\n",
" \n",
" 0 | \n",
- " lg_parcels_ef7cdae4 | \n",
- " Parcels - United States of America (Parcel) | \n",
- " Nationwide dataset with data from 140 million ... | \n",
- " housing | \n",
- " usa | \n",
- " parcels | \n",
- " landgrid | \n",
- " Parcel - United States of America | \n",
- " Land parcels compiled by Landgrid | \n",
- " monthly | \n",
- " None | \n",
- " monthly | \n",
- " False | \n",
- " eng | \n",
- " v1 | \n",
- " Housing | \n",
- " Landgrid | \n",
- " carto-do.landgrid.geography_usa_parcel_v1 | \n",
- " carto-do.landgrid.housing_parcels_usa_parcel_v... | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " sg_social_dist_667d8e8e | \n",
- " Social Distancing Metrics - United States of A... | \n",
- " Due to the COVID-19 pandemic, people are curre... | \n",
- " covid19 | \n",
- " usa | \n",
- " social_distancing | \n",
- " safegraph | \n",
- " Census Block Group - United States of America ... | \n",
- " Shoreline clipped TIGER/Line boundaries. More ... | \n",
- " daily | \n",
- " None | \n",
- " daily | \n",
- " False | \n",
- " eng | \n",
- " v1 | \n",
- " Covid-19 | \n",
- " SafeGraph | \n",
- " carto-do-public-data.carto.geography_usa_block... | \n",
- " carto-do.safegraph.covid19_socialdistancing_us... | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " ine_sociodemogr_c8c87afe | \n",
- " Sociodemographics (Spain, Census Sections) | \n",
- " Sociodemographic data from the Instituto Nacio... | \n",
+ " ags_sociodemogr_a7e14220 | \n",
+ " Sociodemographics - United States of America (... | \n",
+ " Census and ACS sociodemographic data estimated... | \n",
" demographics | \n",
- " esp | \n",
+ " usa | \n",
" sociodemographics | \n",
- " esp_ine | \n",
- " Census Section (Spain) | \n",
- " 2020 Census Sections, from the Instituto Nacio... | \n",
+ " ags | \n",
+ " Census Block Group - United States of America | \n",
+ " None | \n",
" yearly | \n",
" None | \n",
" None | \n",
- " True | \n",
+ " False | \n",
" eng | \n",
- " 2011 | \n",
+ " 2020 | \n",
" Demographics | \n",
- " Instituto Nacional de Estadística | \n",
- " carto-do-public-data.esp_ine.geography_esp_cen... | \n",
- " carto-do-public-data.esp_ine.demographics_soci... | \n",
+ " Applied Geographic Solutions | \n",
+ " carto-do.ags.geography_usa_blockgroup_2015 | \n",
+ " carto-do.ags.demographics_sociodemographics_us... | \n",
"
\n",
" \n",
- " 3 | \n",
- " expn_sociodemogr_25b78bba | \n",
- " Sociodemographics - Thailand (Grid 250m) | \n",
- " Worldview combines Experian's own datasets wit... | \n",
+ " 1 | \n",
+ " ags_retailpoten_aaf25a8c | \n",
+ " Retail Potential - United States of America (C... | \n",
+ " The retail potential database consists of aver... | \n",
" demographics | \n",
- " tha | \n",
- " sociodemographics | \n",
- " experian | \n",
- " Grid 250m - Thailand | \n",
- " Experian 250mx250m grid cells | \n",
+ " usa | \n",
+ " retailpotential | \n",
+ " ags | \n",
+ " Census Block Group - United States of America ... | \n",
+ " Shoreline clipped TIGER/Line boundaries. More ... | \n",
" yearly | \n",
- " [2019-01-01, 2020-01-01) | \n",
+ " [2018-01-01, 2019-01-01) | \n",
" None | \n",
" False | \n",
" eng | \n",
- " 2020 | \n",
+ " 2019 | \n",
" Demographics | \n",
- " Experian | \n",
- " carto-do.experian.geography_tha_grid_v1 | \n",
- " carto-do.experian.demographics_sociodemographi... | \n",
+ " Applied Geographic Solutions | \n",
+ " carto-do-public-data.carto.geography_usa_block... | \n",
+ " carto-do.ags.demographics_retailpotential_usa_... | \n",
"
\n",
" \n",
- " 4 | \n",
- " mc_geographic__7980c5c3 | \n",
- " Geographic Insights - United States of America... | \n",
- " Geographic Insights validate, evaluate and ben... | \n",
- " financial | \n",
+ " 2 | \n",
+ " pb_consumer_po_62cddc04 | \n",
+ " Points Of Interest - Consumer - United States ... | \n",
+ " Consumer Point of interest database per catego... | \n",
+ " points_of_interest | \n",
" usa | \n",
- " geographic_insights | \n",
- " mastercard | \n",
- " Census Block Group - United States of America ... | \n",
- " Shoreline clipped TIGER/Line boundaries. More ... | \n",
+ " consumer_points_of_interest | \n",
+ " pitney_bowes | \n",
+ " Latitude/Longitude - United States of America | \n",
+ " Location of Points of Interest | \n",
" monthly | \n",
- " [2019-01-01, 2020-01-01) | \n",
+ " None | \n",
" monthly | \n",
" False | \n",
" eng | \n",
" v1 | \n",
- " Financial | \n",
- " Mastercard | \n",
- " carto-do-public-data.carto.geography_usa_block... | \n",
- " carto-do.mastercard.financial_geographicinsigh... | \n",
+ " Points of Interest | \n",
+ " Pitney Bowes | \n",
+ " carto-do.pitney_bowes.geography_usa_latlon_v1 | \n",
+ " carto-do.pitney_bowes.pointsofinterest_consume... | \n",
"
\n",
" \n",
- " 5 | \n",
- " acs_sociodemogr_b758e778 | \n",
+ " 3 | \n",
+ " ags_sociodemogr_f510a947 | \n",
" Sociodemographics - United States of America (... | \n",
- " The American Community Survey (ACS) is an ongo... | \n",
+ " Census and ACS sociodemographic data estimated... | \n",
" demographics | \n",
" usa | \n",
" sociodemographics | \n",
- " usa_acs | \n",
+ " ags | \n",
" Census Block Group - United States of America ... | \n",
" Shoreline clipped TIGER/Line boundaries. More ... | \n",
- " 5yrs | \n",
- " [2013-01-01, 2018-01-01) | \n",
+ " yearly | \n",
+ " [2019-01-01, 2020-01-01) | \n",
" None | \n",
- " True | \n",
+ " False | \n",
" eng | \n",
- " 20132017 | \n",
+ " 2019 | \n",
" Demographics | \n",
- " American Community Survey | \n",
+ " Applied Geographic Solutions | \n",
" carto-do-public-data.carto.geography_usa_block... | \n",
- " carto-do-public-data.usa_acs.demographics_soci... | \n",
- "
\n",
- " \n",
- " 6 | \n",
- " can_sociodemogr_affc7f83 | \n",
- " Sociodemographics - Canada (Dissemination Area) | \n",
- " Sociodemographic data from Statistics Canada. ... | \n",
- " demographics | \n",
- " can | \n",
- " sociodemographics | \n",
- " can_statistics | \n",
- " Dissemination Area - Canada (2016) | \n",
- " Canada - Dissemination Area | \n",
- " 5yrs | \n",
- " None | \n",
- " None | \n",
- " True | \n",
- " eng | \n",
- " 2016 | \n",
- " Demographics | \n",
- " Statistics Canada | \n",
- " carto-do-public-data.carto.geography_can_disse... | \n",
- " carto-do-public-data.can_statistics.demographi... | \n",
- "
\n",
- " \n",
- " 7 | \n",
- " can_employment_e3bbbb6 | \n",
- " Employment And Income - Canada (Dissemination ... | \n",
- " Employment and income data from Statistics Can... | \n",
- " demographics | \n",
- " can | \n",
- " employment | \n",
- " can_statistics | \n",
- " Dissemination Area - Canada (2016) | \n",
- " Canada - Dissemination Area | \n",
- " 5yrs | \n",
- " None | \n",
- " None | \n",
- " True | \n",
- " eng | \n",
- " 2016 | \n",
- " Demographics | \n",
- " Statistics Canada | \n",
- " carto-do-public-data.carto.geography_can_disse... | \n",
- " carto-do-public-data.can_statistics.demographi... | \n",
+ " carto-do.ags.demographics_sociodemographics_us... | \n",
"
\n",
" \n",
- " 8 | \n",
- " wp_population_cd347169 | \n",
- " Population Mosaics - Switzerland (Grid 1km, 2020) | \n",
- " Mosaiced 1km resolution global datasets. The m... | \n",
+ " 4 | \n",
+ " ags_consumer_sp_dbabddfb | \n",
+ " Consumer Spending - United States of America (... | \n",
+ " The Consumer Expenditure database consists of ... | \n",
" demographics | \n",
- " che | \n",
- " population | \n",
- " worldpop | \n",
- " Grid 1km - Switzerland | \n",
- " Global grid at aprox. 1-kilometer resolution (... | \n",
- " yearly | \n",
- " [2020-01-01, 2021-01-01) | \n",
+ " usa | \n",
+ " consumer_spending | \n",
+ " ags | \n",
+ " Census Block Group - United States of America | \n",
" None | \n",
- " True | \n",
- " eng | \n",
- " 2020 | \n",
- " Demographics | \n",
- " WorldPop | \n",
- " carto-do-public-data.worldpop.geography_che_gr... | \n",
- " carto-do-public-data.worldpop.demographics_pop... | \n",
- "
\n",
- " \n",
- " 9 | \n",
- " wp_population_35d01fd4 | \n",
- " Population Mosaics - Belgium (Grid 100m, 2015) | \n",
- " Mosaiced 100m resolution global datasets. The ... | \n",
- " demographics | \n",
- " bel | \n",
- " population | \n",
- " worldpop | \n",
- " Grid 100m - Belgium | \n",
- " Global grid at aprox. 100-meter resolution (0.... | \n",
" yearly | \n",
- " [2015-01-01, 2016-01-01) | \n",
" None | \n",
- " True | \n",
- " eng | \n",
- " 2015 | \n",
- " Demographics | \n",
- " WorldPop | \n",
- " carto-do-public-data.worldpop.geography_bel_gr... | \n",
- " carto-do-public-data.worldpop.demographics_pop... | \n",
- "
\n",
- " \n",
- " 10 | \n",
- " wp_population_6bf077c7 | \n",
- " Population Mosaics - Italy (Grid 1km, 2020) | \n",
- " Mosaiced 1km resolution global datasets. The m... | \n",
- " demographics | \n",
- " ita | \n",
- " population | \n",
- " worldpop | \n",
- " Grid 1km - Italy | \n",
- " Global grid at aprox. 1-kilometer resolution (... | \n",
- " yearly | \n",
- " [2020-01-01, 2021-01-01) | \n",
" None | \n",
- " True | \n",
+ " False | \n",
" eng | \n",
" 2020 | \n",
" Demographics | \n",
- " WorldPop | \n",
- " carto-do-public-data.worldpop.geography_ita_gr... | \n",
- " carto-do-public-data.worldpop.demographics_pop... | \n",
+ " Applied Geographic Solutions | \n",
+ " carto-do.ags.geography_usa_blockgroup_2015 | \n",
+ " carto-do.ags.demographics_consumerspending_usa... | \n",
"
\n",
" \n",
- " 11 | \n",
+ " 5 | \n",
" spa_geosocial_s_d5dc42ae | \n",
" Geosocial Segments - United States of America ... | \n",
" By analysing feeds from Twitter, Instagram, Me... | \n",
@@ -404,504 +272,188 @@
" carto-do.spatial_ai.behavioral_geosocialsegmen... | \n",
"
\n",
" \n",
- " 12 | \n",
- " expn_consumer_se_7d6172d | \n",
- " Consumer Segments - Russia (Grid 250m) | \n",
- " WorldView segments has been developed to segme... | \n",
- " demographics | \n",
- " rus | \n",
- " consumer_segments | \n",
- " experian | \n",
- " Grid 250m - Russia | \n",
- " Experian 250mx250m grid cells | \n",
- " yearly | \n",
- " [2019-01-01, 2020-01-01) | \n",
- " None | \n",
- " False | \n",
- " eng | \n",
- " 2020 | \n",
- " Demographics | \n",
- " Experian | \n",
- " carto-do.experian.geography_rus_grid_v2 | \n",
- " carto-do.experian.demographics_consumersegment... | \n",
- "
\n",
- " \n",
- " 13 | \n",
- " expn_sociodemogr_81aa1d1e | \n",
- " Sociodemographics - Russia (Grid 250m) | \n",
- " Worldview combines Experian's own datasets wit... | \n",
- " demographics | \n",
- " rus | \n",
- " sociodemographics | \n",
- " experian | \n",
- " Grid 250m - Russia | \n",
- " Experian 250mx250m grid cells | \n",
- " yearly | \n",
- " [2019-01-01, 2020-01-01) | \n",
- " None | \n",
- " False | \n",
- " eng | \n",
- " 2020 | \n",
- " Demographics | \n",
- " Experian | \n",
- " carto-do.experian.geography_rus_grid_v2 | \n",
- " carto-do.experian.demographics_sociodemographi... | \n",
- "
\n",
- " \n",
- " 14 | \n",
- " cdb_spatial_fea_d23a5c97 | \n",
- " Spatial Features - Spain (Quadgrid 15) | \n",
- " Spatial Features is a dataset curated by CARTO... | \n",
- " derived | \n",
- " esp | \n",
- " spatial_features | \n",
- " carto | \n",
- " Quadgrid 15 - Spain | \n",
- " Global Quadgrid (zoom level 15) | \n",
- " yearly | \n",
- " None | \n",
- " None | \n",
- " True | \n",
- " eng | \n",
- " 2020 | \n",
- " Derived | \n",
- " CARTO | \n",
- " carto-do-public-data.carto.geography_esp_quadg... | \n",
- " carto-do-public-data.carto.derived_spatialfeat... | \n",
- "
\n",
- " \n",
- " 15 | \n",
- " cdb_spatial_fea_802d4d44 | \n",
- " Spatial Features - France (Quadgrid 15) | \n",
- " Spatial Features is a dataset curated by CARTO... | \n",
- " derived | \n",
- " fra | \n",
- " spatial_features | \n",
- " carto | \n",
- " Quadgrid 15 - France | \n",
- " Global Quadgrid (zoom level 15) | \n",
- " yearly | \n",
- " None | \n",
- " None | \n",
- " False | \n",
- " eng | \n",
- " 2020 | \n",
- " Derived | \n",
- " CARTO | \n",
- " carto-do-public-data.carto.geography_fra_quadg... | \n",
- " carto-do.carto.derived_spatialfeatures_fra_qua... | \n",
- "
\n",
- " \n",
- " 16 | \n",
- " cdb_spatial_fea_7bd51aec | \n",
- " Spatial Features - Guyana (Quadgrid 15) | \n",
- " Spatial Features is a dataset curated by CARTO... | \n",
- " derived | \n",
- " guy | \n",
- " spatial_features | \n",
- " carto | \n",
- " Quadgrid 15 - Guyana | \n",
- " Global Quadgrid (zoom level 15) | \n",
- " yearly | \n",
- " None | \n",
- " None | \n",
- " False | \n",
- " eng | \n",
- " 2020 | \n",
- " Derived | \n",
- " CARTO | \n",
- " carto-do-public-data.carto.geography_guy_quadg... | \n",
- " carto-do.carto.derived_spatialfeatures_guy_qua... | \n",
- "
\n",
- " \n",
- " 17 | \n",
- " ws_climatology_83bcb297 | \n",
- " Climatology - Japan (Grid 22km, hourly) | \n",
- " Global climatology data providing weather stat... | \n",
- " environmental | \n",
- " jpn | \n",
- " climatology | \n",
- " weather_source | \n",
- " Grid 22km - Global | \n",
- " Custom grid at 22 kilometer resolution | \n",
- " hourly | \n",
- " [2005-01-01, 2020-01-01) | \n",
- " yearly | \n",
- " False | \n",
- " eng | \n",
- " v1 | \n",
- " Environmental | \n",
- " Weather Source | \n",
- " carto-do.weather_source.geography_glo_grid22km_v1 | \n",
- " carto-do.weather_source.environmental_climatol... | \n",
- "
\n",
- " \n",
- " 18 | \n",
- " uc_activity_ae564b62 | \n",
- " Activity - Philippines (Quadgrid 17) | \n",
- " Leveraging a global panel of location signals ... | \n",
- " human_mobility | \n",
- " phl | \n",
- " activity | \n",
- " unacast | \n",
- " Quadgrid 17 - Philippines | \n",
- " Quad Key Grid - Level 17 | \n",
+ " 6 | \n",
+ " mc_geographic__7980c5c3 | \n",
+ " Geographic Insights - United States of America... | \n",
+ " Geographic Insights validate, evaluate and ben... | \n",
+ " financial | \n",
+ " usa | \n",
+ " geographic_insights | \n",
+ " mastercard | \n",
+ " Census Block Group - United States of America ... | \n",
+ " Shoreline clipped TIGER/Line boundaries. More ... | \n",
" monthly | \n",
" [2019-01-01, 2020-01-01) | \n",
" monthly | \n",
" False | \n",
" eng | \n",
" v1 | \n",
- " Human Mobility | \n",
- " Unacast | \n",
- " carto-do-public-data.carto.geography_phl_quadg... | \n",
- " carto-do.unacast.humanmobility_activity_phl_qu... | \n",
+ " Financial | \n",
+ " Mastercard | \n",
+ " carto-do-public-data.carto.geography_usa_block... | \n",
+ " carto-do.mastercard.financial_geographicinsigh... | \n",
"
\n",
" \n",
- " 19 | \n",
- " uc_home_and_wo_b42b8699 | \n",
- " Home And Work - Philippines (Quadgrid 17) | \n",
- " Paired with the Activity dataset, this data pr... | \n",
- " human_mobility | \n",
- " phl | \n",
- " home_and_work | \n",
- " unacast | \n",
- " Quadgrid | \n",
- " Quadgrid (multiple zoom levels) | \n",
+ " 7 | \n",
+ " pb_points_of_i_94bda91b | \n",
+ " Points Of Interest - Spain (Latitude/Longitude) | \n",
+ " Point of interest database per categories | \n",
+ " points_of_interest | \n",
+ " esp | \n",
+ " points_of_interest | \n",
+ " pitney_bowes | \n",
+ " Latitude/Longitude - Spain | \n",
+ " Location of Points of Interest | \n",
" monthly | \n",
- " [2019-01-01, 2020-01-01) | \n",
+ " None | \n",
" monthly | \n",
" False | \n",
" eng | \n",
" v1 | \n",
- " Human Mobility | \n",
- " Unacast | \n",
- " carto-do-public-data.carto.geography_phl_quadg... | \n",
- " carto-do.unacast.humanmobility_homeandwork_phl... | \n",
+ " Points of Interest | \n",
+ " Pitney Bowes | \n",
+ " carto-do.pitney_bowes.geography_esp_latlon_v1 | \n",
+ " carto-do.pitney_bowes.pointsofinterest_pointso... | \n",
"
\n",
" \n",
- " 20 | \n",
- " expn_sociodemogr_8d3aa47a | \n",
- " Sociodemographics - Philippines (Grid 250m) | \n",
- " Worldview combines Experian's own datasets wit... | \n",
+ " 8 | \n",
+ " u360_sociodemogr_28e93b81 | \n",
+ " Sociodemographics - Spain (Grid 100m) | \n",
+ " Curation of socio-demographic and socio-econom... | \n",
" demographics | \n",
- " phl | \n",
+ " esp | \n",
" sociodemographics | \n",
- " experian | \n",
- " Grid 250m - Philippines | \n",
- " Experian 250mx250m grid cells | \n",
+ " unica360 | \n",
+ " Grid 100m | \n",
+ " 100x100m grid | \n",
" yearly | \n",
- " [2019-01-01, 2020-01-01) | \n",
- " None | \n",
- " False | \n",
- " eng | \n",
- " 2020 | \n",
- " Demographics | \n",
- " Experian | \n",
- " carto-do.experian.geography_phl_grid_v1 | \n",
- " carto-do.experian.demographics_sociodemographi... | \n",
- "
\n",
- " \n",
- " 21 | \n",
- " ws_forecast_39e1ab6a | \n",
- " Forecast - Spain (Municipality, hourly) | \n",
- " 10 and 15-day weather forecasts | \n",
- " environmental | \n",
- " esp | \n",
- " forecast | \n",
- " weather_source | \n",
- " Municipality (Spain) | \n",
- " 2020 Municipalities, from the Instituto Geográ... | \n",
- " hourly | \n",
" None | \n",
- " daily | \n",
- " False | \n",
- " eng | \n",
- " v1 | \n",
- " Environmental | \n",
- " Weather Source | \n",
- " carto-do-public-data.esp_ign.geography_esp_mun... | \n",
- " carto-do.weather_source.environmental_forecast... | \n",
- "
\n",
- " \n",
- " 22 | \n",
- " ws_historic_43f694cc | \n",
- " Historic - Spain (Municipality, hourly) | \n",
- " Past weather data from the year 2000 to present | \n",
- " environmental | \n",
- " esp | \n",
- " historic | \n",
- " weather_source | \n",
- " Municipality (Spain) | \n",
- " 2020 Municipalities, from the Instituto Geográ... | \n",
- " hourly | \n",
" None | \n",
- " daily | \n",
" False | \n",
" eng | \n",
- " v1 | \n",
- " Environmental | \n",
- " Weather Source | \n",
- " carto-do-public-data.esp_ign.geography_esp_mun... | \n",
- " carto-do.weather_source.environmental_historic... | \n",
+ " 2019 | \n",
+ " Demographics | \n",
+ " Unica360 | \n",
+ " carto-do.unica360.geography_esp_grid100x100m_2019 | \n",
+ " carto-do.unica360.demographics_sociodemographi... | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " slug \\\n",
- "0 lg_parcels_ef7cdae4 \n",
- "1 sg_social_dist_667d8e8e \n",
- "2 ine_sociodemogr_c8c87afe \n",
- "3 expn_sociodemogr_25b78bba \n",
- "4 mc_geographic__7980c5c3 \n",
- "5 acs_sociodemogr_b758e778 \n",
- "6 can_sociodemogr_affc7f83 \n",
- "7 can_employment_e3bbbb6 \n",
- "8 wp_population_cd347169 \n",
- "9 wp_population_35d01fd4 \n",
- "10 wp_population_6bf077c7 \n",
- "11 spa_geosocial_s_d5dc42ae \n",
- "12 expn_consumer_se_7d6172d \n",
- "13 expn_sociodemogr_81aa1d1e \n",
- "14 cdb_spatial_fea_d23a5c97 \n",
- "15 cdb_spatial_fea_802d4d44 \n",
- "16 cdb_spatial_fea_7bd51aec \n",
- "17 ws_climatology_83bcb297 \n",
- "18 uc_activity_ae564b62 \n",
- "19 uc_home_and_wo_b42b8699 \n",
- "20 expn_sociodemogr_8d3aa47a \n",
- "21 ws_forecast_39e1ab6a \n",
- "22 ws_historic_43f694cc \n",
- "\n",
- " name \\\n",
- "0 Parcels - United States of America (Parcel) \n",
- "1 Social Distancing Metrics - United States of A... \n",
- "2 Sociodemographics (Spain, Census Sections) \n",
- "3 Sociodemographics - Thailand (Grid 250m) \n",
- "4 Geographic Insights - United States of America... \n",
- "5 Sociodemographics - United States of America (... \n",
- "6 Sociodemographics - Canada (Dissemination Area) \n",
- "7 Employment And Income - Canada (Dissemination ... \n",
- "8 Population Mosaics - Switzerland (Grid 1km, 2020) \n",
- "9 Population Mosaics - Belgium (Grid 100m, 2015) \n",
- "10 Population Mosaics - Italy (Grid 1km, 2020) \n",
- "11 Geosocial Segments - United States of America ... \n",
- "12 Consumer Segments - Russia (Grid 250m) \n",
- "13 Sociodemographics - Russia (Grid 250m) \n",
- "14 Spatial Features - Spain (Quadgrid 15) \n",
- "15 Spatial Features - France (Quadgrid 15) \n",
- "16 Spatial Features - Guyana (Quadgrid 15) \n",
- "17 Climatology - Japan (Grid 22km, hourly) \n",
- "18 Activity - Philippines (Quadgrid 17) \n",
- "19 Home And Work - Philippines (Quadgrid 17) \n",
- "20 Sociodemographics - Philippines (Grid 250m) \n",
- "21 Forecast - Spain (Municipality, hourly) \n",
- "22 Historic - Spain (Municipality, hourly) \n",
- "\n",
- " description category_id \\\n",
- "0 Nationwide dataset with data from 140 million ... housing \n",
- "1 Due to the COVID-19 pandemic, people are curre... covid19 \n",
- "2 Sociodemographic data from the Instituto Nacio... demographics \n",
- "3 Worldview combines Experian's own datasets wit... demographics \n",
- "4 Geographic Insights validate, evaluate and ben... financial \n",
- "5 The American Community Survey (ACS) is an ongo... demographics \n",
- "6 Sociodemographic data from Statistics Canada. ... demographics \n",
- "7 Employment and income data from Statistics Can... demographics \n",
- "8 Mosaiced 1km resolution global datasets. The m... demographics \n",
- "9 Mosaiced 100m resolution global datasets. The ... demographics \n",
- "10 Mosaiced 1km resolution global datasets. The m... demographics \n",
- "11 By analysing feeds from Twitter, Instagram, Me... behavioral \n",
- "12 WorldView segments has been developed to segme... demographics \n",
- "13 Worldview combines Experian's own datasets wit... demographics \n",
- "14 Spatial Features is a dataset curated by CARTO... derived \n",
- "15 Spatial Features is a dataset curated by CARTO... derived \n",
- "16 Spatial Features is a dataset curated by CARTO... derived \n",
- "17 Global climatology data providing weather stat... environmental \n",
- "18 Leveraging a global panel of location signals ... human_mobility \n",
- "19 Paired with the Activity dataset, this data pr... human_mobility \n",
- "20 Worldview combines Experian's own datasets wit... demographics \n",
- "21 10 and 15-day weather forecasts environmental \n",
- "22 Past weather data from the year 2000 to present environmental \n",
- "\n",
- " country_id data_source_id provider_id \\\n",
- "0 usa parcels landgrid \n",
- "1 usa social_distancing safegraph \n",
- "2 esp sociodemographics esp_ine \n",
- "3 tha sociodemographics experian \n",
- "4 usa geographic_insights mastercard \n",
- "5 usa sociodemographics usa_acs \n",
- "6 can sociodemographics can_statistics \n",
- "7 can employment can_statistics \n",
- "8 che population worldpop \n",
- "9 bel population worldpop \n",
- "10 ita population worldpop \n",
- "11 usa geosocial_segments spatial_ai \n",
- "12 rus consumer_segments experian \n",
- "13 rus sociodemographics experian \n",
- "14 esp spatial_features carto \n",
- "15 fra spatial_features carto \n",
- "16 guy spatial_features carto \n",
- "17 jpn climatology weather_source \n",
- "18 phl activity unacast \n",
- "19 phl home_and_work unacast \n",
- "20 phl sociodemographics experian \n",
- "21 esp forecast weather_source \n",
- "22 esp historic weather_source \n",
- "\n",
- " geography_name \\\n",
- "0 Parcel - United States of America \n",
- "1 Census Block Group - United States of America ... \n",
- "2 Census Section (Spain) \n",
- "3 Grid 250m - Thailand \n",
- "4 Census Block Group - United States of America ... \n",
- "5 Census Block Group - United States of America ... \n",
- "6 Dissemination Area - Canada (2016) \n",
- "7 Dissemination Area - Canada (2016) \n",
- "8 Grid 1km - Switzerland \n",
- "9 Grid 100m - Belgium \n",
- "10 Grid 1km - Italy \n",
- "11 Census Block Group - United States of America ... \n",
- "12 Grid 250m - Russia \n",
- "13 Grid 250m - Russia \n",
- "14 Quadgrid 15 - Spain \n",
- "15 Quadgrid 15 - France \n",
- "16 Quadgrid 15 - Guyana \n",
- "17 Grid 22km - Global \n",
- "18 Quadgrid 17 - Philippines \n",
- "19 Quadgrid \n",
- "20 Grid 250m - Philippines \n",
- "21 Municipality (Spain) \n",
- "22 Municipality (Spain) \n",
- "\n",
- " geography_description temporal_aggregation \\\n",
- "0 Land parcels compiled by Landgrid monthly \n",
- "1 Shoreline clipped TIGER/Line boundaries. More ... daily \n",
- "2 2020 Census Sections, from the Instituto Nacio... yearly \n",
- "3 Experian 250mx250m grid cells yearly \n",
- "4 Shoreline clipped TIGER/Line boundaries. More ... monthly \n",
- "5 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n",
- "6 Canada - Dissemination Area 5yrs \n",
- "7 Canada - Dissemination Area 5yrs \n",
- "8 Global grid at aprox. 1-kilometer resolution (... yearly \n",
- "9 Global grid at aprox. 100-meter resolution (0.... yearly \n",
- "10 Global grid at aprox. 1-kilometer resolution (... yearly \n",
- "11 Shoreline clipped TIGER/Line boundaries. More ... quarterly \n",
- "12 Experian 250mx250m grid cells yearly \n",
- "13 Experian 250mx250m grid cells yearly \n",
- "14 Global Quadgrid (zoom level 15) yearly \n",
- "15 Global Quadgrid (zoom level 15) yearly \n",
- "16 Global Quadgrid (zoom level 15) yearly \n",
- "17 Custom grid at 22 kilometer resolution hourly \n",
- "18 Quad Key Grid - Level 17 monthly \n",
- "19 Quadgrid (multiple zoom levels) monthly \n",
- "20 Experian 250mx250m grid cells yearly \n",
- "21 2020 Municipalities, from the Instituto Geográ... hourly \n",
- "22 2020 Municipalities, from the Instituto Geográ... hourly \n",
- "\n",
- " time_coverage update_frequency is_public_data lang version \\\n",
- "0 None monthly False eng v1 \n",
- "1 None daily False eng v1 \n",
- "2 None None True eng 2011 \n",
- "3 [2019-01-01, 2020-01-01) None False eng 2020 \n",
- "4 [2019-01-01, 2020-01-01) monthly False eng v1 \n",
- "5 [2013-01-01, 2018-01-01) None True eng 20132017 \n",
- "6 None None True eng 2016 \n",
- "7 None None True eng 2016 \n",
- "8 [2020-01-01, 2021-01-01) None True eng 2020 \n",
- "9 [2015-01-01, 2016-01-01) None True eng 2015 \n",
- "10 [2020-01-01, 2021-01-01) None True eng 2020 \n",
- "11 [2020-01-01, 2020-04-01) quarterly False eng v1 \n",
- "12 [2019-01-01, 2020-01-01) None False eng 2020 \n",
- "13 [2019-01-01, 2020-01-01) None False eng 2020 \n",
- "14 None None True eng 2020 \n",
- "15 None None False eng 2020 \n",
- "16 None None False eng 2020 \n",
- "17 [2005-01-01, 2020-01-01) yearly False eng v1 \n",
- "18 [2019-01-01, 2020-01-01) monthly False eng v1 \n",
- "19 [2019-01-01, 2020-01-01) monthly False eng v1 \n",
- "20 [2019-01-01, 2020-01-01) None False eng 2020 \n",
- "21 None daily False eng v1 \n",
- "22 None daily False eng v1 \n",
- "\n",
- " category_name provider_name \\\n",
- "0 Housing Landgrid \n",
- "1 Covid-19 SafeGraph \n",
- "2 Demographics Instituto Nacional de Estadística \n",
- "3 Demographics Experian \n",
- "4 Financial Mastercard \n",
- "5 Demographics American Community Survey \n",
- "6 Demographics Statistics Canada \n",
- "7 Demographics Statistics Canada \n",
- "8 Demographics WorldPop \n",
- "9 Demographics WorldPop \n",
- "10 Demographics WorldPop \n",
- "11 Behavioral Spatial.ai \n",
- "12 Demographics Experian \n",
- "13 Demographics Experian \n",
- "14 Derived CARTO \n",
- "15 Derived CARTO \n",
- "16 Derived CARTO \n",
- "17 Environmental Weather Source \n",
- "18 Human Mobility Unacast \n",
- "19 Human Mobility Unacast \n",
- "20 Demographics Experian \n",
- "21 Environmental Weather Source \n",
- "22 Environmental Weather Source \n",
- "\n",
- " geography_id \\\n",
- "0 carto-do.landgrid.geography_usa_parcel_v1 \n",
- "1 carto-do-public-data.carto.geography_usa_block... \n",
- "2 carto-do-public-data.esp_ine.geography_esp_cen... \n",
- "3 carto-do.experian.geography_tha_grid_v1 \n",
- "4 carto-do-public-data.carto.geography_usa_block... \n",
- "5 carto-do-public-data.carto.geography_usa_block... \n",
- "6 carto-do-public-data.carto.geography_can_disse... \n",
- "7 carto-do-public-data.carto.geography_can_disse... \n",
- "8 carto-do-public-data.worldpop.geography_che_gr... \n",
- "9 carto-do-public-data.worldpop.geography_bel_gr... \n",
- "10 carto-do-public-data.worldpop.geography_ita_gr... \n",
- "11 carto-do-public-data.carto.geography_usa_block... \n",
- "12 carto-do.experian.geography_rus_grid_v2 \n",
- "13 carto-do.experian.geography_rus_grid_v2 \n",
- "14 carto-do-public-data.carto.geography_esp_quadg... \n",
- "15 carto-do-public-data.carto.geography_fra_quadg... \n",
- "16 carto-do-public-data.carto.geography_guy_quadg... \n",
- "17 carto-do.weather_source.geography_glo_grid22km_v1 \n",
- "18 carto-do-public-data.carto.geography_phl_quadg... \n",
- "19 carto-do-public-data.carto.geography_phl_quadg... \n",
- "20 carto-do.experian.geography_phl_grid_v1 \n",
- "21 carto-do-public-data.esp_ign.geography_esp_mun... \n",
- "22 carto-do-public-data.esp_ign.geography_esp_mun... \n",
- "\n",
- " id \n",
- "0 carto-do.landgrid.housing_parcels_usa_parcel_v... \n",
- "1 carto-do.safegraph.covid19_socialdistancing_us... \n",
- "2 carto-do-public-data.esp_ine.demographics_soci... \n",
- "3 carto-do.experian.demographics_sociodemographi... \n",
- "4 carto-do.mastercard.financial_geographicinsigh... \n",
- "5 carto-do-public-data.usa_acs.demographics_soci... \n",
- "6 carto-do-public-data.can_statistics.demographi... \n",
- "7 carto-do-public-data.can_statistics.demographi... \n",
- "8 carto-do-public-data.worldpop.demographics_pop... \n",
- "9 carto-do-public-data.worldpop.demographics_pop... \n",
- "10 carto-do-public-data.worldpop.demographics_pop... \n",
- "11 carto-do.spatial_ai.behavioral_geosocialsegmen... \n",
- "12 carto-do.experian.demographics_consumersegment... \n",
- "13 carto-do.experian.demographics_sociodemographi... \n",
- "14 carto-do-public-data.carto.derived_spatialfeat... \n",
- "15 carto-do.carto.derived_spatialfeatures_fra_qua... \n",
- "16 carto-do.carto.derived_spatialfeatures_guy_qua... \n",
- "17 carto-do.weather_source.environmental_climatol... \n",
- "18 carto-do.unacast.humanmobility_activity_phl_qu... \n",
- "19 carto-do.unacast.humanmobility_homeandwork_phl... \n",
- "20 carto-do.experian.demographics_sociodemographi... \n",
- "21 carto-do.weather_source.environmental_forecast... \n",
- "22 carto-do.weather_source.environmental_historic... "
+ " slug \\\n",
+ "0 ags_sociodemogr_a7e14220 \n",
+ "1 ags_retailpoten_aaf25a8c \n",
+ "2 pb_consumer_po_62cddc04 \n",
+ "3 ags_sociodemogr_f510a947 \n",
+ "4 ags_consumer_sp_dbabddfb \n",
+ "5 spa_geosocial_s_d5dc42ae \n",
+ "6 mc_geographic__7980c5c3 \n",
+ "7 pb_points_of_i_94bda91b \n",
+ "8 u360_sociodemogr_28e93b81 \n",
+ "\n",
+ " name \\\n",
+ "0 Sociodemographics - United States of America (... \n",
+ "1 Retail Potential - United States of America (C... \n",
+ "2 Points Of Interest - Consumer - United States ... \n",
+ "3 Sociodemographics - United States of America (... \n",
+ "4 Consumer Spending - United States of America (... \n",
+ "5 Geosocial Segments - United States of America ... \n",
+ "6 Geographic Insights - United States of America... \n",
+ "7 Points Of Interest - Spain (Latitude/Longitude) \n",
+ "8 Sociodemographics - Spain (Grid 100m) \n",
+ "\n",
+ " description category_id \\\n",
+ "0 Census and ACS sociodemographic data estimated... demographics \n",
+ "1 The retail potential database consists of aver... demographics \n",
+ "2 Consumer Point of interest database per catego... points_of_interest \n",
+ "3 Census and ACS sociodemographic data estimated... demographics \n",
+ "4 The Consumer Expenditure database consists of ... demographics \n",
+ "5 By analysing feeds from Twitter, Instagram, Me... behavioral \n",
+ "6 Geographic Insights validate, evaluate and ben... financial \n",
+ "7 Point of interest database per categories points_of_interest \n",
+ "8 Curation of socio-demographic and socio-econom... demographics \n",
+ "\n",
+ " country_id data_source_id provider_id \\\n",
+ "0 usa sociodemographics ags \n",
+ "1 usa retailpotential ags \n",
+ "2 usa consumer_points_of_interest pitney_bowes \n",
+ "3 usa sociodemographics ags \n",
+ "4 usa consumer_spending ags \n",
+ "5 usa geosocial_segments spatial_ai \n",
+ "6 usa geographic_insights mastercard \n",
+ "7 esp points_of_interest pitney_bowes \n",
+ "8 esp sociodemographics unica360 \n",
+ "\n",
+ " geography_name \\\n",
+ "0 Census Block Group - United States of America \n",
+ "1 Census Block Group - United States of America ... \n",
+ "2 Latitude/Longitude - United States of America \n",
+ "3 Census Block Group - United States of America ... \n",
+ "4 Census Block Group - United States of America \n",
+ "5 Census Block Group - United States of America ... \n",
+ "6 Census Block Group - United States of America ... \n",
+ "7 Latitude/Longitude - Spain \n",
+ "8 Grid 100m \n",
+ "\n",
+ " geography_description temporal_aggregation \\\n",
+ "0 None yearly \n",
+ "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n",
+ "2 Location of Points of Interest monthly \n",
+ "3 Shoreline clipped TIGER/Line boundaries. More ... yearly \n",
+ "4 None yearly \n",
+ "5 Shoreline clipped TIGER/Line boundaries. More ... quarterly \n",
+ "6 Shoreline clipped TIGER/Line boundaries. More ... monthly \n",
+ "7 Location of Points of Interest monthly \n",
+ "8 100x100m grid yearly \n",
+ "\n",
+ " time_coverage update_frequency is_public_data lang version \\\n",
+ "0 None None False eng 2020 \n",
+ "1 [2018-01-01, 2019-01-01) None False eng 2019 \n",
+ "2 None monthly False eng v1 \n",
+ "3 [2019-01-01, 2020-01-01) None False eng 2019 \n",
+ "4 None None False eng 2020 \n",
+ "5 [2020-01-01, 2020-04-01) quarterly False eng v1 \n",
+ "6 [2019-01-01, 2020-01-01) monthly False eng v1 \n",
+ "7 None monthly False eng v1 \n",
+ "8 None None False eng 2019 \n",
+ "\n",
+ " category_name provider_name \\\n",
+ "0 Demographics Applied Geographic Solutions \n",
+ "1 Demographics Applied Geographic Solutions \n",
+ "2 Points of Interest Pitney Bowes \n",
+ "3 Demographics Applied Geographic Solutions \n",
+ "4 Demographics Applied Geographic Solutions \n",
+ "5 Behavioral Spatial.ai \n",
+ "6 Financial Mastercard \n",
+ "7 Points of Interest Pitney Bowes \n",
+ "8 Demographics Unica360 \n",
+ "\n",
+ " geography_id \\\n",
+ "0 carto-do.ags.geography_usa_blockgroup_2015 \n",
+ "1 carto-do-public-data.carto.geography_usa_block... \n",
+ "2 carto-do.pitney_bowes.geography_usa_latlon_v1 \n",
+ "3 carto-do-public-data.carto.geography_usa_block... \n",
+ "4 carto-do.ags.geography_usa_blockgroup_2015 \n",
+ "5 carto-do-public-data.carto.geography_usa_block... \n",
+ "6 carto-do-public-data.carto.geography_usa_block... \n",
+ "7 carto-do.pitney_bowes.geography_esp_latlon_v1 \n",
+ "8 carto-do.unica360.geography_esp_grid100x100m_2019 \n",
+ "\n",
+ " id \n",
+ "0 carto-do.ags.demographics_sociodemographics_us... \n",
+ "1 carto-do.ags.demographics_retailpotential_usa_... \n",
+ "2 carto-do.pitney_bowes.pointsofinterest_consume... \n",
+ "3 carto-do.ags.demographics_sociodemographics_us... \n",
+ "4 carto-do.ags.demographics_consumerspending_usa... \n",
+ "5 carto-do.spatial_ai.behavioral_geosocialsegmen... \n",
+ "6 carto-do.mastercard.financial_geographicinsigh... \n",
+ "7 carto-do.pitney_bowes.pointsofinterest_pointso... \n",
+ "8 carto-do.unica360.demographics_sociodemographi... "
]
},
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -965,7 +517,7 @@
" 'id': 'carto-do-public-data.carto.derived_spatialfeatures_esp_quadgrid15_v1_yearly_2020'}"
]
},
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -1728,7 +1280,7 @@
"9 312.384817 571.083390 "
]
},
- "execution_count": 6,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -1772,6 +1324,14 @@
"outputId": "06f9f1e2-c68c-46a1-b24c-864509b9ab60"
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "You can find more entities with the Global country filter. To apply that filter run:\n",
+ "\tCatalog().country('glo')\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -1930,7 +1490,7 @@
"4 carto-do-public-data.whos_on_first.geography_g... "
]
},
- "execution_count": 7,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -1987,7 +1547,7 @@
" 'id': 'carto-do-public-data.whos_on_first.geography_glo_geojson_20190520'}"
]
},
- "execution_count": 9,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -2056,19 +1616,19 @@
"