From 6942716da755a97347f7e4a2e61e9d7a9c320734 Mon Sep 17 00:00:00 2001 From: Lingtao Xie Date: Thu, 18 Apr 2024 15:07:40 -0700 Subject: [PATCH] fixed typos --- items_metadata.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/items_metadata.yaml b/items_metadata.yaml index df2c0493a2..4b95095080 100644 --- a/items_metadata.yaml +++ b/items_metadata.yaml @@ -3,7 +3,7 @@ samples: url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391 path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png - snippet: Use big data tools to analye NYC taxi data + snippet: Use big data tools to analyze NYC taxi data description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python. licenseInfo: "" tags: ["Data Science", "GIS", "Taxi"] @@ -35,7 +35,7 @@ samples: url: https://www.arcgis.com/home/item.html?id=acc8b4e5e0d5422d8af19166c1fc21d5 path: ./samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb thumbnail: ./static/thumbnails/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.png - snippet: Analyze growth factors of Arbnb properties in New York + snippet: Analyze growth factors of Airbnb properties in New York description: A study is carried out in this sample notebook to understand the factors that are fuelling widespread growth in the number of Airbnb listings licenseInfo: "" tags: ["Data Science", "GIS", "airbnb"] @@ -225,7 +225,7 @@ samples: # url: https://www.arcgis.com/home/item.html?id=50d6c2001e864d44ab5278e7b439bf41 # path: ./samples/04_gis_analysts_data_scientists/detect_super_blooms_using_satellite_image_classification.ipynb # thumbnail: ./static/thumbnails/detect_super_blooms_using_satellite_image_classification.jpg -# snippet: Determine the occurance of super blooms in the study area for a given year +# snippet: Determine the occurrence of super blooms in the study area for a given year # description: This sample is to study three poppy fields where people often go for watching super blooms, compare the sites with historic scenes, capture the differences in vegetation conditions, and calculate the vegetation density of blooms. # licenseInfo: "" # tags: ["Data Science", "GIS", "Super Blooms", "Classification"] @@ -245,7 +245,7 @@ samples: description: This sample shows how ArcGIS API for Python can be used to train a deep learning model to extract building footprints from drone data. licenseInfo: "" runtime: advanced_gpu - tags: ["Data Science", "GIS", "Building", "Foorprint", "Deep Learning"] + tags: ["Data Science", "GIS", "Building", "Footprint", "Deep Learning"] - title: Extracting Slums from Satellite Imagery url: https://www.arcgis.com/home/item.html?id=5b5461f3df814fc1b65539365668904d path: ./samples/04_gis_analysts_data_scientists/extracting_slums_from_satellite_imagery.ipynb @@ -376,7 +376,7 @@ samples: url: https://www.arcgis.com/home/item.html?id=95236a13179b40c39c9fc01ab96719e3 path: ./samples/04_gis_analysts_data_scientists/locating_a_new_retirement_community.ipynb thumbnail: ./static/thumbnails/locating_a_new_retirement_community.png - snippet: Locate new retirement communites + snippet: Locate new retirement communities description: This sample demonstrates the utility of ArcGIS API for Python to identify some great locations for a new retirement community, which will satisfy these needs of senior citizens. licenseInfo: "" tags: ["Data Science", "GIS", "Retirement", "Community", "Featured"] @@ -393,7 +393,7 @@ samples: # path: ./samples/04_gis_analysts_data_scientists/part2_explore_hurricane_tracks.ipynb # thumbnail: ./static/thumbnails/part2_explore_hurricane_tracks.png # snippet: Analyze aggregate tracks of hurricanes -# description: In this notebook you will analyze the aggregated tracks to investigate the communities that are most affected by hurricanes, as well as as answer important questions about the prevalance of hurricanes, their seasonality, their density, and places where they make landfall. +# description: In this notebook you will analyze the aggregated tracks to investigate the communities that are most affected by hurricanes, as well as as answer important questions about the prevalence of hurricanes, their seasonality, their density, and places where they make landfall. # licenseInfo: "" # tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics", "Part 2"] # - title: Correlation - Hurricane analysis, part 3/3