadsheet applications

Error Responses#

If an error occurs, the API will return an appropriate HTTP status code and a JSON response body containing details about the error.

• 400 Bad Request: Invalid parameter values.
• 404 Not Found: The requested resource does not exist.
• 500 Internal Server Error: An unexpected error occurred on the server.
• 503 Service Unavailable: The server is temporarily unable to handle the request.

Error Response Body #

All errors are returned as RFC 7807 “problem details” with the application/problem+json content type and the following structure:

{
  "type": "about:blank",          // RFC 7807 problem-type URI ("about:blank" means no type beyond the status)
  "title": "Invalid Parameter",   // Short, human-readable summary of the problem type (stable for a given code)
  "status": 400,                  // HTTP status code (also repeated in the body)
  "detail": "Invalid value for the bbox parameter. Expected format: minLng,minLat,maxLng,maxLat.", // This occurrence
  "code": "INVALID_PARAMETER",    // Stable, machine-readable error code you can branch on
  "parameter": "bbox"             // Extension member: the specific parameter at fault (omitted when not applicable)
}

Data Analysis Tips #

The Overall Stats API provides a comprehensive view of Project Sidewalk data. Here are some suggestions for effectively using this data:

• Consider using filterLowQuality=true for more reliable analysis, especially when examining severity ratings
• Compare accuracy rates across label types to identify which accessibility issues might be more ambiguous or difficult to detect
• Use km_explored vs. km_explored_no_overlap to understand the level of redundancy in data collection
• Look at avg_timestamp_last_100_labels to gauge how recently the data has been updated
• Analyze the ratio of validators to explorers to understand community participation patterns

Contribute#

Project Sidewalk is an open-source project created by the Makeability Lab and hosted on GitHub. We welcome your contributions! If you found a bug or have a feature request, please open an issue on GitHub.

You can also email us at sidewalk@cs.uw.edu

Project Sidewalk in Your City!#

If you are interested in bringing Project Sidewalk to your city, please read our Wiki page.