From a 8-row sample across 4 columns.
:@computed_region_sbqj_enih:@computed_region_f5dn_yrercity_city+8:@computed_region_sbqj_enih:@computed_region_f5dn_yrercity_city+8:@computed_region_sbqj_enih:@computed_region_f5dn_yrer:@computed_region_92fq_4b7q+2Mappable — Socrata point column `city`.
Open on the map →| city | street_homeless_estimate | general_population | ratio |
|---|---|---|---|
| {"human_address":"{\"address\": \"\", \"city\": \"New York City\", \"state\": \"\", \"zip\": \"\"}"} | 3262 | 8175133 | 1 in 2,506 |
| {"human_address":"{\"address\": \"\", \"city\": \"San Francisco, CA\", \"state\": \"\", \"zip\": \"\"}"} | 3106 | 805235 | 1 in 259 |
| {"human_address":"{\"address\": \"\", \"city\": \"Los Angeles, CA\", \"state\": \"\", \"zip\": \"\"}"} | 12977 | 3792621 | 1 in 292 |
| {"human_address":"{\"address\": \"\", \"city\": \"Seattle, WA\", \"state\": \"\", \"zip\": \"\"}"} |
The machine field names SoQL needs, with the publisher's own description of each.
| Field | Type | Description | Null | Values |
|---|---|---|---|---|
city | coordinate | — | — | — |
street_homeless_estimate | categorical | — | 0% | 18983262182of 8 |
general_population | categorical | — | 0% | 8052356086603792621of 8 |
ratio | categorical | — | 0% | 1 in 259 1 in 1,973 1 in 1,565 of 8 |
GET /api/datasets/483x-fy9e GET /api/datasets/483x-fy9e/quality GET /api/datasets/483x-fy9e/related GET /api/columns?dataset=483x-fy9e
| 1898 |
| 608660 |
| 1 in 320 |
| {"human_address":"{\"address\": \"\", \"city\": \"Miami, FL\", \"state\": \"\", \"zip\": \"\"}"} | 487 | 399457 | 1 in 820 |
| {"human_address":"{\"address\": \"\", \"city\": \"Washington, DC\", \"state\": \"\", \"zip\": \"\"}"} | 305 | 601723 | 1 in 1,973 |
| {"human_address":"{\"address\": \"\", \"city\": \"Chicago, IL\", \"state\": \"\", \"zip\": \"\"}"} | 1722 | 2695598 | 1 in 1,565 |
| {"human_address":"{\"address\": \"\", \"city\": \"Boston, MA\", \"state\": \"\", \"zip\": \"\"}"} | 182 | 617594 | 1 in 3,393 |