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Analyzed about 4 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
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+1398. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Google Earth Engine VIIRS City Nightlights Radiance for Longitude-Latitude Bounding Box - Chennai Metropolitan Area periurban region example - 17 February 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397 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, 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^M
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----------------------------------------------------------------------------------------------------------------------------- 1398. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Google Earth Engine VIIRS City Nightlights Radiance for Longitude-Latitude Bounding Box - Chennai Metropolitan Area periurban region example - 17 February 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397 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, 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 ----------------------------------------------------------------------------------------------------------------------------- 1398.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to import json for printing Google Earth Engine Radiance Feature collections. 1398.2 Function urban_sprawl_from_GEE(imagecollection,featurecollection,nightlightsparameter,cities=None,boundingbox=None,daterange=None) has been redefined to include Longitude-Latitude bounding box and date range of imagery as additional parameters for supporting user defined longitude-latitude bounding boxes of urban sprawls and dates filter. New if-else clause has been introduced to handle generic case of longitude-latitude bounding box geometry over Google earth engine imagery by filterBounds() on ee.Geometry.BBox() defined by boundingbox rectangle and EE image collection is filtered for the daterange parameter and usual GAUL city administrative name based VIIRS radiance (Section 1397) is handled in else. 1398.3 Radiance mean values for a date range are accessed directly as JSON string, bypassing usual Featue collection API and iteratively appeneded to radiance array as urbansprawlBBaverageradiance["features"][n]["properties"]["mean"] by JSON dictionary dereferencing. 1398.4 Radiance values are plotted to a graph by matplotlib and following logs and images capture the average radiance of Chennai Metropolitan Area bounding box timeseries (obtained from GUI of http://bboxfinder.com/#12.439259,79.271851,13.568572,80.351257) for daterange 1 November 2021 to 1 February 2023. Timeseries in testlogs/GISUrbanSprawlAnalytics.GEEVIIRS.17February2023.jpeg shows a peak radiance during this daterange which coincides with 2634% boom in Class 21 periurban pixels for same longitude-latitude bounding box of GHSL R2022A GHS-SMOD in 1313: testlogs/GISUrbanSprawlAnalytics.GEEVIIRS.17February2023.jpeg testlogs/GISUrbanSprawlAnalytics.log.17February2023
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+1397. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Google Earth Engine VIIRS City Nightlights Radiance support - 15 February 2023,16 February 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395 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, 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, Autonomous Driving, Expander Graphs,Timeseries analysis
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----------------------------------------------------------------------------------------------------------------------------- 1397. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Google Earth Engine VIIRS City Nightlights Radiance support - 15 February 2023,16 February 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395 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, 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, Autonomous Driving, Expander Graphs,Timeseries analysis ----------------------------------------------------------------------------------------------------------------------------- 1397.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to import Google Earth Engine Python (geemap,ee), Seaborn and Pandas libraries. 1397.2 Code for this commit is based on Tutorial from Worldbank OpenNightLights GitHub - https://worldbank.github.io/OpenNightLights/tutorials/mod5_4_comparing_cities.html - for comparing urban areas by their Nighttime Lights radiance from Google Earth Engine VIIRS dataset. 1397.3 Map functions GEE_city_average_radiance() and GEE_get_date() have been defined to gather radiance data and dates of the VIIRS imagery as per Google Earth Engine MapReduce functionality. 1397.4 New Function urban_sprawl_from_GEE(imagecollection,featurecollection,nightlightsparameter,cities) has been defined to authenticate and initialize Google Earth Engine (requires installation of Google Cloud SDK - https://cloud.google.com/python/docs/reference and Google Earth Engine Python API - https://developers.google.com/earth-engine/guides/python_install) 1397.5 Function urban_sprawl_from_GEE() authenticates and initializes a project "urbansprawlviirsanalytics" in https://console.cloud.google.com/welcome?project=urbansprawlviirsanalytics (pre-created manually in https://cloud.google.com). Array of Cities is searched in Google Earth Engine by ADM1_NAME (Global Administrative Unit Layer - GAUL - defined by UN FAO - https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level1) for their average radiance and formatted to a Pandas dataframe datewise. 1397.6 VIIRS NightLights radiance data from 2014 till 2022 for 3 example cities are searched in Google Earth Engine VIIRS-DNB by urban_sprawl_from_GEE("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG","FAO/GAUL/2015/level1","avg_rad",['Berlin','Seoul','Sao Paulo']) and plotted as timeseries. Through this nightlights are quantified numerically which were so far analyzed visually only through geometric clustering (k-means,DBSCAN,minimum enclosing circles,convex hulls). NightLight radiance could be substituted in data models as deemed necessary e.g GDP regression based on VIIRS radiance in Section 864.1.6 of https://neuronrain-documentation.readthedocs.io/en/latest/ relates GDP of a square bounding box and brightness variation of VIIRS NightLights. 1397.7 Formatted Pandas DataFrames and Seaborn plot of VIIRS radiance values of 3 cities are in: python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.GEEVIIRS.16February2023.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.15February2023
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Section 1142 updated
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+1395. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Isochrones of OSMnx transport network graph as Flood mitigation models - Chennai Metropolitan Area Periurban region example - 14 February 2023 - 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 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, 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, Autonomous Driving, Expander Graphs, Navier-Stokes equation augmented Watershed segmentation algorithm
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Updated BestPractices.txt
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Section 1386 updated
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Section 1386 updated
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Updated Section 1386 updated
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Updated Requirements.txt
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+1388. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 10 February 2023 - N-Body gravity analysis of some extreme weather and seismic events during first week of February 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules
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-------------------------------------------------------------------------------------------------------------------------------------------- 1388. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 10 February 2023 - N-Body gravity analysis of some extreme weather and seismic events during first week of February 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules -------------------------------------------------------------------------------------------------------------------------------------------- 1388.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to analyze N-Body gravitational accelerations for few solar system body conjunctions in Sequence mined class association rules for Earthquakes and Hurricanes (Section 148 and Section 1358) from 31 January 2023 to 10 February 2023 which might be correlated. Three events occurred during this daterange: 1) An unseasonal depression over Bay of Bengal bringing heavy rainfall to east coast of Tamilnadu-India 2) A high intensity Turkiye-Syria earthquake (7.8 richter, Mercalli intensity 11 (maximum 12)) 3) Once in 50000 years Oort cloud Comet ZTF was closest to earth (and its gravity could be non-negligible being a long-tailed comet of few million kilometers) - Following are the n-body conjunctions analyzed for their gravitational effects - prominent among them are Venus-Sun-Mercury (a sequence mined rule and for their alleged role in heavy rainfall) and Sun-Mars(which is a sequence mined rule in few past earthquakes - Sendai 2011,Nepal and Chile 2015) : climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2023,1,31,17,30,00),'dateto':(2023,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Jupiter-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2023,1,31,17,30,00),'dateto':(2023,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Venus-Sun-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2023,1,31,17,30,00),'dateto':(2023,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Jupiter-Venus",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2023,1,31,17,30,00),'dateto':(2023,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Saturn-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2023,1,31,17,30,00),'dateto':(2023,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Mars",'angularsepbounds':('0d','180d')}) 1388.2 Following are the logs and graphics capturing pairwise gravitational intensities derived from above sequence mined rules: testlogs/GISWeatherAnalytics.Jupiter-Mercury.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Jupiter-Venus.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Saturn-Mercury.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Sun-Mars.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Sun-Mercury.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Sun-Saturn.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Venus-Mercury.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.Venus-Sun.Gravity.10February2023.jpg testlogs/GISWeatherAnalytics.log.10February2023 1388.3 Most conspicuous in these graphics are Mercury (sharp declining curve - peak around 1 February 2023), Mars (gradual decline - peak around 1 February 2023) and Venus gravity L2 norms (gradual increase - peak around 10 February 2023) when earlier events unfolded. 1388.4 Following are matching N-Body pairs for above daterange and angular separations of 30 and 180 degrees - Jupiter-Venus separated by <30 degrees (high support of 11.8% in HURDAT2 Hurricane datasets sequence mining - Section 172) and Sun-Mars separated by <180 degrees - These two pairs were also amongst top percentile SequenceMined CARs from USGS Earthquake datasets in Section 148 tantalizingly coinciding with earthquake on 6 February 2023: Angular separation of Jupiter-Venus matches bounds for date: 2023-01-31 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-01 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-02 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-03 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-04 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-05 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-06 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-07 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-08 17:30:00.000 Angular separation of Jupiter-Venus matches bounds for date: 2023-02-09 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-01-31 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-01 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-02 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-03 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-04 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-05 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-06 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-07 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-08 17:30:00.000 Angular separation of Sun-Mars matches bounds for date: 2023-02-09 17:30:00.000
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Updated Requirements.txt
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+1386. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Isochrones of OSMnx transport network graph - Chennai Metropolitan Area Periurban region example - 8 February 2023 - 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 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, 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, Autonomous Driving, Expander Graphs^M
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----------------------------------------------------------------------------------------------------------------------------- 1386. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Isochrones of OSMnx transport network graph - Chennai Metropolitan Area Periurban region example - 8 February 2023 - 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 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, 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, Autonomous Driving, Expander Graphs ----------------------------------------------------------------------------------------------------------------------------- 1386.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to import descartes,shapely widgets and geopandas. 1386.2 Function urban_sprawl_road_network_OSM(cityname=None,latx=0,laty=0,longx=0,longy=0,address=None,radius=1000,defaultcapacity=1,travel_speed=30,trip_times=[10,15,20,25,30]) has been enhanced to accept travel speed and travel times arguments to compute Isochrones which are convex hull areas of a road network that can be covered by travel in a specified time(walk time radius or drive time radius). Isochrones are standard urban planning metrics - more asymmetric the isochrones are more congestion prone is the urban sprawl - ideally isochrones have to be concentric circles for planned cities. Isochrones could be tools for commute based automatic delineation of urban sprawls. 1386.3 Code fragments for drawing Isochrones and Isochrone polygons for periurban regions of Chennai Metropolitan Area have been added to urban_sprawl_road_network_OSM() based on OSMnx documentation. 1386.4 Following are logs capturing OSMnx road network stats and OSMnx road network isochrone images for 7 periurban regions in 1377.4: testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork1.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork1_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork1_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork2.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork2_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork2_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork3.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork3_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork3_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork4.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork4_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork4_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork5.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork5_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork5_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork6.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork6_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork6_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork7.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork7_isochrone.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork7_isochronepoly.8February2023.jpeg testlogs/GISUrbanSprawlAnalytics.log.8February2023
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Section 1255 updated
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Section 1255 updated
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Updated BestPractices.txt
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Updated Section 1255
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Section 1255 updated
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Section 1377 updated
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Section 1236 updated
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Updated Section 1379
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Section 1377 updated
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1379. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore 4093 bits Benchmarks - Self Initializing Quadratic Sieve Factorization and Elliptic Curve Method (SIQS + ECM) comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 18 January 2023
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------------------------------------------------------ 1379. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.7.5 + Spark 3.3.0 Quadcore 4093 bits Benchmarks - Self Initializing Quadratic Sieve Factorization and Elliptic Curve Method (SIQS + ECM) comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369 and all sections on Integer Factorization,Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 18 January 2023 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1379.1 Huge 4093 bits integer 99230238982398298398293883982983982381802910920190290192091029019209102901920910920912091029019209120910920913839289728723872873723782738728378723872379179281928198298192818281928918298992302389823982983982938839829839823818029109201902901920910290192091029019209109209120910290192091209109209138392897287238728737237827387283787238723791792819281982981928182819289182981928981298919923023898239829839829388398298398238180291092019029019209102901920910290192091092091209102901920912091092091383928972872387287372378273872837872387237917928192819829819281828192891829819289923023898239829839829388398298398238180291092019029019209102901920910290192091092091209102901920912091092091383928972872387287372378273872837872387237917928192819829819281828192891829899230238982398298398293883982983982381802910920190290192091029019209102901920910920912091029019209120910920913839289728723872873723782738728378723872379179281928198298192818281928918298192898129891992302389823982983982938839829839823818029109201902901920910290192091029019209109209120910290192091209109209138392897287238728737237827387283787238723791792819281982981928182819289182981928981298911928981298919812989119289899230238982398298398293883982983982381802910928321234299 has been factorized by (*) CADO-NFS GNFS (*) a web factorization application which is a combination of ECM and SIQS optimized for speed - https://www.alpertron.com.ar/ECM.HTM (*) and NeuronRain Computational Geometric Factorization 1379.2 (*) NeuronRain Computational Geometric Factorization found first factor pair (9 and 11025582109155366488699320442553775820200323435576698910232336557689900322435656768990232336557689902323435657093254414302652541524864748747597635985819908809103133144243646475769879810999144709980442553775882093314426647090892123244655877991212254465787892113245456578791212254465787912123245459821433031915414304137536376364865248747087976979920220331325353646587686997992109033213324780433137758871092154266477599804242254565779892113245455877991212254465787899121245455877991212454565787931547663652487476374708697096981985820804213103132535536646586869799210203313254435891544248869982203265377588710915353365676891003224356566989102323365576899010232356566989102323565676899042658774763598587485819808208093096931915324214243646647757697980910321314433247804331377588710921542664775998042422545657798921132454558779912122544657878991212454558779912124545657879315476636524874763747086970969819858208042131031325355366465868697992102033132544236654665811376647109220442548759981093313757558789911322544657878921132454558779912123245457878921132454578789912126488099698582080970804203043031531915413754643646586886997992020313254353664658775699879103220144324423665457698877692248775822033155365987109220442486866990103146803811) in 19.6 seconds (19637529527 nanoseconds - logs captured in DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.GNFS_4093bits.log.18January2023). (*) CADO-NFS GNFS factorization of same number had to be abandoned because of polynomial selection slowdown. (*) SIQS + ECM optimized factorization in https://www.alpertron.com.ar/ECM.HTM was still factorizing at 25 minutes, as this change is committed (logs captured in Alpertron.SIQSPlusECM_4093bits.log.18January2023) 1379.3 This execution completes benchmark of all major factorization algorithm implementations - General Number Field Sieve(GNFS),Elliptic Curve Method(ECM),Pollar-Rho,Quadratic Sieve(QS),Flint,Mathematica,Pari-GP - versus NeuronRain computational geometric factorization though underlying platforms and languages are different and thus comparison may be unfair. Special Number Field Sieve (SNFS) factorization (meant for special form of integers) of Fermat Number F9 (2^2^9 + 1) was also tried and had to be abandoned because of polynomial selection slowdown in CADO-NFS.
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Section 1377 updated
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Section 1223 updated
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1377. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Maxflow-Minxut Bottleneck measure of OSMnx transport network graph - O verlay of GHSL R2022A BUILT-V Visualization and Chennai Metropolitan Area Map - 13 January 2023 - related to 823,1163,1164,1174 ,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1 350,1351,1362,1363 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, Intrinsi c Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequ al Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Eco nomic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviat ion, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urba n Growth Models, Autonomous Driving, Expander Graphs
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