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Analyzed about 23 hours ago. based on code collected 2 days ago.
Oct 21, 2025 — Oct 21, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1665 updated
Srinivasan Kannan
More... 6 months ago
Section 1665 updated
Srinivasan Kannan
More... 6 months ago
1665. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Enterprise and Governance Decision Making from GIS - ORNL LandScan 2024 - 5 May 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching
Srinivasan Kannan
More... 6 months ago
-------------------------------------------------------------------------------------------------- 1665. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Enterprise and Governance Decision Making from GIS - ORNL LandScan 2024 - 5 May 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------- 1665.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to revvert the granularity to 0.01 degrees from 0.005 degrees because of 0.00833 degrees (30 arcseconds) LandScan 2024 resolution mentioned in 1664.7 - 0.01 approximates 0.00833 1665.2 Function urban_sprawl_from_raster(longx,latx,longy,laty,raster,dt,granularity=0.01,delineationparams=None,bboxcentroid=True) has been updated to invoke a new function GIS_decision_making() which is a universal function that computes various statistics variables from GIS data. Enterprise and Governance decision making is the singular most important usecase for massive GIS datasets - Organizations and Governments could automate their business and policy decision making process through AI analytics of GIS datasets. 1665.3 Following are some prominent AI Governance and Business decision making usecase examples from GIS data: 1665.3.1 Governance - Find all densely populated residential area longitude-latitude(s) that might require infrastructure upgrade (housing,roads,water,electricity,transport,sanitation) - For example https://tngis.tn.gov.in/generic_viewer/ is a single-window portal for infrastructure across all departments 1665.3.2 Governance - Find all longitude-latitude(s) with high concentration of natural resources. 1665.3.3 Business - Find all densely built-up volume residential area longitude-latitude(s) that might be market for autonomous drone shopping delivery. 1665.3.4 Business - Find optimal route between source to destination for freight traffic. 1665.4 New function GIS_decision_making() has been defined that computes various analytics variables from GIS dataset. At present two variables "gini_coefficient" and "residential_longlats" have been computed through 2 clauses and might be expanded for arbitrary number of decision variables: GIS_decision_making(urbansprawlstatistics,urbansprawlstatisticsaggregate,longlatstat,variable="gini_coefficient") GIS_decision_making(urbansprawlstatistics,urbansprawlstatisticsaggregate,longlatstat,variable="residential_longlats") 1665.5 Clause for "gini_coefficient" invokes already implemented urbansprawl_gini_coefficient() and clause for "residential_longlats" aggregates all longitude-latitude(s) with population exceeding configurable populationthreshold argument. 1665.6 Full logs for eariler codechanges are at testlogs/GISUrbanSprawlAnalytics.log.5May2026
Srinivasan Kannan
More... 6 months ago
Section 1639 updated
Srinivasan Kannan
More... 6 months ago
Updated NeuronRainUsecases.txt
Srinivasan Kannan
More... 6 months ago
Section 1664 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
1664. (THEORY and FEATURE) GIS Urban Sprawl Analytics - ORNL LandScan 2024 - Population Estimate of Chennai Metropolitan Area Bounding Box with higher granularity and OpenAI GPT-5.5 prompt integration - 29 April 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching
Srinivasan Kannan
More... 6 months ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1664. (THEORY and FEATURE) GIS Urban Sprawl Analytics - ORNL LandScan 2024 - Population Estimate of Chennai Metropolitan Area Bounding Box with higher granularity and OpenAI GPT-5.5 prompt integration - 29 April 2026 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608,1623,1630,1640 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------------------------------------------------- 1664.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has to been updated to import OpenAIQuestionAnswering from NeuronRainApps/QuestionAnswering/QuestionAnswering.py 1664.2 Function def urban_sprawl_from_raster(longx,latx,longy,laty,raster,dt,granularity=0.005,delineationparams=None,bboxcentroid=True) has been changed to increase granularity from 0.01 to 0.005 that would in turn increase the number of longitude-latitude(s) included in the sample. It has to be noted that earlier function estimates population by iteratively summing up data from every long-lat point in the raster bounding box separated by granularity which is less efficient than AI way of population estimation vibe code blocks generated by ClaudeAI and OpenAI in 1630.23 and 1630.24 which require a shape geometry .shp file to mask the raster and later sum-up the non-zero points in masked TIF raster. If .shp file is not available for an urban area convex hull, only an approximation can be obtained from bounding box estimate. Higher long-lat granularity creates more accurate bounding box estimate. 1664.3 New function urban_sprawl_from_ai_models(model="OpenAI",query="") has been defined to query AI models for prompts related to urban sprawls - at present only clause implemented is for OpenAI gpt-5.5 model. 1664.4 Following calls respectively (*) query OpenAI gpt-5.5 for demographics (census 2011) data on few urban areas and (*) estimate population of Chennai metropolitan area from ORNL LandScan 2024 dataset from https://landscan.ornl.gov/ downloaded to testlogs/landscan-global-2024.tif : urban_sprawl_from_ai_models(query="Analyze 2026 population estimates and area of all urban areas in Tamilnadu") r5data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/landscan-global-2024.tif",dt="ORNL LandScan 2024") 1664.5 Logs for earlier changes are at python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.29April2026 - with reduced granularity there are multiple consecutive long-lat point pairs with similar values as in example below for 2 points separated by 0.005 latitude having 180 as population value which may not be duplicates: ... Longitude: 79.271851 Latitude: 12.56925900000002 ORNL LandScan 2024 from raster for longitude-latitude: [array([180], dtype=int32)] ====================testlogs/landscan-global-2024.tif===================== ... Longitude: 79.271851 Latitude: 12.574259000000021 ORNL LandScan 2024 from raster for longitude-latitude: [array([180], dtype=int32)] 1664.6 Following is the population estimate for bounding box - 79.271851,12.439259,80.351257,13.568572 which is a rectangle of 110 km East-West * 125 km North-South spanning 13500 square kms - double the official CMA planning limit of 5904 sqkm: urban_sprawl_from_raster(): urbansprawlstatisitcsaggregate = [46767704] #4.67 crores
Srinivasan Kannan
More... 6 months ago
Section 1630 updated
Srinivasan Kannan
More... 6 months ago
Section 1449 updated
Srinivasan Kannan
More... 6 months ago
AI computed estimates of: (*) Chennai metropolitan area GDP - by ChatGPT (*) Chennai metropolitan area population from ORNL LandScan Ambient/GHSL R2023A/WorldPop Global 2 datasets - by ClaudeAI - includes code
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1662 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1630 updated
Srinivasan Kannan
More... 6 months ago
ChatGPT generated geopandas-rasterio-QGIS code for estimating ambient (floating population included) population of Chennai metropolitan area from ORNL LandScan datasets
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Article in URL https://phys.org/news/2011-02-fair-accurate-elections-statistically.html has been locally cached due to its academic importance
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Claude AI partial review of accessible portions of NeuronRain Theory Drafts
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago
Section 1659 updated
Srinivasan Kannan
More... 6 months ago