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Nov 05, 2025 — Nov 05, 2026
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Section 1226 updated
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+1358. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 22 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of few more pairs of solar system bodies from Sequence Mining - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327 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^M
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------------------------------------------------------------------------------------------------------------------------------ 1358. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 22 November 2022 - Prediction of Extreme Weather Events based on angular separations-NBody gravitational accelerations of few more pairs of solar system bodies from Sequence Mining - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327 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 ------------------------------------------------------------------------------------------------------------------------------ 1358.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to compute predict_EWE() angular separation search for few more solar system body pairs and new date range 22 November 2022 - 31 December 2022 from Sequence Mined body pairs of HURDAT2 dataset in Section 172 having high class association rule support - Venus-Mercury (19.5798949737%) , Mercury-Jupiter (11.9279819955%) and Jupiter-Venus (11.0277569392%) : climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,22,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodypair':"Venus-Mercury",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,22,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodypair':"Mercury-Jupiter",'angularsepbounds':('0d','30d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,11,22,17,30,00),'dateto':(2022,12,31,17,30,00),'loc':'@0','bodypair':"Jupiter-Venus",'angularsepbounds':('0d','30d')}) 1358.2 Following are dates matching 30 degree angular separations of three pairs above implying precipitation with high probability in geographic areas under seasonal monsoon activity (22 November 2022-8 December 2022 and 16 December 2022-27 December 2022): Angular separation of Venus-Mercury on 2022-11-22 17:30:00.000: [7d36m28.40821885s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-22 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-23 17:30:00.000: [8d42m35.00775519s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-23 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-24 17:30:00.000: [9d50m36.02749517s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-24 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-25 17:30:00.000: [11d00m24.62193288s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-25 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-26 17:30:00.000: [12d12m01.27995635s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-26 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-27 17:30:00.000: [13d25m31.01990856s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-27 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-28 17:30:00.000: [14d41m01.86307955s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-28 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-29 17:30:00.000: [15d58m43.97444152s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-29 17:30:00.000 Angular separation of Venus-Mercury on 2022-11-30 17:30:00.000: [17d18m49.16827706s] Angular separation of Venus-Mercury matches bounds for date: 2022-11-30 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-01 17:30:00.000: [18d41m30.62191374s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-01 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-02 17:30:00.000: [20d07m02.7124476s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-02 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-03 17:30:00.000: [21d35m40.92809131s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-03 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-04 17:30:00.000: [23d07m41.82528595s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-04 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-05 17:30:00.000: [24d43m23.01330957s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-05 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-06 17:30:00.000: [26d23m03.15389377s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-06 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-07 17:30:00.000: [28d07m01.96639836s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-07 17:30:00.000 Angular separation of Venus-Mercury on 2022-12-08 17:30:00.000: [29d55m40.23048009s] Angular separation of Venus-Mercury matches bounds for date: 2022-12-08 17:30:00.000 --------- Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-16 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-17 17:30:00.000: [23d14m54.31296893s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-17 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-18 17:30:00.000: [18d49m25.14077136s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-18 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-19 17:30:00.000: [14d16m44.15034129s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-19 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-20 17:30:00.000: [9d39m31.03469044s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-20 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-21 17:30:00.000: [5d11m50.79478856s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-21 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-22 17:30:00.000: [3d08m19.1974257s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-22 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-23 17:30:00.000: [6d50m35.13973457s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-23 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-24 17:30:00.000: [11d57m44.31447172s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-24 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-25 17:30:00.000: [17d25m46.74770108s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-25 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-26 17:30:00.000: [23d06m18.70854917s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-26 17:30:00.000 Angular separation of Mercury-Jupiter on 2022-12-27 17:30:00.000: [28d56m40.37861651s] Angular separation of Mercury-Jupiter matches bounds for date: 2022-12-27 17:30:00.000 1358.3 An Indian astronomy text - Brihat Samhita of Varahamihira - mentions proximity of Venus-Mercury, Jupiter-Mercury and Jupiter-Venus as major factor influencing rainfall thus concurring with three AprioriGSP Sequence Mined Class Association Rules from HURDAT2 100 year hurricane datasets earlier.
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Updated sections 1351 and 1352
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Section 1351 updated
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Section 1352 updated
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Section 1217 updated
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Section 675 updated
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Updated BestPractices.txt
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+1356. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.6 + Spark 3.3.0 Quadcore Benchmarks - Multiple Consecutive Integers - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355 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 - 16 November 2022
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---------------------------------------------------------------------------------------------------------------------------- 1356. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.6 + Spark 3.3.0 Quadcore Benchmarks - Multiple Consecutive Integers - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355 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 - 16 November 2022 ----------------------------------------------------------------------------------------------------------------------------- 1356.1 Multiple consecutive integers from 9000 to 9100 have been factorized by python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized_MultipleIntegers.py for code changes in Section 1355. 1356.2 Logs and theoretical-actual runtime plots (for O((logN)^2)) are at: testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized_MultipleIntegers.jpg testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized_MultipleIntegers.log.15November2022 1356.3 This commit resolves JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-14
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+1355. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.6 + Spark 3.3.0 Quadcore Benchmarks - Optional Square Root Optimization, Refactoring of Multiple Integer Factorization, Linux Pollard-Rho Factor Command Benchmarks comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325 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 - 15 November 2022
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---------------------------------------------------------------------------------------------------------------------------- 1355. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.6 + Spark 3.3.0 Quadcore Benchmarks - Optional Square Root Optimization, Refactoring of Multiple Integer Factorization, Linux Pollard-Rho Factor Command Benchmarks comparison - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325 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 - 15 November 2022 ----------------------------------------------------------------------------------------------------------------------------- 1355.1 python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been updated to add a boolean parameter StartFromSquareRoot to SearchTiles_and_Factorize(n, k, Parallel_for="False", StartFromSquareRoot="False") for optionally choosing square root optimization (single CPU divmod-sqrt instruction) which begins binary searching for factors from square root of N to N along the rasterized hyperbolic arc tile segments instead of 1 to N. 1355.2 StartFromSquareRoot is passed on as commandline boolean flag to python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py by sys.argv[6]. 1355.3 python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized_MultipleIntegers.py has been changed to import os and DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized modules. Commandline argument sys.argv[2] has been renamed as integerrange from increment. 1355.4 subprocess.call() within python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized_MultipleIntegers.py has been replaced by os.system() for its simpler syntax - subprocess and os.system wrap respectively popen() and system() system calls. Direct invocation of DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.SearchTiles_and_Factorize() is the obvious choice but Spark executors go erratic in cross-module invocation of a spark parallelized routine and since has been commented. 1355.5 NeuronRain computational geometric factorization has been benchmarked versus Pollard-Rho Linux Factor command for following integers and runtimes are tabulated in logs: testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.Python3.10Upgrade.log.15November2022 - 9120291920012909120901920910290192010210291921 - 153 bits NeuronRain - 6.2 seconds: ------------------------- factors_accum._value: [9120291920012909120901920910290192010210291921, 1, 3040097306670969706967306970096730670070097307, 3]
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Section 1325 updated
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Section 1353 updated
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Corrections to Section 1353 - JIRA bug - https://krishnairesearch.atlassian.net/browse/NEUR-13: ---------------------------------------------------------------------------------------------- (*) MultiFractals.py - librosa.load() now accepts music file name as argument instead of hardcoded virtual_piano_music.wav (*) Logs music_pattern_mining/testlogs/AudioToBitMatrix.log.MusicSynthesizer.10November2022 have been rewritten for this updated (*) All waveforms were similar in MFDFA because of hardcoded name earlier - MFDFA has been recomputed for new set of 3 music waveforms and music_pattern_mining/testlogs/MultiFractals_Music_054-SBC-Aanandhamridhakarshini.jpg has been deleted from repository (*) New music waveforms consist of 2 Bach and 1 Virtual Piano Synthesis and MFDFA shows clustering similarity between Bach music versus Function Synthesized Piano Music
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+1353. (THEORY and FEATURE) Intrinsic Merit of Music - MFDFA model of Music Waveforms and Piano notes string generation from mathematical functions - 10 November 2022 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries^M
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------------------------------------------------------------------------------------------------------------------------------ 1353. (THEORY and FEATURE) Intrinsic Merit of Music - MFDFA model of Music Waveforms and Piano notes string generation from mathematical functions - 10 November 2022 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries ------------------------------------------------------------------------------------------------------------------------------ 1353.1 python-src/music_pattern_mining/AudioToBitMatrix.py has been changed to implement new function generate_virtual_piano_notes(function="",length=10, genre="WesternClassical") which evaluates a mathematical function passed on as lambda string (by eval()) and maps each point value of function to a WesternClassical or Carnatic music piano 12-note octave symbol. Complex note patterns could be created by generate_virtual_piano_notes() through choice of appropriate mathematical functions. 1353.2 music_synthesis() is invoked on virtual_piano_notes obtained from generate_virtual_piano_notes() for an example summation of damped sinusoids (in Fourier frequency domain notation): music_synthesis(virtual_piano_notes=generate_virtual_piano_notes(function='(np.iinfo(np.int16).max*math.sin(2*3.1428*720*x) + np.iinfo(np.int16).max*math.sin(2*3.1428*1240*x) + np.iinfo(np.int16).max*math.sin(2*3.1428*2400*x) + np.iinfo(np.int16).max*math.sin(2*3.1428*4800*x))',length=300,genre="WesternClassical"),tempo=0.25) 1353.3 New function music_mfdfa_model(music,order=2,q=41,lagfrom=0.5,lagto=3,lagnum=100) has been defined within MultiFractals.py which loads a music file in librosa and analyzes the music waveform from it as a non-stationary timeseries by MFDFA - function has been parameterized by order of polynomial per segment, lag range and number of exponents(q). Music is non-stationary as mean and variance vary over time depending on notes, pitch and tempo. 1353.4 Following 3 music files belonging to different genres are analyzed by MFDFA: Synthesized by Virtual Piano - music_mfdfa_model("virtual_piano_music.wav",order=2) JSBach Sonata Flute EFlat - music_mfdfa_model("testlogs/Bach_Flute_Sonata_EFlat.mp4",order=2) Amritavarshini - SBC - music_mfdfa_model("testlogs/054-SBC-Aanandhamridhakarshini.mp4",order=2) 1353.5 Following are logs and MultiFractal plots for 3 music genres earlier and similarity of all 3 MFDFA plots are remarkable though they belong to different genres: testlogs/AudioToBitMatrix.log.MusicSynthesizer.10November2022 testlogs/MultiFractals_Music_054-SBC-Aanandhamridhakarshini.jpg testlogs/MultiFractals_Music_Bach_Flute_Sonata_EFlat.jpg testlogs/MultiFractals_Music_virtual_piano_music.jpg
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Section 1285 updated
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Section 1351 updated
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+1352. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 9 November 2022 - MFDFA model of precipitation and bug resolution in predict_EWE() - related to 148,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327 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^M
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------------------------------------------------------------------------------------------------------------------------------- 1352. (JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 9 November 2022 - MFDFA model of precipitation and bug resolution in predict_EWE() - related to 148,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327 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 ------------------------------------------------------------------------------------------------------------------------------- 1352.1 This commit resolves 2 JIRA issues :https://krishnairesearch.atlassian.net/browse/NEUR-11 and https://krishnairesearch.atlassian.net/browse/NEUR-12 1352.2 Function predict_EWE() in python-src/EphemerisSearch.py has been changed to correct the date formats of from and to date range for including time in hour:minute:seconds before passing on to JPL Horizons API as Julian Date object. 1352.3 New function precipitation_mfdfa_model() has been defined in python-src/MultiFractals.py to accept precipitation (rainfall timeseries) history as argument and create an MFDFA model for it. Rainfall and Financial Market timeseries data are non-stationary (their mean and variance is not a constant but seasonal) and fluctuate seasonally depending on Monsoon and Global cues respectively. Usual timeseries analysis by ARMA, ARIMA are suited mostly to stationary timeseries which have predictably constant mean and variance while MFDFA is specific to non-stationary timeseries having varied mean and variance across segments(seasons). 1352.4 GISWeatherAnalytics.py has been updated to redefine climate_analytics(datasource,date="",time="",predict_EWE_params=None,precipitation_timeseries=None) to accept paramaters for predict_EWE() as dictionary and rainfall timeseries as array. Rainfall timeseries is analyzed by precipitation_mfdfa_model() in MultiFractals.py through segments of order 2 polynomials. Clause of N-Body analytics has been updated to do sanity check on date and time and invoke predict_EWE() from EphemerisSearch.py 1352.5 climate_analytics() has been invoked for predicting Extreme Weather Events based on solar system Syzygies for a narrowed down angular separation search ranges for Venus-Mercury (10 degrees) and Sun-Moon (60 degrees) body pairs between 21 October 2022 - 15 December 2022: climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,10,21,17,30,00),'dateto':(2022,12,15,17,30,00),'loc':'@0','bodypair':"Venus-Mercury",'angularsepbounds':('0d','10d')}) climate_analytics(datasource="n-body-analytics",predict_EWE_params={'datefrom':(2022,10,21,17,30,00),'dateto':(2022,12,15,17,30,00),'loc':'@0','bodypair':"Sun-Moon",'angularsepbounds':('120d','180d')}) 1352.6 An example timeseries of North East Monsoon Rainfall from https://mausam.imd.gov.in/chennai/mcdata/hydro.pdf has been modelled by MFDFA: nem_rainfall_timeseries=[2,1,3,1,4,2,1,8,21,10,6,4,8,16,9,14,8,6,10,16,5,2,1,1,1,1,3,4,2,4,14,18,10,10,5,8,2,4] climate_analytics(datasource="precipitation_MFDFA",precipitation_timeseries=nem_rainfall_timeseries) 1352.7 Predictions from predict_EWE() and rainfall MFDFA model have been captured in following logs and images: image_pattern_mining/ImageNet/testlogs/GISWeatherAnalytics.log.9November2022 image_pattern_mining/ImageNet/testlogs/MultiFractals_Precipitation.jpg 1352.8 Rainfall timeseries has been manually gathered earlier as datasets on historic rainfall data for longitude-latitude are available only through premium REST API.
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Section 1325 updated
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Section 1351 updated
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----------------------------------------------------------------------------------------------------------------------------- 1351. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Urban Sprawl Road Network Analytics (OSMnx) based delineation for periurban sprawl of Chennai Metropolitan Area - case study - Corrections to Road Density Measures and Cheeger constant - 7 November 2022 - 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 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, Drone Electronic Voting Machines, Drone Autonomous Delivery, Autonomous Driving, Expander Graphs ----------------------------------------------------------------------------------------------------------------------------- 1351.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to compute basic_stats() including per-square kilometer density measures from circular area based on radius which were excluded in 1350. stats=osmnx.stats.basic_stats(roadgraph,area=math.pi*radius*radius) 1351.2 GeoPandas DataFrame from Road network graph is obtained by graph_to_gdfs() and Roads-Locations are printed as JSONs. 1351.3 In addition to Betweenness Centrality, Cheeger constant or Isoperimetric number which is the measure of bottleneck in a graph (by Theory of Expander Graphs) is computed for minimum weighted vertex cover edge boundary of road network (the set of prominent locations covered by road network edges) by NetworkX edge_expansion() - lower the value of Cheeger constant, higher is the traffic congestion bottleneck and viceversa: print("Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover:",nx.edge_expansion(roadgraph,minweightedvertexcover)) 1351.4 OSMnx graphics have been changed for background,node and edge colors. osmnx.plot_graph(roadgraph,bgcolor="w",node_color="g",edge_color="r") 1351.5 Increased Road Densities imply high Road gravity factor and high built-up surface and volume (or) GHSL BUILT-V and BUILT-S datasets can be indirectly estimated from densities of road networks. Densities and Cheeger constants of Six Road networks in 1350 have been recomputed as below (from testlogs/GISUrbanSprawlAnalytics.log.7November2022) for increased radius of 6 kilometres: urban_sprawl_road_network_OSM(address="Uthiramerur, Tamil Nadu, India",radius=6000) urban_sprawl_road_network_OSM(address="Madurantakam, Tamil Nadu, India",radius=6000) urban_sprawl_road_network_OSM(address="Cheyyur, Tamil Nadu, India",radius=6000) urban_sprawl_road_network_OSM(address="Sholinghur, Tamil Nadu, India",radius=6000) urban_sprawl_road_network_OSM(address="Ranipet, Tamil Nadu, India",radius=6000) urban_sprawl_road_network_OSM(address="Cheyyar, Tamil Nadu, India",radius=6000) ---------------------- Basic statistics about road network graph: {'n': 270, 'm': 706, 'k_avg': 5.229629629629629, 'edge_length_total': 241062.41800000018, 'edge_length_avg': 341.4481841359776, 'streets_per_node_avg': 2.7037037037037037, 'streets_per_node_counts': {0: 0, 1: 55, 2: 0, 3: 185, 4: 30}, 'streets_per_node_proportions': {0: 0.0, 1: 0.2037037037037037, 2: 0.0, 3: 0.6851851851851852, 4: 0.1111111111111111}, 'intersection_count': 215, 'street_length_total': 120531.20900000005, 'street_segment_count': 353, 'street_length_avg': 341.44818413597744, 'circuity_avg': 1.101597529926978, 'self_loop_proportion': 0.0028328611898017, 'node_density_km': 2.3873241463784303, 'intersection_density_km': 1.901017375819861, 'edge_density_km': 2131.4597454658174, 'street_density_km': 1065.7298727329085} Basic statistics about road network graph: {'n': 404, 'm': 930, 'k_avg': 4.603960396039604, 'edge_length_total': 262757.35700000025, 'edge_length_avg': 282.53479247311856, 'streets_per_node_avg': 2.660891089108911, 'streets_per_node_counts': {0: 0, 1: 83, 2: 2, 3: 288, 4: 31}, 'streets_per_node_proportions': {0: 0.0, 1: 0.20544554455445543, 2: 0.0049504950495049506, 3: 0.7128712871287128, 4: 0.07673267326732673}, 'intersection_count': 321, 'street_length_total': 149108.34300000008, 'street_segment_count': 527, 'street_length_avg': 282.93803225806465, 'circuity_avg': 1.1084673595357681, 'self_loop_proportion': 0.0, 'node_density_km': 3.572144278284762, 'intersection_density_km': 2.838263151805467, 'edge_density_km': 2323.2851222395484, 'street_density_km': 1318.4072135939903} Basic statistics about road network graph: {'n': 299, 'm': 774, 'k_avg': 5.177257525083612, 'edge_length_total': 230751.3490000002, 'edge_length_avg': 298.1283578811372, 'streets_per_node_avg': 2.802675585284281, 'streets_per_node_counts': {0: 0, 1: 57, 2: 0, 3: 189, 4: 52, 5: 0, 6: 1}, 'streets_per_node_proportions': {0: 0.0, 1: 0.19063545150501673, 2: 0.0, 3: 0.6321070234113713, 4: 0.17391304347826086, 5: 0.0, 6: 0.0033444816053511705}, 'intersection_count': 242, 'street_length_total': 124342.73900000009, 'street_segment_count': 410, 'street_length_avg': 303.2749731707319, 'circuity_avg': 1.1320665207272225, 'self_loop_proportion': 0.0, 'node_density_km': 2.6437404435820393, 'intersection_density_km': 2.139749790457704, 'edge_density_km': 2040.2898788040618, 'street_density_km': 1099.431197190856} Basic statistics about road network graph: {'n': 192, 'm': 490, 'k_avg': 5.104166666666667, 'edge_length_total': 201640.488, 'edge_length_avg': 411.51120000000003, 'streets_per_node_avg': 2.6458333333333335, 'streets_per_node_counts': {0: 0, 1: 42, 2: 0, 3: 134, 4: 16}, 'streets_per_node_proportions': {0: 0.0, 1: 0.21875, 2: 0.0, 3: 0.6979166666666666, 4: 0.08333333333333333}, 'intersection_count': 150, 'street_length_total': 100820.24399999998, 'street_segment_count': 245, 'street_length_avg': 411.5111999999999, 'circuity_avg': 1.1012718310894227, 'self_loop_proportion': 0.0, 'node_density_km': 1.6976527263135504, 'intersection_density_km': 1.3262911924324612, 'edge_density_km': 1782.8933551478892, 'street_density_km': 891.4466775739444} Basic statistics about road network graph: {'n': 3425, 'm': 8963, 'k_avg': 5.233868613138686, 'edge_length_total': 1061817.5869999996, 'edge_length_avg': 118.46676191007471, 'streets_per_node_avg': 2.6791240875912408, 'streets_per_node_counts': {0: 0, 1: 738, 2: 0, 3: 2316, 4: 365, 5: 6}, 'streets_per_node_proportions': {0: 0.0, 1: 0.21547445255474454, 2: 0.0, 3: 0.6762043795620438, 4: 0.10656934306569343, 5: 0.0017518248175182481}, 'intersection_count': 2687, 'street_length_total': 550363.6500000011, 'street_segment_count': 4564, 'street_length_avg': 120.58800394390909, 'circuity_avg': 1.0641017259116985, 'self_loop_proportion': 0.0008764241893076249, 'node_density_km': 30.28364889387453, 'intersection_density_km': 23.758296227106822, 'edge_density_km': 9388.528757386586, 'street_density_km': 4866.283077533221} Basic statistics about road network graph: {'n': 368, 'm': 942, 'k_avg': 5.119565217391305, 'edge_length_total': 266388.98399999976, 'edge_length_avg': 282.79085350318445, 'streets_per_node_avg': 2.6331521739130435, 'streets_per_node_counts': {0: 0, 1: 79, 2: 0, 3: 266, 4: 23}, 'streets_per_node_proportions': {0: 0.0, 1: 0.21467391304347827, 2: 0.0, 3: 0.7228260869565217, 4: 0.0625}, 'intersection_count': 289, 'street_length_total': 133302.91300000003, 'street_segment_count': 473, 'street_length_avg': 281.82434038054976, 'circuity_avg': 1.0829914709698045, 'self_loop_proportion': 0.0, 'node_density_km': 3.2538343921009716, 'intersection_density_km': 2.5553210307532086, 'edge_density_km': 2355.3957549348765, 'street_density_km': 1178.656529583271} ------------------------------------------------ Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.3684210526315788 Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.0384615384615383 Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.422222222222222 Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.25 Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.024793388429752 Cheeger Constant - Isoperimetric Number - Edge Expansion of urban sprawl road network graph minimum weight vertex cover: 3.3214285714285716 1351.6 This commit resolves two JIRA issues : https://krishnairesearch.atlassian.net/browse/NEUR-9 and https://krishnairesearch.atlassian.net/browse/NEUR-10
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Section 1325 updated
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Section 1350 updated
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+1350. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Urban Sprawl Road Network Analytics (OSMnx) based delineation for periurban sprawl of Chennai Metropolitan Area - a case study - 3 November 2022 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349 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, Drone Electronic Voting Machines, Drone Autonomous Delivery
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------------------------------------------------------------------------------------------------------------------------------ 1350. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Urban Sprawl Road Network Analytics (OSMnx) based delineation for periurban sprawl of Chennai Metropolitan Area - a case study - 3 November 2022 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349 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, Drone Electronic Voting Machines, Drone Autonomous Delivery ------------------------------------------------------------------------------------------------------------------------------ 1350.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to add more parameters to urban_sprawl_road_network_OSM(cityname=None,latx=0,laty=0,longx=0,longy=0,address=None,radius=1000) for additional clauses to accept geocoded address and radius around the address in meters as parameters. 1350.2 OSMnx graph_from_address() is invoked for the address and radius supplied to get the road network subgraph within cicle of radius around an address as a new clause: roadgraph = osmnx.graph.graph_from_address(address,dist=radius) 1350.3 Some very crucial statistics of the road network graph are printed by basic_stats() API of OSMnx which are precisely THE urban sprawl analytics showing various metrics of urban road network density. High road network density implies high urbanization and high built up surface by road gravity factor (1226.9). 1350.4 Minimum weighted vertex cover (minimum weight set of vertices on which atleast one endpoint of every edge of the road network graph is incident) of the road network graph is printed by NetworkX min_weighted_vertex_cover() which in urban sprawl setting translates to the set of prime localities (which could constitute non-residential-commercial central business district) where road network is concentrated and are prone to traffic congestion. 1350.5 Strongly connected components of road network are listed which is a crucial analytics measure in delineating suburban areas where road connectivity is close-knit. 1350.6 Road graph Statistics earlier are computed by urban_sprawl_road_network_OSM() for following 6 contiguous periurban areas (circles of radius 3 km) outside periphery of Chennai Metropolitan Area (5904 sqkm) but are within 130 * 130 sqkm suburban commuting bounding box comparisons for GHSL R2022A BUILT datasets in Sections 1313 and 1320 which showed an abnormal class 21 pixel periurban growth between 2019 and 2022 (~2500 to 2700%): 1350.6.1.urban_sprawl_road_network_OSM(address="Uthiramerur, Tamil Nadu, India",radius=3000) 1350.6.2.urban_sprawl_road_network_OSM(address="Madurantakam, Tamil Nadu, India",radius=3000) 1350.6.3.urban_sprawl_road_network_OSM(address="Cheyyur, Tamil Nadu, India",radius=3000) 1350.6.4.urban_sprawl_road_network_OSM(address="Sholinghur, Tamil Nadu, India",radius=3000) 1350.6.5.urban_sprawl_road_network_OSM(address="Ranipet, Tamil Nadu, India",radius=3000) 1350.6.6.urban_sprawl_road_network_OSM(address="Cheyyar, Tamil Nadu, India",radius=3000) and following are basic road network statistics for aforementioned periurban regions (from python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.3November2022) : ------------------------------------------------------------------------------------ (Uthiramerur) Basic statistics about road network graph: {'n': 17, 'm': 40, 'k_avg': 4.705882352941177, 'edge_length_total': 46082.056, 'edge_length_avg': 1152.0513999999998, 'streets_per_node_avg': 2.764705882352941, 'streets_per_node_counts': {0: 0, 1: 3, 2: 0, 3: 12, 4: 2}, 'streets_per_node_proportions': {0: 0.0, 1: 0.17647058823529413, 2: 0.0, 3: 0.7058823529411765, 4: 0.11764705882352941}, 'intersection_count': 14, 'street_length_total': 23041.028000000006, 'street_segment_count': 20, 'street_length_avg': 1152.0514000000003, 'circuity_avg': 1.1419817746835252, 'self_loop_proportion': 0.05} (Madurantakam) Basic statistics about road network graph: {'n': 230, 'm': 555, 'k_avg': 4.826086956521739, 'edge_length_total': 86681.11200000011, 'edge_length_avg': 156.18218378378398, 'streets_per_node_avg': 2.643478260869565, 'streets_per_node_counts': {0: 0, 1: 48, 2: 0, 3: 168, 4: 14}, 'streets_per_node_proportions': {0: 0.0, 1: 0.20869565217391303, 2: 0.0, 3: 0.7304347826086957, 4: 0.06086956521739131}, 'intersection_count': 182, 'street_length_total': 50129.777, 'street_segment_count': 298, 'street_length_avg': 168.22072818791946, 'circuity_avg': 1.1029625209172427, 'self_loop_proportion': 0.0} (Cheyyur) Basic statistics about road network graph: {'n': 13, 'm': 26, 'k_avg': 4.0, 'edge_length_total': 15784.394000000002, 'edge_length_avg': 607.092076923077, 'streets_per_node_avg': 2.6923076923076925, 'streets_per_node_counts': {0: 0, 1: 2, 2: 0, 3: 11}, 'streets_per_node_proportions': {0: 0.0, 1: 0.15384615384615385, 2: 0.0, 3: 0.8461538461538461}, 'intersection_count': 11, 'street_length_total': 7892.196999999998, 'street_segment_count': 13, 'street_length_avg': 607.0920769230768, 'circuity_avg': 1.0325374283887163, 'self_loop_proportion': 0.0} (Sholinghur) Basic statistics about road network graph: {'n': 140, 'm': 352, 'k_avg': 5.0285714285714285, 'edge_length_total': 61977.237999999976, 'edge_length_avg': 176.0716988636363, 'streets_per_node_avg': 2.592857142857143, 'streets_per_node_counts': {0: 0, 1: 34, 2: 0, 3: 95, 4: 11}, 'streets_per_node_proportions': {0: 0.0, 1: 0.24285714285714285, 2: 0.0, 3: 0.6785714285714286, 4: 0.07857142857142857}, 'intersection_count': 106, 'street_length_total': 30988.619, 'street_segment_count': 176, 'street_length_avg': 176.07169886363636, 'circuity_avg': 1.1007680362993044, 'self_loop_proportion': 0.0} (Ranipet) Basic statistics about road network graph: {'n': 1889, 'm': 4920, 'k_avg': 5.209105346744309, 'edge_length_total': 494615.27199999866, 'edge_length_avg': 100.53155934959322, 'streets_per_node_avg': 2.7046056114346215, 'streets_per_node_counts': {0: 0, 1: 375, 2: 0, 3: 1327, 4: 182, 5: 5}, 'streets_per_node_proportions': {0: 0.0, 1: 0.19851773425092642, 2: 0.0, 3: 0.7024880889359449, 4: 0.09634727368978295, 5: 0.0026469031233456856}, 'intersection_count': 1514, 'street_length_total': 256254.85700000028, 'street_segment_count': 2511, 'street_length_avg': 102.05290999601763, 'circuity_avg': 1.066166105224634, 'self_loop_proportion': 0.0007964954201513341} (Cheyyar) Basic statistics about road network graph: {'n': 156, 'm': 402, 'k_avg': 5.153846153846154, 'edge_length_total': 67737.73400000003, 'edge_length_avg': 168.50182587064683, 'streets_per_node_avg': 2.7884615384615383, 'streets_per_node_counts': {0: 0, 1: 20, 2: 0, 3: 129, 4: 7}, 'streets_per_node_proportions': {0: 0.0, 1: 0.1282051282051282, 2: 0.0, 3: 0.8269230769230769, 4: 0.04487179487179487}, 'intersection_count': 136, 'street_length_total': 33977.28799999999, 'street_segment_count': 203, 'street_length_avg': 167.375802955665, 'circuity_avg': 1.0640883318289263, 'self_loop_proportion': 0.0} 1350.7 Road Networks for earlier 6 periurban regions have been plotted as JPEGs in: python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork1.3November2022.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork2.3November2022.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork3.3November2022.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork4.3November2022.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork5.3November2022.jpeg python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork6.3November2022.jpeg 1350.8 From road network density statistics in 1350.6 periurban regions [1350.6.2],[1350.6.4],[1350.6.5],[1350.6.6] are high ranked and stand out as dense periurban contiguous extensions of Chennai Metropolitan Area sprawl and contribute to suburban commute. Of six regions earlier [1350.6.5 - Ranipet] has highest road network density.
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Sections 1245 and 1349 updated
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