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Analyzed about 3 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
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+1534. (JIRA,THEORY and FEATURE) People Analytics - Social Network Analysis - Name paradox implementation generalized to Attribute paradox - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1532 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 1 August 2024
Srinivasan Kannan
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------------------------------------------------------------------------------------------------------------------------------------------- 1534. (JIRA,THEORY and FEATURE) People Analytics - Social Network Analysis - Name paradox implementation generalized to Attribute paradox - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452,1534 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 1 August 2024 ------------------------------------------------------------------------------------------------------------------------------------------- 1534.1 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to change function nameparadox() to attributeparadox() so as to bring all possible people attributes into its fold - attribute name is passed on as additional argument to attributeparadox(). Possible people attributes could be "Names","Genders","Qualifications" etc.,: def attributeparadox(self,number_of_attributes=1000,name_of_attribute="names") 1534.2 Personal Access Token for GitHub codesearch REST API has been updated due to authentication exception: codesearchstats=hranal.codesearch_statistics(opensourceid="shrinivaasanka",personalaccesstoken="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") codesearchstats1=hranal.codesearch_statistics(query="shrinivaasanka+THEORY+and+FEATURE",personalaccesstoken="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") 1534.3 Function attributeparadox() is invoked for 2 population attributes - Names and Genders: hranal.attributeparadox(number_of_attributes=100000,name_of_attribute="names") hranal.attributeparadox(number_of_attributes=3,name_of_attribute="genders") 1534.4 Following are the minimum number of people required for atleast 2 people having same attributes earlier - from python-src/testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.1August2024: attributeparadox(): number of names = 100000 attributeparadox(): Birthday paradox variant for names - Minimum number of people required for atleast 2 people having same names (k) with atleast 50% probability = 373.33007682967144 attributeparadox(): number of genders = 3 attributeparadox(): Birthday paradox variant for genders - Minimum number of people required for atleast 2 people having same genders (k) with atleast 50% probability = 3.099734050626334 1534.5 This update resolves JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-43
Srinivasan Kannan
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Section 1528 updated
K.Srinivasan
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Section 1533 updated
K.Srinivasan
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Section 1533 updated
K.Srinivasan
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Section 222 updated
K.Srinivasan
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Section 1533 updated
K.Srinivasan
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Section 1533 updated - 2
K.Srinivasan
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Section 1533 updated
K.Srinivasan
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+1533. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Updates to Urban Population Growth Model - Naive estimate - CMA example - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching
Srinivasan Kannan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1533. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Updates to Urban Population Growth Model - Naive estimate - CMA example - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------------------------------------------------- 1533.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to lazy import GISLiveTraffic functions on need basis within urban_sprawl_live_traffic(). 1533.2 Function def verhulste_ricker_population_growth_model(population_at_t1,intrinsic_growth_rate,carrying_capacity,metropolitan_agglomeration,fromyear,toyear) has been updated to compute naive population projection based on annual census growth rate (as carrying capacity is quite subjective to define) through compounding equation: Population at year y+n = (Population at year y)*(1+intrinsic_growth_rate)^n within a separate for loop in addition to Verhulste-Ricker population growth model based on carrying capacities. 1533.3 Population projections for Chennai Metropolitan Area (CMA) till 2035 are computed by earlier naive estimate and Verhulste models based on official census 2011 population of CMA which is 122.88 lakhs for 5904 sqkm. This is 13 year old data and is projected till 2035 for annual intrinsic growth rate of 2.39% in both naive and Verhulste models. 1533.4 There is an amazing agreement between population projection for 2022 by naive estimate and offical 2022 population estimate published in 1350.14 - both are 15.9 million in 2022 for 2.39% growth rate and 2024 population projection for CMA is 16.7 million by naive estimate. 1533.5 Carrying capacity has been set to present maximum of 80 million population (Pearl River Delta - https://en.wikipedia.org/wiki/Pearl_River_Delta) and Verhulste model projections are found for 2.39% annual growth rate: print("============================================") print("Based on historical census growth rate:") print("============================================") verhulste_ricker_population_growth_model(12288000,2.39/100.0,80000000,"Chennai Metropolitan Area Population",fromyear=2011,toyear=2035)
Srinivasan Kannan
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File testlogs/ThePopulationOfTheExpandedCMA_census2011.pdf added
Srinivasan Kannan
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Sections 202 and 1532 updated
Srinivasan Kannan
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1532. (JIRA,THEORY and FEATURE) People Analytics - Social Network Analysis - Name Paradox implementation (a variant of birthday paradox) - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 21 July 2024
Srinivasan Kannan
More... over 2 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 1532. (JIRA,THEORY and FEATURE) People Analytics - Social Network Analysis - Name Paradox implementation (a variant of birthday paradox) - related to 14,365,626,711,727,753,876,1127,1128,1129,1130,1204,1263,1269,1427,1442,1452 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,Talent Analytics,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium, Intrinsic Merit as Query Complexity,Software Analytics-Mythical Manmonth-CathedralAndBazaar,Birthday Paradox - 21 July 2024 ------------------------------------------------------------------------------------------------------------------------------------------- 1532.1 python-src/CompressedSensing.py has been updated to import cv2 directly instead of "from cv2". This resolves JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-42 1532.2 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to define a new function def nameparadox(self,number_of_names=1000) which implements the bound for birthday paradox variant for names from Section 5.4.1 of Introduction to Algorithms by [Cormen-Rivest-Leiceson-Stein] - Fourth Edition - pages 140-142 - which is k >= 1 + math.sqrt(1+number_of_names*8*math.log(2))/2 with atleast 50% probability. 1532.3 Above name paradox variant of birthday paradox could be motivated by following problem in people analytics: Let S be a population in a city (Sum of Men and Women headcounts). Find the probability that there is a person with first name X and second name Y in S (second name is Father's name or Surname). 1532.4 Answering problem 1532.3 - Version 1 - Page 140 of Section 5.4.1 in [CLRS-4th edition] discusses this probability for n=365 possible birthdays as independet events. For name paradox knowledge of number of all possible names for human beings is the prerequisite - Following derivation discusses one of the two possibilities for First name and Second name (Woman-Man as First-Second names) and other possibility of Man-Man as First-Second names is symmetric: 1532.4.1 Let number of women = S/2 and number of men = S/2 (if men's population exactly equals that of women) 1532.4.2 Event A: Probability that there is a woman with first name X in S/2 = 2*N1/S where N1 is the number of women first named X in S/2 1532.4.3 Event B: Probability that there is a woman with second name Y in S/2 = 2*N2/S where N2 is the number of men first named Y in S/2 (Woman's second name is the first name of a man) 1532.4.4 Probability that there is a woman with First-Secon name X-Y: 4*N1*N2/(S*S) by Pr(A intersection B) = P(A)P(B) for 2 independent events A and B (A and B may not be independent if cultures are considered) 1532.4.5 In a city of 1 million if N1 = N2 = 1000 each, Pr(A intersection B) = 0.000001 (or 1 in million) 1532.5 Answering problem 1532.3 - Version 2 - from Birthday paradox: Above problem is a variant of Birthday paradox which concerns probability of 2 persons in a group having same birthday - Replacing birthday by name, new paradox (name paradox) is obtained which is the probability of 2 persons in a group having same name. Probability of names for all k people (or birthdays) being distinct is at most 1/2 when k >= (1 + sqrt(1 + (8ln2)n)/2 [where n is number of days per year (birthday paradox) or n is number of all possible names (name paradox)] (or) Probability of atleast 2 people in a gathering to have same names is atleast 1/2 when k is atleast (1+sqrt(1+(8ln2)n)/2. 1532.6 Function nameparadox() is invoked to compute people required for atleast 2 persons to have same name for number of possible names = 100000 with atleast 50% probability as (excerpts of full logs from python-src/testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.21July2024) : nameparadox(): number of names = 100000 nameparadox(): Birthday paradox variant for name - Minimum number of people required for atleast 2 people having same name (k) with atleast 50% probability = 373.33007682967144
Srinivasan Kannan
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Section 1350 updated
K.Srinivasan
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Claude AI and Meta AI Llama LLM Population Projection Examples
K.Srinivasan
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Sections 1377,1450 updated
Srinivasan Kannan
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Section 1450 updated
Srinivasan Kannan
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Section 1531 updated
K.Srinivasan
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Section 1450 corrected - thrice ten is 30
K.Srinivasan
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1531. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - Paths in Air Traffic Dyn amic Graph by Dynetx and Visualization - 15 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,12 91,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397, 1398,1412,1414,1423,1448,1449,1462,1471,1472,1476,1529,1530 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones,EventNet GEM
K.Srinivasan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1531. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - Paths in Air Traffic Dynamic Graph by Dynetx and Visualization - 15 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1476,1529,1530 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones,EventNet GEM -------------------------------------------------------------------------------------------------------------------------------------------- 1531.1 In continuation of earlier air traffic dynamic graph implementation, python-src/image_pattern_mining/ImageNet/GISLiveTraffic.py has been updated to compute all pairs shortest paths in dynamic air traffic graph by NetworkX.all_pairs_shortest_path() 1531.2 Air traffic dynamic graph is plotted as a visual by NetworkX.draw_networkx() (code commented) and DOT file of the dynamic graph is written to DynamicAirTrafficGraph.dot. Vertex names of the air traffic dynamic graph are trimmed to airport codes and exception in Dynetx due to special characters in vertex names has been handled by strip(). 1531.3 EventNet Graphical Event Model Causality could be implemented as an in-memory streaming Dynamic Graph (instead of present EventNet 3-dimensional Tensor text file persistence implementation based on intraevent interaction between actors within an event and interevent causalities between 2 events) through Dynetx timestamped interactions. 1531.4 Logs and air traffic dynamic graph DOT file have been added to repositories as under: testlogs/GISLiveTraffic.log.15July2024 python-src/image_pattern_mining/ImageNet/DynamicAirTrafficGraph.dot 1531.5 Sensitive information has been censored from logs and obfuscated (testlogs/GISLiveTraffic.log.15July2024). Following airport codes with high temporal betweenness centralities are busy transit hubs based on two air traffic dynamic graph snapshots separated by time duration: 1531.5.1 ('BKK', 0.0007246376811594203), ('SIN', 0.0005175983436853002), ('MAD', 0.00031055900621118014), ('CDG', 0.00031055900621118014), ('MLE', 0.00020703933747412008) - not part of the logs testlogs/GISLiveTraffic.log.15July2024 1531.5.2 ('SIN', 0.0004389815627743635), ('MAD', 0.0004389815627743635), ('LHR', 0.0004389815627743635), ('IAD', 0.00021949078138718174), ('SGN', 0.00021949078138718174), ('NRT', 0.00021949078138718174)
K.Srinivasan
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Updated Requirements.txt
K.Srinivasan
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+1530. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - Air Traffic Dynamic Graph by Dynetx - 11 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1476,1529 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones
K.Srinivasan
More... over 2 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1530. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - Air Traffic Dynamic Graph by Dynetx - 11 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1476,1529 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones -------------------------------------------------------------------------------------------------------------------------------------------- 1530.1 python-src/image_pattern_mining/ImageNet/GISLiveTraffic.py has been updated to import datetime, dynetx, networkx and operator for Dynamic Air Traffic Graph instantiation. 1530.2 Function def flightradar24_live_air_traffic(zones=None,max_no_of_flights=10) has been updated to set zones=None. Dynamic Air Traffic Directed Graph object is instantiated from Dynetx which is temporal NetworkX variant for Dynamic Graphs to allow edge additions and deletions at certain timestamps. 1530.3 New temporal flight origin-destination edges called as interactions are added with timestamp by Dynetx add_interaction(). Temporal betweenness centrality of the air traffic dynamic graph is computed by usual betweenness_centrality() function invocation on Dynetx DiGraph object. 1530.4 Sorted temporal betweenness centrality (TBC) measures of airports are printed to testlogs/GISLiveTraffic.log.11July2024 and TBC measure of an airport is proportional to its global importance. 1530.5 Temporal centrality measures equally apply to dynamic traffic graphs of surface transport vehicles (rail and road) as well.
K.Srinivasan
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Section 1450 updated
K.Srinivasan
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Section 1528 updated
K.Srinivasan
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Updated Requirements.txt
K.Srinivasan
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1529. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Realtime Live Air Traffic from FlightRadar24 - FlightRadar24API and pyflightdata - 9 July 2024 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1476 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching,TomTom and HERE live road traffic API,Autonomous Online Shopping Delivery by Cargo drones
K.Srinivasan
More... over 2 years ago