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Nov 05, 2025 — Nov 05, 2026
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1349. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Urban Sprawl Road Network Graph from OpenStreetMap (OSMnx) - 2 November 2022 - related to 823,1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328,1342,1343 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
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----------------------------------------------------------------------------------------------------------------------------- 1349. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Urban Sprawl Road Network Graph from OpenStreetMap (OSMnx) - 2 November 2022 - related to 823,1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328,1342,1343 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 ----------------------------------------------------------------------------------------------------------------------------- 1349.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to import OSMnx python library which optimizes and wraps OpenStreetMap Overpass API for retrieving road network topology for an urban sprawl. 1349.2 Function urban_sprawl_road_network_OSM(cityname=None,latx=0,laty=0,longx=0,longy=0) accepts name of an urban area or longitude-latitude bounding box as parameters and internally invokes OpenStreetMap Overpass API wrappers of OSMnx to get road network graph as NetworkX MultiDiGraph object for those parameters. 1349.3 Nodes (locations) and Ways (roads) connecting the locations are printed along with the most important bottleneck measure of Betweenness Centrality for the road network graph. Betweenness Centrality is the ratio of number of shortest s-t paths passing through a vertex to total number of shortest paths between vertex s and t. Betweenness centrality is ideal metric for transportation networks in which every vertex is assigned a bottleneck centrality score compared to MaxFlow-Mincut (which is the minimum size set of edges when removed disconnect the graph). High betweenness vertices are more likely to be in minimum cut set and might carry major chunk of traffic as major fraction of shortest paths between pairs of localities pass through them. High betweenness vertices are potentially part of central business district as well since they could be arterial highways (thus delineating non-residential-commercial segments). 1349.4 Road network graph for a tiny urban area (Kumbakonam,India) by name (graph_from_place()) and longitude-latitude bounding box (graph_from_bbox()) is computed from OpenStreetMap by OSMnx and has been plotted in python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork.2November2022.jpeg (name) and testlogs/GISUrbanSprawlAnalytics.OSMnx.RoadNetwork2.2November2022.jpeg (bounding box) : #urban_sprawl_road_network_OSM(cityname="Kumbakonam") urban_sprawl_road_network_OSM(latx=11.007927,laty=10.922989,longx=79.456730,longy=79.313908)
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Section 1343 updated
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Updated Requirements.txt
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Updated Requirements.txt
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+1348. (THEORY and FEATURE) MultiFractal Detrended Fluctuation Analysis (MFDFA) - for unearthing Fractal structure within any timeseries data (including Music, Medical Imageing-ECG, Meteorology-Precipitation, Seismology-Tremors, NBody Gravitational Accelerations, Economics-Financial Markets) - 31 October 2022 - related to 690,691,692,1257,1315,1316,1327 and all sections on Music Information Retrieval(MIR), Climate analytics and Weather Forecast, Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Chaos and Fractals, Polynomial Interpolation
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------------------------------------------------------------------------------------------------------------------------------- 1348. (THEORY and FEATURE) MultiFractal Detrended Fluctuation Analysis (MFDFA) - for unearthing Fractal structure within any timeseries data (including Music, Medical Imageing-ECG, Meteorology-Precipitation, Seismology-Tremors, NBody Gravitational Accelerations, Economics-Financial Markets) - 31 October 2022 - related to 690,691,692,1257,1315,1316,1327 and all sections on Music Information Retrieval(MIR), Climate analytics and Weather Forecast, Computational Economics-Quantitative Finance and TimeSeries Analysis, Graphical Event Models (GEM), Chaos and Fractals, Polynomial Interpolation ------------------------------------------------------------------------------------------------------------------------------- 1348.1 This commit implements MultiFractal Detrended Fluctuation Analysis (MFDFA) which oflate has been accepted as de-facto standard in analyzing and extracting fractal self-similarity within timeseries datasets across multiple fields - in python-src/MultiFractals.py 1348.2 Theory behind MultiFractals in a gist - quoted from https://mfdfa.readthedocs.io/en/latest/functions/index.html: "... MFDFA generates a fluctuation function F²(q,s), with s the segment size and q the q-powers, Take a timeseries Xₜ, find the integral Yₜ = cumsum(Xₜ), and segment the timeseries into Nₛ segments of size s. F^2(v,s)=1s∑i=1s[Y(v−1)s+i−yv,i]2, for v=1,2,…,Ns, with yv,i the polynomial fittings of order m. Having obtained the variances of each (detrended) segment, average over s and increase s, to obtain the fluctuation function F2q(s) depending on the segment length. F^2q(s)={1Ns∑v=1/Ns[F2(v,s)]^q/2}^1/q The fluctuation function F^2q(s) can now be plotted in a log-log scale, the slope of the fluctuation function F^2q(s) vs the s-segment size is the self-similarity scaling h(q) ..." 1348.3 In simple terms, timeseries is split into multiple segments,detrended by subtracting mean and each segment is fit to an order-n polynomial. Variances of the polynomials fit to each segment are computed and averaged by raising each variance to fractal exponents q/2 and finding 1/q root of their sum. 1348.4 Every timeseries data used within NeuronRain - Music waveforms, Medical Imagery-Heartbeats, Weather and Seismic models of NBody gravitational acceleration timeseries for Hurricanes-Earthquakes, Stock quote tickers, Event Timeseries of GEM - could be brought under single umbrella of MFDFA models. 1348.5 Function stockquote_mfdfa_model() defined within MultiFractals.py imports Yahoo Finance python package (yfinance), obtains price history of a ticker symbol for a period and intervals, and fits the quotes data by MFDFA python package function: def stockquote_mfdfa_model(ticker,period='2y',interval='1wk',order=2): ... lag, dfa = MFDFA(timeseries, lag=lag, q=q_list, order=order) ... which takes timeseries, lag range of points, list of fractal exponents q and order of polynomial fit to each segment. 1348.6 Function stockquote_mfdfa_model() has been imported within Streaming_StockMarketData.py and Three ticker symbols TSLA,MSFT and AAPL belonging to IT sector have been fit to MFDFA model (2 year history and 1 week interval) and logs-plots (quite similar fluctuations) of the DFA data for each exponent q could be found in: testlogs/MultiFractals_AAPL.jpg testlogs/MultiFractals_MSFT.jpg testlogs/MultiFractals_TSLA.jpg testlogs/Streaming_StockMarketData.MFDFA.log.31OCtober2022 1348.7 Files Streaming_StockMarketData.py and Streaming_TimeSeriesData.py have been updated by autopep8 and 2to3-3.9 tools for indentations and Python 3.9 compliance.
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Updated BestPractices.txt
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Section 1325 updated
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Sections 1344,1345,1346,1347 numbered
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1343. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Verhulste Population Growth Model - Population Estimator 2 - Chennai Metropolitan Area Sprawl Example - 27 October 2022 - related to 823,1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328,1342 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring MAXDNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Fame versus Merit
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Section 1342 updated (3/3)
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Updated Section 1342 (2)
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Section 1342 updated
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1342. (THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - Verhulste Population Growth Model - Chennai Metropolitan Area Sprawl Example - 26 October 2022 - related to 823,1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1 312,1313,1320,1323,1326,1328 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population E stimators (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 Cur ves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color th eorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Povert y alleviation, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fix ed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Fac egraph Urban Growth Models
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------------------------------------------------------------------------------------------------------------------------------- 1342. (THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - Verhulste Population Growth Model - Chennai Metropolitan Area Sprawl Example - 26 October 2022 - related to 823,1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326,1328 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models ------------------------------------------------------------------------------------------------------------------------------ 1342.1 This commit defines a new function verhulste_ricker_population_growth_model(population_at_t1,intrinsic_growth_rate,carrying_capacity,metropolitan_agglomeration) for Verhulste Population Growth Model within GISUrbanSprawlAnalytics.py which is the Population Dynamics equivalent of Ricker Model (implemented in NeuronRain Grafit - course_material/Programming/R/code/Ricker.R) defined in terms of population at time t1, intrinsic growth rate of population and Carrying capacity (or sustainable population): projected_population_at_t2 = population_at_t1 * math.exp(intrinsic_growth_rate*(1 - population_at_t1/carrying_capacity)) 1342.2 Differential Equation for Verhulste Population Growth Model has been defined as: dN/dt = rN(1 - N/K) where N is the population size, r is the intrinsic growth rate K is the carrying capacity of the local environment, and dN/dt, the derivative of N with respect to time t, is the rate of change in population with time. 1342.3 Population Projection is computed based on SEDAC PopEst 3 population data 2020 for 5904 square km Chennai Metropolitan Area post-expansion: verhulste_ricker_population_growth_model(14730872,2.39,30000000,"Chennai Metropolitan Area Population") for 2020 approximate population N=1,47,30,872, intrinsic growth rate r = 2.39% annual, carrying capacity K = 30 million inhabitants (tentative) 1342.4 From testlogs/GISUrbanSprawlAnalytics.log.26October2022: verhulste_ricker_population_growth_model(): projected Chennai Metropolitan Area Population = 49718940.56063712 1342.5 Earlier projected population is not timebound and based on following GIS imagery: 1342.5.1 SEDAC Population Estimation Service v3 - Chennai Metropolitan Area - Approximate Polygon Convex Hull - testlogs/ChennaiMetropolitanAreaExpansion21October2022_SEDAC_PopEst2020.jpeg 1342.5.2 Chennai Metropolitan Area - 5904 sqkm - delineated boundaries for convex hull in 1342.5.1 - testlogs/ChennaiMetropolitanAreaExpansion21October2022.jpeg
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+1328. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - 3D Urban Sprawl Growth Model from BUILT-V and BUILT-S datasets - Digital Elevation Model from Matplotlib3D - Chennai Metropolitan Area Sprawl Example - 19 October 2022 - related to 1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models
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------------------------------------------------------------------------------------------------------------------------------ 1327. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - 3D Urban Sprawl Growth Model from BUILT-V and BUILT-S datasets - Digital Elevation Model from Matplotlib3D - Chennai Metropolitan Area Sprawl Example - 19 October 2022 - related to 1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323,1326 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models ------------------------------------------------------------------------------------------------------------------------------- 1327.1 GISUrbanSprawlAnalytics.py has been changed to plot 3D Digital Elevation Model within three_dimensional_urban_growth_model() by Matplotlib 3D plot_surface() function invoked on (longitudes,latitudes,heights) data from ANBHx = BUVOLx / BUSURFx formula for BUILT-H specified by GHSL R2022A datasets (BUILT-V & BUILT-S) as (X,Y,Z) points (Section 1323.6). 1327.2 Following matplotlib 3D modules have been imported: from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter 1327.3 urban_sprawl_from_raster() returns tuple (urbansprawlstatistics,(rows,cols)) which includes the shape of bounding box. NaN values for heights have been replaced by zeros. 1327.4 Parameters to three_dimensional_urban_growth_model(rasterdata_2d,rasterdata_3d,longx,latx,longy,laty) have been expanded to include bounding box long-lat range. 1327.5 Shape of bounding box and (rows,cols) information are used to create meshgrid for X,Y axes and reshape heights Z axis array to 2D plotted by ax.plot_surface(X, Y, heights, cmap=cm.coolwarm, linewidth=0, antialiased=False) 1327.6 3D Digital Elevation Model (DEM) for Chennai Metropolitan Area Suburban Commuting-aligned Bounding Box - testlogs/GISUrbanSprawlAnalytics.ChennaiMetropolitanArea_3D_DEM.19October2022.jpeg - has been plotted from GHSL R2022A BUILT-V and BUILT-S datasets: r2ddata=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/RemoteSensingGIS/GHS_BUILT_S_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif",dt="BUILT_S R2022A") r3ddata=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/RemoteSensingGIS/GHS_BUILT_V_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif",dt="BUILT_V R2022A")
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Section 1327 updated
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Prediction of EWEs on Python 3.10.4 + AstroPy
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+1327.(JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 17 October 2022 - Predictions of Extreme Weather Events - related to 148,313,445,446,463,1165,1171,1176,1266,1289,1316,1318 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, Large Scale Visuals Recognition, Artificial Intelligence models for Climate Change, Astronomical algorithms
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----------------------------------------------------------------------------------------------------------------------------- 1327.(JIRA,THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 17 October 2022 - Predictions of Extreme Weather Events - related to 148,313,445,446,463,1165,1171,1176,1266,1289,1316,1318 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, Large Scale Visuals Recognition, Artificial Intelligence models for Climate Change, Astronomical algorithms ----------------------------------------------------------------------------------------------------------------------------- 1327.1 This commit resolves two JIRA issues https://krishnairesearch.atlassian.net/browse/NEUR-5 and https://krishnairesearch.atlassian.net/browse/NEUR-6 1327.2 python-src/EphemerisSearch.py has been updated to import Angle and SkyCoord from astropy.coordinates. New function predict_EWE(datefrom,dateto,loc,bodypair,angularsepbounds) has been implemented to obtain dates range from user for a Longitude-Latitude location, Solar system body pair, range of angular separations between the pair and search the dates range for matching angular separations (SkyCoord.separation()) between the pair of celestial bodies by Angle.is_within_bounds() which could be potential days of extreme weather events (heavy rain or cyclonic storms in this case). 1327.3 JIRA Bug NEUR-6 raised earlier concerns erroneous overwrite of datesofEWEs variable and the problem in vectors() invocation on Horizons() object when Longitude-Latitudes are supplied. Problem is due to the lack of support for Longitude-Latitude in vectors_async() code at https://astroquery.readthedocs.io/en/latest/_modules/astroquery/jplhorizons/core.html which raises exception ValueError(('cannot use topographic position in state vectors query')) if location is a dictionary of Longitude-Latitude. As an alternative to dictionary, location could be one of the 2300 pre-defined earth sites in https://ssd.jpl.nasa.gov/horizons/manual.html#center which is a numeric id and not in Longitude-Latitude format. But there doesn't seem to be a comprehensive documentation of 2300 earth site codes except 500 which is geocenter. 1327.4 From AstroQuery JPL Horizons documentation https://astroquery.readthedocs.io/en/latest/jplhorizons/jplhorizons.html only following locations are supported by Horizons.vectors() though ephermerides() supports Longitude-Latitude dictionaries for locations but there is no API support to map ephemerides() data to vectors() format: ------------------------------------------------- Method location refplane refsystem astropy frame ------------------------------------------------- .vectors() '@0' 'ecliptic' N/A 'custombarycentricecliptic' .vectors() '@0' 'earth' N/A 'icrs' .vectors() '@10' 'ecliptic' N/A 'heliocentriceclipticiau76' 1327.5 Because of earlier AstroQuery-AstroPy limitations (or could imply Longitude-Latitude is not necessary for vectors computation) Longitude-Latitude arguments are ignored (ephemerides() invocation has been commented which could be reverted if there is an API support for ephemerides() to vectors() conversion) at present and replaced by hardcoded "@0" as below: obj = Horizons(id=i, location="@0", epochs=Time(str(date[0])+"-"+str(date[1])+"-"+str(date[2])).jd, id_type='id').vectors(refplane="earth") 1327.6 predict_EWE() is invoked to search for matching angular separations between 2 solar system body pairs "Sun-Moon" and "Venus-Mercury" respectively of separation ranges (120-180 degrees) and (0-30 degrees) for dates between 21/10/2022 and 1/12/2022 to match for Sun-Moon-Earth and Venus-Mercury-Earth 3-body syzygies which have been investigated in 1266,1267,1268,1316 and 1318 supra: predict_EWE(datefrom=(2022,10,21,1,00,00),dateto=(2022,12,1,1,00,00),loc='@0',bodypair="Sun-Moon",angularsepbounds=('120d','180d')) predict_EWE(datefrom=(2022,10,21,1,00,00),dateto=(2022,12,1,1,00,00),loc='@0',bodypair="Venus-Mercury",angularsepbounds=('0d','30d')) 1327.7 Following dates match earlier angular separation bound Syzygy queries which coincide with active North-East Monsoon and Bay of Bengal Cyclone seasons in peninsular India predicting heavy rainfall - this forecast differs from MaitreyaEncHoro_RuleSearch.py Sequence Mining - Class Association Rule - based forecast of Section 313 and relies only on patterns found from planetarium search of sky for angular separations learnt from HURDAT2 datasets and does not compute N-Body gravitational accelerations for these dates. Of particular significance are the dates 25/10/2022 and 8/11/2022 which are days of Solar and Lunar Eclipse Syzygies : Angular separation of Sun-Moon matches bounds for date: 2022-10-21 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-22 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-23 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-24 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-25 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-26 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-27 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-28 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-29 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-30 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-10-31 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-01 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-02 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-03 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-04 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-05 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-06 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-07 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-08 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-09 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-10 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-11 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-12 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-13 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-14 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-15 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-16 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-17 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-18 00:00:00.000 Angular separation of Sun-Moon matches bounds for date: 2022-11-19 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-10-28 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-10-29 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-10-30 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-10-31 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-01 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-02 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-03 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-04 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-05 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-06 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-07 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-08 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-09 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-10 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-11 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-12 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-13 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-14 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-15 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-16 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-17 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-18 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-19 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-20 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-21 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-22 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-23 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-24 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-25 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-26 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-27 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-28 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-29 00:00:00.000 Angular separation of Venus-Mercury matches bounds for date: 2022-11-30 00:00:00.000
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Sections 1324,1326 updated
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+1326. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - 3D Urban Sprawl Growth Model from BUILT-V and BUILT-S datasets - Mean,Median,Standard Deviation of Building Hegihts - Chennai Metropolitan Area Sprawl Example - 15 October 2022 - related to 1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models
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------------------------------------------------------------------------------------------------------------------------------ 1326. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - R2022A - 3D Urban Sprawl Growth Model from BUILT-V and BUILT-S datasets - Mean,Median,Standard Deviation of Building Hegihts - Chennai Metropolitan Area Sprawl Example - 15 October 2022 - related to 1174,1175,1177,1179,1194,1211,1234,1290,1291,1292,1295,1296,1297,1301,1304,1312,1313,1320,1323 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, MARA (Multiple Agent Resource Allocation) Facegraph walk coloring DNFSAT algorithm, Preferential Attachment, Fixed Parameter Tractability-Kernelization, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models ------------------------------------------------------------------------------------------------------------------------------- 1326.1 This commit addresses JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-4 - python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to import mean-median-standard deviation functions from statistics. 1326.2 function three_dimensional_urban_growth_model(rasterdata_2d,rasterdata_3d) has been updated to compute heights for each longitude-latitude point in Chennai Metropolitan Area bounding box as ratio of following R2022A 3d to 2d rasters - BUILT-V and BUILT-S: r2ddata=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/RemoteSensingGIS/GHS_BUILT_S_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif",dt="BUILT_S R2022A") r3ddata=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/RemoteSensingGIS/GHS_BUILT_V_P2030LIN_GLOBE_R2022A_54009_100_V1_0_R8_C26.tif",dt="BUILT_V R2022A") 1326.3 Mean-Median-Standard Deviation of Building Heights are computed through statistics module functions. Heights data earlier could be plotted on 3D by https://matplotlib.org/2.0.2/mpl_toolkits/mplot3d/tutorial.html to obtain a Digital Elevation Model(DEM) graphics for an urban sprawl. 1326.4 An example delineation algorithm for urban sprawls in 3D DEM Urban Growth Model which complements (*) Nighttime Lights based delineation (Section 1174) (*) commuting based delineation guided by suburban transport network (Section 1323.8) and (*) periurbanization based delineation (Sections 1320.4.1 and 1320.4.2) could be to cluster regions by building heights and include them within an administrative boundary when they reach threshold heights. 1326.5 Following metrics have been arrived at for BUILT-V and BUILT-S R2022A datasets projected to Chennai Metropolitan Area Sprawl bounding box above (grepped from testlogs/GISUrbanSprawlAnalytics.log.15October2022): ----------------------------------------------------------- Gini Index of Inequality ----------------------------------------------------------- BUILT-S: urbansprawl_gini_coefficient(): Gini Index of the dataset = 0.9031628347857127 BUILT-V: urbansprawl_gini_coefficient(): Gini Index of the dataset = 0.9384512498866854 ------------------------------------------------------------ Mean-Median-Standard Deviation of Heights (BUILT-V/BUILT-S): ------------------------------------------------------------ three_dimensional_urban_growth_model(): mean(heights) = 3.763351991142693 three_dimensional_urban_growth_model(): median(heights) = 2.5014084507042256 three_dimensional_urban_growth_model(): stdev(heights) = 2.6588372379556384
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1325. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.4 + Spark 3.3.0 Quadcore Benchmarks - Hardware Square root instruction optimization and Semiprime Factorization - related to 498,601,1226,1259,1303,1319,1321,1323,1324 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 - 13 October 2022
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1325. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.4 + Spark 3.3.0 Quadcore Benchmarks - Hardware Square root instruction optimization and Semiprime Factorization - related to 498,601,1226,1259,1303,1319,1321,1323,1324 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 - 13 October 2022 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1325.1 python-src/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.py has been updated to get maximum number of factors from commandline. If argv[5] is set to "all" factorization is unlimited and all factors (limited by sys.maxsize) are parallel-planar-point-located. 1325.2 tiles_start is now set to begin from square root of the integer to be factorized instead of 1 earlier (factors are symmetric on either halves of the hyperbolic arc separated by (sqrt(N),sqrt(N)) point) as an optimization because almost all CPUs now support division-square_root instructions in their instruction sets which are implemented by Digit Recurrence Algorithm of low complexity. GCC square root functions internally invoke square root assembly instructions and it has been assumed that Python math.sqrt() internally is bytecode compiled to CPU sqrt instruction. Being hardware implementation square root might only add a polylogarithmic overhead or even less. 1325.3 Another minor bug has been found and fixed in intervalmidpoint=0 clause which was not printing factors though computed correctly - JIRA issue: https://krishnairesearch.atlassian.net/browse/NEUR-3 . 1325.4 Two semiprimes 2505701953 and 2560391 of 32 and 22 bits respectively have been factorized and captured in logs: 1325.4.1 testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log.13October2022 - ('factors of ', 2505701953, '(', 31.222567673873087, ' bits integer) are:') - ( (', 50093, ',', 50021, ') (at ', 'Thu, 13 Oct 2022 12:22:32 GMT', ')') 1325.4.2 testlogs/DiscreteHyperbolicFactorizationUpperbound_TileSearch_Optimized.log2.13October2022 - ('factors of ', 2560391, '(', 21.287932711849106, ' bits integer) are:') - ( (', 1873, ',', 1367, ') (at ', 'Thu, 13 Oct 2022 12:29:13 GMT', ')')
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Updated Requirements.txt
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Section 1324 updated
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+1324. (JIRA,THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.10.4,Python 3.7.5,Spark 3.0.1,Spark 3.3.0 Quadcore Benchmarks - Numba JIT enabled for joblib parallelized tiles creation - Semiprimes,Cunningham's Number - related to 498,601,1226,1259,1303,1319,1321,1323 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 - 13 October 2022
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