From a 500-row sample across 35 columns.
survey_modenum_tripshh_idsurvey_modecms_zonehh_idsurvey_modeday_numperson_num+4survey_modeperson_nummade_trips+4| hh_id | day_weight | cms_zone | survey_mode | person_id | person_num | day_num | travel_date | travel_date_dow | day_is_complete | made_trips | num_trips |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 191547753 | 35.18806 | Inner Brooklyn | 1 | 19154775301 | 1 | 7 | 2019-06-27T00:00:00.000 | 4 | 1 | 1 | 11 |
| 191547753 | 35.18806 |
The machine field names SoQL needs, with the publisher's own description of each.
| Field | Type | Description | Null | Values |
|---|---|---|---|---|
hh_id | integer | — | 0% | 192251551925602619038499 |
day_weight | float | — | 0% | 017.3734810.39299 |
cms_zone | categorical | — | 0% | Northern BronxStaten IslandSouthern Bronxof 10 |
survey_mode | categorical | — | 0% | 123of 3 |
person_id | integer |
GET /api/datasets/x5ra-hhdw GET /api/datasets/x5ra-hhdw/quality GET /api/datasets/x5ra-hhdw/related GET /api/columns?dataset=x5ra-hhdw
day_numperson_numday_is_complete+3person_numnum_tripsperson_id+1| Inner Brooklyn |
| 1 |
| 19154775301 |
| 1 |
| 6 |
| 2019-06-26T00:00:00.000 |
| 3 |
| 1 |
| 1 |
| 9 |
| 191547753 | 35.18806 | Inner Brooklyn | 1 | 19154775301 | 1 | 5 | 2019-06-25T00:00:00.000 | 2 | 1 | 1 | 5 |
| 191547753 | 35.18806 | Inner Brooklyn | 1 | 19154775301 | 1 | 4 | 2019-06-24T00:00:00.000 | 1 | 1 | 1 | 9 |
| 191547753 | 35.18806 | Inner Brooklyn | 1 | 19154775301 | 1 | 3 | 2019-06-23T00:00:00.000 | 7 | 1 | 1 | 6 |
| 191547753 | 35.18806 | Inner Brooklyn | 1 | 19154775301 | 1 | 2 | 2019-06-22T00:00:00.000 | 6 | 1 | 1 | 7 |
| 191547753 | 0 | Inner Brooklyn | 1 | 19154775301 | 1 | 1 | 2019-06-21T00:00:00.000 | 5 | 0 | 1 | 11 |
| 19305798 | 0 | Inner Queens | 1 | 1930579801 | 1 | 7 | 2019-06-07T00:00:00.000 | 5 | 0 | 1 | 7 |
| 19305798 | 0 | Inner Queens | 1 | 1930579801 | 1 | 6 | 2019-06-06T00:00:00.000 | 4 | 0 | 1 | 4 |
| 19305798 | 0 | Inner Queens | 1 | 1930579801 | 1 | 5 | 2019-06-05T00:00:00.000 | 3 | 0 | 1 | 8 |
Showing 12 of 35 columns. All of them are listed below.
| — |
| 0% |
191619840119908512011996422501 |
person_num | integer | — | 0% | 1of 1 |
day_num | categorical | — | 0% | 143of 7 |
travel_date | datetime | — | 0% | 2019-06-09T00:00:00.0002019-06-08T00:00:00.0002019-06-07T00:00:00.000 |
travel_date_dow | categorical | — | 0% | 437of 7 |
day_is_complete | categorical | — | 0% | 10of 2 |
made_trips | categorical | — | 0% | 10of 2 |
num_trips | integer | — | 0% | 024 |
num_complete_trip_surveys | integer | — | 0% | 024 |
num_deleted_trips | categorical | — | 5% | 012of 13 |
no_travel_capture | categorical | — | 0% | 99501of 4 |
no_travel_no_work | categorical | — | 0% | 9950-9998of 4 |
no_travel_telework | categorical | — | 0% | 9950-9998of 4 |
no_travel_housework | categorical | — | 0% | 99501of 4 |
no_travel_kids_break | categorical | — | 0% | 9950-9998of 4 |
no_travel_no_transport | categorical | — | 0% | 9950-9998of 4 |
no_travel_sick | categorical | — | 0% | 9950-9998of 4 |
no_travel_delivery | categorical | — | 0% | 9950-9998of 4 |
no_travel_homeschool | categorical | — | 0% | 9950-9998of 4 |
no_travel_weather | categorical | — | 0% | 9950-9998of 4 |
no_travel_other | categorical | — | 0% | 9950-9998of 4 |
deliver_prepared | categorical | — | 0% | 0-99981of 3 |
deliver_service | categorical | — | 0% | 0-99981of 3 |
deliver_food | categorical | — | 0% | 0-99981of 3 |
deliver_package_home | categorical | — | 0% | 01-9998of 3 |
deliver_package_work | categorical | — | 0% | 0-99981of 3 |
deliver_package_locker | categorical | — | 0% | 0-99981of 3 |
deliver_other | categorical | — | 0% | 0-99981of 3 |
deliver_none | categorical | — | 0% | 10-9998of 3 |
shop_online | categorical | — | 0% | 21-9998of 4 |
telecommute_time | float | — | 29% | 0810 |