Step 1. challenge question
To load large dataset from remote server into your browser is costly in terms of time.
When you pan the map to another area, your previous loaded data are wasted, since that area is already move out of your browser window. When you pan or zoom back to old area, you have to load everything again.
For small data, no difference, but for large data, cost lots of time to load again.
How to solve this problem in terms of load large dataset?
Step 2.
Google AI is fast, pretty much is collection of previous people's old idea, old experience, not new, not creative, not applicable to this case.
https://gemini.google.com/app/0295e4049f59b959
ChatGPT is just same as google, you can see from URL, it says chatGPT is using paid google customer search. I am using paid google search too. So ChatGPT is equals to Google AI gemini here.
https://chatgpt.com/?utm_source=google&utm_medium=paidsearch_brand&utm_campaign=DEPT_SEM_Google_Brand_Acquisition_NAMER_US_Consumer_CPA_BAU_Mix&utm_term=chatgpt&gad_source=1&gad_campaignid=21714513245&gbraid=0AAAAA-IW-UXBuqv6GBEjX5PmUBCII1bvD&gclid=CjwKCAjw87XBBhBIEiwAxP3_A7HgPn8FEAPcmIbz5OnmcS7qAbSO1ARmoKHr2hCsLiTe5sHqR-IMbhoCqTsQAvD_BwE
Step 3.
According to CalTech research results, human brain information processing speed is 10 Byte / sec.
Human brain is very slow, but can be very creative by jumping between dimension.
If one dimension is not working, human brain can easily backout, withdraw from current dimension and jump to next level higher dimension.
https://transparentgov.net/cleargov1/1828/google-ai-vs-human-brain-google-poi-bottle-neck-issue
Step 4.
Human brain can withdraw from this dimension, from this world, then jump to another dimension, another world on the fly. Inspiration.
As human brain, I withdraw from gis world completely for now.
Then jump to car world on the fly.
How toyota hybrid engine save your gas, double your gas milage?
1. Gas engergy convert to electricity to power electric motor instead of straight gas motor.
2. recycling energy when you break back to battery.
Recycling energy inspire human brain to create a similar thing to recycle previous loaded data into memory to avoid load again.
Human brain can be inspired to create something not exist before. Art, music, painting, is good example.
Step 5.
Let's test how difference is with 830k building polygon (0.8 million)
model 6216, efficent engine with 16x core,
https://transparentgov.net/json2tree/gateway/google-efficient-engine/core-16x.html?layer_id=0&layer=SanBernardino_2021_Buildings¢er_lat=34.065412242446655¢er_long=-117.48159015173016¢er_zoom=12&url=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices%2F2D_Building_Footprints_2021%2FFeatureServer&panto=0
model 6216,
https://transparentgov.net/json2tree/esri/server/folder2.html?url=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices&org=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices&arcgis_online_token=&select_folder=9&select_layer=0&select_folder_text=2D_Building_Footprints_2021+%3Csup%3EFeatureServer%3C%2Fsup%3E&select_layer_text=0+%26%23x21E2%3B+SanBernardino_2021_Buildings+%3Csup%3EFeature+Layer%3Csub%3E+esriGeometryPolygon%3C%2Fsub%3E%3C%2Fsup%3E
Step 6.
Same 830k building polygon (0.8 million)
model 6393, without efficient engine, with single 1x core
https://transparentgov.net/json2tree/datahub.io/embed/google_vertical.html?layer_id=0&layer=SanBernardino_2021_Buildings¢er_lat=33.97815794512721¢er_long=-117.36334039079114¢er_zoom=10&url=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices%2F2D_Building_Footprints_2021%2FFeatureServer&panto=0
model 6393,
https://transparentgov.net/json2tree/esri/server/folder2.html?url=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices&org=https%3A%2F%2Fservices.arcgis.com%2FaA3snZwJfFkVyDuP%2FArcGIS%2Frest%2Fservices&arcgis_online_token=&select_folder=9&select_layer=0&select_folder_text=2D_Building_Footprints_2021+%3Csup%3EFeatureServer%3C%2Fsup%3E&select_layer_text=0+%26%23x21E2%3B+SanBernardino_2021_Buildings+%3Csup%3EFeature+Layer%3Csub%3E+esriGeometryPolygon%3C%2Fsub%3E%3C%2Fsup%3E
