|
1 | 1 | samples:
|
2 |
| -- title: Analyzing New York City taxi data using big data tools |
3 |
| - url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391 |
4 |
| - path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb |
5 |
| - thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png |
6 |
| - snippet: Use big data tools to analye NYC taxi data |
7 |
| - description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python. |
8 |
| - licenseInfo: "" |
9 |
| - tags: ["Data Science", "GIS", "Taxi"] |
| 2 | +# - title: Analyzing New York City taxi data using big data tools |
| 3 | +# url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391 |
| 4 | +# path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb |
| 5 | +# thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png |
| 6 | +# snippet: Use big data tools to analye NYC taxi data |
| 7 | +# description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python. |
| 8 | +# licenseInfo: "" |
| 9 | +# tags: ["Data Science", "GIS", "Taxi"] |
10 | 10 | - title: Data Visualization - Construction permits, part 1/2
|
11 | 11 | url: https://www.arcgis.com/home/item.html?id=467bc6806c9e40dc8222744e0937b80c
|
12 | 12 | path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb
|
@@ -116,7 +116,7 @@ samples:
|
116 | 116 | # tags: ["Data Science", "GIS", "Thomas Fire", "California"]
|
117 | 117 | - title: Chennai Floods 2015 - A Geographic Analysis
|
118 | 118 | url: https://www.arcgis.com/home/item.html?id=44c4fc1e56654768840a03971feb1e77
|
119 |
| - path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis.ipynb |
| 119 | + path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis_rn.ipynb |
120 | 120 | thumbnail: ./static/thumbnails/chennai_floods_analysis.jpg
|
121 | 121 | snippet: Analyze the rainfall in Chennai, India
|
122 | 122 | description: This sample showcases not just the analysis and visualization capabilities of your GIS, but also the ability to store illustrative text, graphics and live code in a Jupyter notebook.
|
@@ -320,9 +320,9 @@ samples:
|
320 | 320 | # description: Use the ArcGIS API for Python to answer if wildfires are increasing over time.
|
321 | 321 | # licenseInfo: ""
|
322 | 322 | # tags: ["Data Science", "GIS", "Wildfire"]
|
323 |
| -- title: How much green is Delhi as on 15 Oct 2017? |
| 323 | +- title: How much green is Delhi as on 21 Oct 20122? |
324 | 324 | url: https://www.arcgis.com/home/item.html?id=8094be16f34e46e48880883a1ae6a4f1
|
325 |
| - path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017.ipynb |
| 325 | + path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-21-oct-2022.ipynb |
326 | 326 | thumbnail: ./static/thumbnails/how-much-green-is-Delhi-as-on-15-oct-2017.jpg
|
327 | 327 | snippet: Use Landsat 8 imagery to detect green cover of New Delhi, India
|
328 | 328 | description: This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery.
|
@@ -569,7 +569,7 @@ samples:
|
569 | 569 | tags: ['Data Science', 'GIS', "Geometry"]
|
570 | 570 | - title: Using Geoprocessing Tools
|
571 | 571 | url: https://www.arcgis.com/home/item.html?id=5a5839d87b4645e685bcd46d79995358
|
572 |
| - path: ./samples/02_power_users_developers/using_geoprocessing_tools.ipynb |
| 572 | + path: ./samples/02_power_users_developers/using_geoprocessing_tools_rn.ipynb |
573 | 573 | thumbnail: ./static/thumbnails/using_geoprocessing_tools.png
|
574 | 574 | snippet: using geoprocessing tools
|
575 | 575 | description: The analysis below uses a geoprocessing tool to deduce the path that the debris of a crashed airplane would take if it went down at different places in the ocean.
|
@@ -771,9 +771,9 @@ samples:
|
771 | 771 | licenseInfo: ""
|
772 | 772 | runtime: advanced_gpu
|
773 | 773 | tags: ["Data Science", "GIS", "Hyperspectral", "Deep Learning"]
|
774 |
| -- title: Streams Extraction using MultiTaskRoadExtractor |
| 774 | +- title: Streams Extraction using Deep Learning |
775 | 775 | url: https://www.arcgis.com/home/item.html?id=356899f6baad407b9db49bb526073ee1
|
776 |
| - path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_multi_task_road_extractor.ipynb |
| 776 | + path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_deeplearning.ipynb |
777 | 777 | thumbnail: ./static/thumbnails/default.png
|
778 | 778 | snippet: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
|
779 | 779 | description: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
|
|
0 commit comments