Relevant bibliographies by topics / Call detail record

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Academic literature on the topic 'Call detail record'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Call detail record"

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Mishra, Sejal, and Abhinav Shukla. "A Comparative result-based study on Criminal Call Data Record Analysis." RESEARCH REVIEW International Journal of Multidisciplinary 8, no. 5 (2023): 131–45. http://dx.doi.org/10.31305/rrijm.2023.v08.n05.018.

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All calls that travel through a phone exchange are recorded in detail in call detail records or CDRs. The telephone exchange maintains this CDR, which includes the time of the call, the length of the call, the source and destination numbers, the type of the call, etc. Call data logs are crucial for handling serious criminal cases. Processing of Call Detail Records is now moving towards real-time streaming data. It assists in real-time call detail record analysis, real-time criminal location tracking, and real-time network behaviour analysis. However, the number, diversity, and data rate of these Call Detail Records are enormous, and the current telecom systems were not developed with these challenges in mind. The largest source, which can be viewed as Call Detail Records, can be used (for storage, processing, and analysis). The issues that the telecom sector has with call detail records analysis are the subject of extensive research. In this paper we demonstrate how to use Excel, data mining & graph mining to analyse call detail records of criminal case.
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Jabbar, Ma’shum Abdul, and Suharjito Suharjito. "Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company." Advances in Science, Technology and Engineering Systems Journal 5, no. 4 (2020): 63–69. http://dx.doi.org/10.25046/aj050409.

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Zhang, Sihai, Dandan Yin, Yanqin Zhang, and Wuyang Zhou. "Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record." IEEE Transactions on Emerging Topics in Computing 3, no. 3 (2015): 444–53. http://dx.doi.org/10.1109/tetc.2015.2389614.

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Elayidom, Sudheep, Sasi Gopalan, and Suja C. Nair. "CALL DETAIL RECORD BASED TRAFFIC DENSITY ANALYSIS USING GLOBAL k-MEANS CLUSTERING." International Journal of Intelligent Enterprise 6, no. 3 (2019): 1. http://dx.doi.org/10.1504/ijie.2019.10022805.

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Nair, Suja Chandrasekharan, M. Sudheep Elayidom, and Sasi Gopalan. "Call detail record-based traffic density analysis using global K-means clustering." International Journal of Intelligent Enterprise 7, no. 1/2/3 (2020): 176. http://dx.doi.org/10.1504/ijie.2020.104654.

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Gibbs, Hamish, Anwar Musah, Omar Seidu, et al. "Call detail record aggregation methodology impacts infectious disease models informed by human mobility." PLOS Computational Biology 19, no. 8 (2023): e1011368. http://dx.doi.org/10.1371/journal.pcbi.1011368.

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This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, “all pairs,” is designed to retain long distance network connections while the other, “sequential” methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
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Yuan, Guang, Yanyan Chen, Lishan Sun, Jianhui Lai, Tongfei Li, and Zhuo Liu. "Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data." Journal of Advanced Transportation 2020 (April 2, 2020): 1–16. http://dx.doi.org/10.1155/2020/8956910.

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With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.
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Sejal, Mishra, and Shukla Abhinav. "Use of Graph Technology to Identify Criminal Activity Using Call Data Record." ACCST RESEARCH JOURNAL XXI, no. 1, January 2023 (2023): 31–39. https://doi.org/10.5281/zenodo.7896257.

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&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <em>Crime is a global issue and the first move must be to control it. For a nation to experience healthy, long-term growth, it is essential. We are well aware of the challenges in identifying the criminal domains in the digital world that are constantly influenced by their misdeeds. To stay up with crimes, offenders, and their tactics, police forces across the globe pace themselves continually. The difficulty of sifting through a large amount of data on crimes and criminals has grown significantly for the police department labor force. There is a need for a system that can categorize and thoroughly investigate. Using various strategies and concepts, police personnel can identify and stop crimes in society.&nbsp;In this study, we propose a novel model for investigating how graph technologies might be used to analyze Call Detail Records (CDR) in order to detect potential offenders. We are charged with the arduous task of using an unsupervised data analysis approach without including any prior knowledge about the application context in the model, automatically producing appropriate information from available data. The outcomes of graph data mining are used to create profiling data, identify anomalies, and analyze user behavior.</em>
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Mandić, Marin, Davor Škobić, and Goran Martinović. "Clique Comparison and Homophily Detection in Telecom Social Networks." International journal of electrical and computer engineering systems 9, no. 2 (2019): 81–87. http://dx.doi.org/10.32985/ijeces.9.2.5.

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Social Network Analysis (SNA) is based on graph theory and is used for identification of the structure, behavioral patterns and social connectivity of entities. In this paper, SNA is used in the telecom industry in terms of a call detail record referring to phone call data separated into two groups, i.e., domicile network and virtual operator network data. Emphasis was placed on community detection. Comparison was made among communities detected in domicile and virtual operator networks. Results show that in contrast to domicile network, the number of cliques in the virtual operator network is larger. Also, homophily was detected between domicile network and virtual operator network users.
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Bwambale, Andrew, Charisma Choudhury, and Stephane Hess. "Modelling long-distance route choice using mobile phone call detail record data: a case study of Senegal." Transportmetrica A: Transport Science 15, no. 2 (2019): 1543–68. http://dx.doi.org/10.1080/23249935.2019.1611970.

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Dissertations / Theses on the topic "Call detail record"

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Picornell, Tronch Miguel. "METODOLOGÍA PARA LA EXTRACCIÓN DE PATRONES DE MOVILIDAD URBANA MEDIANTE EL ANÁLISIS DE REGISTROS DE ACTIVIDAD TELEFÓNICA (CALL DETAIL RECORD)." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/88397.

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In the last century, Europe has seen a strong migration from rural to urban areas. Urban mobility is key to the economic and social development of cities, but at the same time it generates a significant number of negative effects such as congestion or air pollution. Understanding urban mobility patterns is essential to evaluate which are the most appropriate policies and measures to achieve sustainable urban development. Most of the empirical studies on urban mobility are based on surveys, since they provide detailed information about population mobility patterns and a large amount of socio-demographic information. However, surveys have several practical limitations (Ortúzar & Willumsen, 2011) such as their high costs and long lead times. The pervasive used of mobile devices opens the opportunity of gather large amounts of anonymised, passively-collected geolocation data overcoming some of the limitations of traditional surveys. Mobile phone data are probably one of the best data sources from which extract population mobility patterns at city scale because of their advantages (large samples, wide spatial coverage, low data collection costs, etc.). The main objective of this research is to contribute to the recent advances in the analysis of mobile phone data by developing and validating a new methodology to extract population activity and mobility patterns in urban areas. The methodology developed present several improvements with respect to previous studies, such as the identification of frequent locations different from home and work, better trip time estimations, sample selection and expansion procedures and improvements on population density estimations. The methodology developed has been tested in three different case studies: (1) estimation of mobility statistics and origin-destination matrices, (2) analysis of the relationship between social network and travel behaviour and (3) evaluation of population exposure to air pollution taking into account population activity and mobility patterns. The results show the potential of mobile phone data to extract information about mobility patterns in urban areas, to better understand the relationship between social network and travel behaviour and to improve population exposure assessment to air pollutants. Despite the potential of mobile phone data to provide rich information about activity and mobility patterns, a number of drawbacks and limitations shall be taken into account. Limitations are mainly related to the spatio-temporal resolution of the data and the limited socio-demographic information available. The results of this research are of great interest for transport planning studies, social network and transport modelling, and population exposure assessments.<br>En el último siglo, Europa ha vivido una fuerte migración del ámbito rural al urbano. La movilidad urbana es fundamental para el desarrollo económico y social de las ciudades pero al mismo tiempo conlleva a una serie de importantes efectos negativos, tales como la congestión o la contaminación del aire. El entendimiento de los patrones de movilidad urbana de los ciudadanos es esencial para que los gestores puedan evaluar cuáles son las políticas y medidas más adecuadas para conseguir un desarrollo urbano sostenible. La mayoría de los estudios empíricos sobre movilidad urbana se apoyan en encuestas. Sin embargo, las encuestas presentan una serie de limitaciones prácticas importantes (Ortúzar & Willumsen, 2011) tales como sus elevados costes económicos o sus largos plazos de ejecución. El uso generalizado de dispositivos móviles por parte de la población proporciona la posibilidad de recoger de manera anónima y pasiva una gran cantidad de información espacio-temporal de una gran muestra de usuarios, superando algunas de las limitaciones de los actuales métodos de recogida de información. En concreto, los datos de la red de telefonía móvil presentan una serie de ventajas que los posicionan como una de las mejores fuentes de datos para el estudio de la movilidad general de grandes núcleos de población (bajos costes de extracción de los datos, gran tamaño de muestra, amplía cobertura espacial, etc.). El objetivo principal de esta investigación es contribuir a los recientes avances en el campo del análisis de los datos de telefonía móvil mediante el desarrollo y validación de una metodología que permita extraer información de patrones de actividad y movilidad de la población en ámbitos urbanos. La metodología desarrollada presenta una serie de mejoras relevantes con respecto a estudios previos, como la estimación de localizaciones frecuentes distintas de casa y trabajo, la mejora en la estimación de la hora del viaje, procedimientos para la selección y expansión de la muestra o la mejora en la estimación del número de personas en un área específica a partir de los patrones de actividad y movilidad de las mismas. Esta metodología ha sido aplicada en tres casos de uso para: (1) la obtención de estadísticas básicas de movilidad y matrices origen-destino en ámbitos urbanos, (2) el análisis de la influencia de la red social en la movilidad y (3) el estudio de la exposición de la población a la contaminación. Los resultados obtenidos demuestran el potencial de los datos de telefonía móvil para extraer información sobre patrones de movilidad en ámbitos urbanos, entender mejor la influencia de la red social en la movilidad y mejorar las estimaciones de exposición de la población a la contaminación. A pesar de las ventajas que proporcionan los datos de telefonía móvil, también se han observado limitaciones relevantes en los distintos estudios realizados, derivadas principalmente de la resolución espacio-temporal de los datos y de la limitada información socio-demográfica disponible. Los resultados de esta investigación son de gran relevancia para estudios de planificación y gestión del transporte, para el desarrollo de nuevos modelos de transporte que tengan en consideración la influencia de la red social en la movilidad y en estudios de evaluación de la exposición de la población a la contaminación.<br>En l'últim segle, Europa ha viscut una forta migració de l'àmbit rural a l'urbà. La mobilitat urbana és fonamental per al desenvolupament econòmic i social de les ciutats però al mateix temps comporta a una sèrie d'importants efectes negatius, com ara la congestió o la contaminació de l'aire. L'entesa dels patrons de mobilitat urbana dels ciutadans és essencial perquè els gestors puguin avaluar quines són les polítiques i mesures més adequades per aconseguir un desenvolupament urbà sostenible. La majoria dels estudis empírics sobre mobilitat urbana es recolzen en enquestes. No obstant això, les enquestes presenten una sèrie de limitacions pràctiques importants (Ortúzar & Willumsen, 2011) com ara els seus elevats costos econòmics o els seus llargs terminis d'execució. L'ús generalitzat de dispositius mòbils per part de la població proporciona la possibilitat de recollir de manera anònima i passiva una gran quantitat d'informació espai-temporal d'una gran mostra d'usuaris, superant algunes de les limitacions dels actuals mètodes de recollida d'informació. En concret, les dades de la xarxa de telefonia mòbil presenten una sèrie d'avantatges que els posicionen com una de les millors fonts de dades per a l'estudi de la mobilitat general de grans nuclis de població (baixos costos d'extracció de les dades, grans dimensions de mostra, amplia cobertura espacial, etc.). L'objectiu principal d'aquesta investigació és contribuir als recents avenços en el camp de l'anàlisi de les dades de telefonia mòbil mitjançant el desenvolupament i validació d'una metodologia que permeti extreure informació de patrons d'activitat i mobilitat de la població en àmbits urbans. La metodologia desenvolupada presenta una sèrie de millores rellevants pel que fa a estudis previs, com l'estimació de localitzacions freqüents diferents de casa i treball, la millora en l'estimació de l'hora del viatge, procediments per a la selecció i expansió de la mostra o la millora en l'estimació del nombre de persones en una àrea específica a partir dels patrons d'activitat i mobilitat de les mateixes. Aquesta metodologia ha estat aplicada en tres casos d'ús per a: (1) l'obtenció d'estadístiques bàsiques de mobilitat i matrius origen-destinació en àmbits urbans, (2) l'anàlisi de la influència de la xarxa social en la mobilitat i (3) l'estudi de l'exposició de la població a la contaminació. Els resultats obtinguts demostren el potencial de les dades de telefonia mòbil per extreure informació sobre patrons de mobilitat en àmbits urbans, entendre millor la influència de la xarxa social en la mobilitat i millorar les estimacions d'exposició de la població a la contaminació. Tot i els avantatges que proporcionen les dades de telefonia mòbil, també s'han observat limitacions rellevants en els diferents estudis realitzats, derivades principalment de la resolució espai-temporal de les dades i de la limitada informació sociodemogràfica disponible. Els resultats d'aquesta investigació són de gran rellevància per a estudis de planificació i gestió del transport, per al desenvolupament de nous models de transport que tinguin en consideració la influència de la xarxa social en la mobilitat i en estudis d'avaluació de l'exposició de la població a la contaminació.<br>Picornell Tronch, M. (2017). METODOLOGÍA PARA LA EXTRACCIÓN DE PATRONES DE MOVILIDAD URBANA MEDIANTE EL ANÁLISIS DE REGISTROS DE ACTIVIDAD TELEFÓNICA (CALL DETAIL RECORD) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88397<br>TESIS
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Hager, Creighton Tsuan-Ren. "Statistical Analysis of ATM Call Detail Records." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/30937.

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Network management is a problem that faces designers and operators of any type of network. Conventional methods of capacity planning or configuration management are difficult to apply directly to networks that dynamically allocate resources, such as Asynchronous Transfer Mode (ATM) networks and emerging Internet Protocol (IP) networks employing Differentiated Services (DiffServ). This work shows a method to generically classify traffic in an ATM network such that capacity planning may be possible. These methods are generally applicable to other networks that support dynamically allocated resources. In this research, Call Detail Records (CDRs) captured from a ¡§live¡¨ ATM network were successfully classified into three traffic categories. The traffic categories correspond to three different video speeds (1152 kbps, 768 kbps, and 384 kbps) in the network. Further statistical analysis was used to characterize these traffic categories and found them to fit deterministic distributions. The statistical analysis methods were also applied to several different network planning and management functions. Three specific potential applications related to network management were examined: capacity planning, traffic modeling, and configuration management.<br>Master of Science
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Wang, Xianrui Roger. "Analysis of ATM Call Detail Records and Recommendations for Standards." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/33763.

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Data network resource management and capacity planning are critical for network design, operation, and management. Equipment vendors often provide good information for traffic management and control and associated tools, but this information and the tools are based on independent, individual switches or routers rather than the whole network. There is a critical need for tools to monitor general resource usage in a network as a whole. In this research, we develop a toolkit to collect ATM Call Detail Records (CDRs) from two types of ATM switches from IBM and FORE Systems. Data records collected by the toolkit can then be used to assess network resource utilization and traffic characteristics with the objective of predicting future needs, making proper network management decisions, and ultimately, assisting in the ability to provide reliable quality of service (QoS) in the network. In addition, we examine current call detail records and requirements for more comprehensive network management and make recommendations for a standardized CDR.<br>Master of Science
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Gupta, Siddharth S. M. Massachusetts Institute of Technology. "Estimating the presence of people in buildings using Call Detail Records." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111437.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 92-97).<br>As geographic data about individual movement become increasingly available, they open LIP the possibility of understanding and modeling urban mobility patterns. While no all-encompassing dataset regarding mobility is available, this study explores how Call Detail Records (CDRs), a highly ubiquitous dataset, can be leveraged to create models that can reproduce mobility patterns observed from time consuming, capital-intensive and infrequent travel surveys. While mechanisms have been proposed for reproducing particular characteristics of individual mobility, this is the first attempt to generate all mobility patterns at fine spatial and temporal scales at the level of individual buildings. Two shortcomings of any dataset include spatial uncertainty at very high resolution and the presence of high-fidelity traces for only a fraction of the population. While the proposed model addressed the former to some extent by providing high accuracy counts at the level of census tracts, a separate method has been explored to address this along with the latter phenomenon. To achieve this, the study leverages hyper-local datasets such as building footprints and places of interest. In the absence of primary datasets, the study is able to provide a model to estimate of the presence of people at the level of individual buildings. Hence, this study provides a pipeline to proceed from high fidelity location traces from a fraction of the population to building level occupancy profiles using fairly ubiquitous data sources.<br>by Siddharth Gupta.<br>S.M. in Transportation
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Rajna, Botond. "Mobility analysis with mobile phone data." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-106949.

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The thesis evaluates mobility based on mobile phone positions. The aim is to develop and assess different methods for travel demand estimation based on CDR data. Besides this estimation location data in cellular data is explained in more detail and a previous work based on mobile phone data and travel demand estimation is reviewed. The different methods of travel time estimation include both static and dynamic estimation. The static travel demand estimation evaluates movements in the city based on predefined time periods, whereas the dynamic estimations are based on different definitions of a trip. A trip can be defined as movements between important places, or just simply count a trip between each position, or a filtering of active states to create more accurate origin-destination matrices. The second part of the thesis includes evaluation of travel time based on CDR data before the final conclusions are drawn. The main finding of the thesis is that it is possible to assess mobility in a city based on CDR data, even if there are no validation data available.
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Leng, Yan Ph D. Massachusetts Institute of Technology. "Urban computing using call detail records : mobility pattern mining, next-location prediction and location recommendation." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104156.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 145-152).<br>Urban computing fuses computer science with other fields, such as transportation, in the context of urban spaces by connecting ubiquitous sensing technologies, analytical models and visualizations to solve challenging problems in urban environment and operation systems. This paper focuses on Call Detail Records, one widely collected opportunistic sensing data source for billing purposes, to understand presence patterns, develop mobility prediction methods and reduce traffic congestions with location recommendations. Understanding human mobility and presence patterns at locations are the building blocks for behavior prediction, service design and system improvements. In the first part, this thesis focuses on 1) understanding presence patterns at user locations with a proposed metric Normalized Hourly Presence, 2) extracting common presence patterns across the population with Principal Component Analysis; 3) and infer home and workplaces using K-means Clustering and Fuzzy C-means Clustering. The proposed method was implemented on MIT Reality Mining data, by which we demonstrate that with inference rates of 56% and 82%, the method can improve 79% and 34% in accuracy respectively in home and workplace inference comparing to the baseline model. In addition, it was implemented on the CDR data collected in a crowded city in China to prove its scalability and applicability in real-world applications. With Fuzzy C-means Clustering, we could flexibly trade-off between inference rate and accuracy to understand the interplay between the two and apply it for various purposes. With an understanding of mobility patterns, the next crucial foundation in urban computing is mobility prediction, enabling transportation practitioners to take actions beforehand and commercial organizations to send location-based advertisements, etc. Specifically, this paper focuses on next-location prediction from Call Detail Records. Mobility traces was analogized to language models, mapping cell towers to words and individual location traces to sentences. Recurrent Neural Network is a successful tool in natural language processing, which is applied in mobility prediction due to its acceptance of sequential input, variable input length and ability to learn the 'meaning' of cell towers. By implementing the method on Call Detail Records collected in Andorra, we show that the method improved more than 40% over the baseline model, with 67% and 78% accuracy in next location at cell tower and merged cell tower level respectively. The 'meanings' of the cell tower could also be inferred, the same as learning the meanings of words in sentences, from the embedding layer of Recurrent Neural Network. The last project aims at tackling the challenge of severe traffic congestions with location recommendations. The availability of large-scale longitudinal geolocation data, such as Call Detail Records, offers planners and service providers an unprecedented opportunity to understand location preferences and alleviate traffic congestions. Location recommendation is a potential tool to achieve these two objectives. Previous research on location recommendations has focused on automatically and accurately inferring users' preferences, while little attention has been devoted to the constraints of service capacity. The ignorance may lead to congestion and long waiting time. We argue that Call Detail Records could help planners and authorities make interventions by providing personalized recommendations given the comprehensive urban-wide picture of historical behaviors and preferences. In this research, we propose a method to make location recommendations for system efficiency, defined as maximizing satisfactions toward recommendations subject to capacity constraints, exploiting travelers' choice flexibilities. We infer implicit location preferences based on sparse and passively-collected Call Detail Records. We then formulate an optimization model the defined system efficiency. As a proof-of-concept experiment, we implement the method in Andorra, a small European country heavily relying on tourism. By extensive simulations, we demonstrate that the method can reduce the travel time increased by congestion during peak hour from 11.73 minutes to 5.6 minutes with idealized trips under full compliance rates. We show that the average travel time increased by congestion is 6.17, 6.98, 8.37 and 10.98 minutes with 80%, 60%, 40% and 20% compliance rates. Overall, our results indicate that Call Detail Records can be used to make locations recommendation while reduce traffic congestion for system efficiency. The proposed method can be applied to other large-scale location traces and extended to other location or events recommendation applications.<br>by Yan Leng.<br>S.M. in Transportation<br>S.M.
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Seppecher, Manon. "Mining call detail records to reconstruct global urban mobility patterns for large scale emissions calculation." Electronic Thesis or Diss., Lyon, 2022. http://www.theses.fr/2022LYSET002.

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En milieu urbain, le trafic routier contribue de manière significative aux émissions atmosphériques, enjeu majeur de la lutte contre le changement climatique. Par conséquent, la surveillance conjointe du trafic routier et des émissions qu’il génère constitue un support essentiel de la décision publique. Au-delà de simples procédures de suivi, les pouvoirs publics ont besoin de méthodes d’évaluation des politiques de transport selon des critères environnementaux.Le couplage de modèles de trafic avec des modèles d’émissions constitue une réponse adaptée à ce besoin. Cependant, l’intégration de tels models à des outils d'aide à la décision nécessite une ca-ractérisation fine et dynamique de la mobilité urbaine. Les données de téléphonie mobile, et en particulier les statistiques d'appel (données CDR), sont une alternative aux données traditionnelles pour estimer cette mobilité. Elles sont riches, massives, et disponibles partout dans le monde. Néanmoins, leur utilisation pour la caractérisation systématique du trafic routier est restée limitée. Cela s'explique par une faible résolution spatiale et des taux d'échantillonnage temporels sensible aux comportements de communication.Cette thèse de doctorat interroge l'estimation des variables de trafic nécessaires au calcul d'émis-sions atmosphériques (distances totales parcourues et vitesses moyennes de trafic) à partir de telles données, et malgré leurs biais. Une première contribution importante est d’articuler des méthodes de classification des individus avec deux approches distinctes de reconstruction de la mobilité. Un seconde contribution est le développement d'une méthode d'estimation des vitesses de trafic basée sur la fusion de larges quantité de données de déplacements. Enfin, un processus méthodologique complet de modélisation et de traitement des données est avancé. Il articule de façon cohérente les méthodes proposées dans cette thèse<br>Road traffic contributes significantly to atmospheric emissions in urban areas, a major issue in the fight against climate change. Therefore, joint monitoring of road traffic and related emissions is essential for urban public decision-making. And beyond this kind of procedure, public authorities need methods for evaluating transport policies according to environmental criteria.Coupling traffic models with traffic-related emission models is a suitable response to this need. However, integrating this solution into decision support tools requires a refined and dynamic char-acterization of urban mobility. Cell phone data, particularly Call Detail Records, are an interesting alternative to traditional data to estimate this mobility. They are rich, massive, and available worldwide. However, their use in literature for systematic traffic characterization has remained limited. It is due to low spatial resolution and temporal sampling rates sensitive to communication behaviors.This Ph.D. thesis investigates the estimation of traffic variables necessary for calculating air emis-sions (total distances traveled and average traffic speeds) from such data, despite their biases. The first significant contribution is to articulate methods of classification of individuals with two distinct approaches of mobility reconstruction. A second contribution is developing a method for estimating traffic speeds based on the fusion of large amounts of travel data. Finally, we present a complete methodological process of modeling and data processing. It relates the methods proposed in this thesis coherently
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Suzudo, Matsumi. "Fundamental understanding of the use of mobile phone data for transport applications." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/212450/1/Matsumi_Suzudo_Thesis.pdf.

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Emerging traffic data sources such as mobile phone data are becoming more and more important for traffic analysis. This masters research is focussed on having a fundamental understanding of mobile phone data and its transport applications. The research first conducts a review of the data and its applications. After the review, an experiment is conducted with some sample mobile phone data, which supports the results of the review. This research reveals the current achievements and limitations on the application of mobile phone data, giving insights for future research.
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Thuillier, Etienne. "Extraction of mobility information through heterogeneous data fusion : a multi-source, multi-scale, and multi-modal problem." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA019.

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Aujourd'hui c'est un fait, nous vivons dans un monde où les enjeux écologiques, économiques et sociétaux sont de plus en plus pressants. Au croisement des différentes lignes directrices envisagées pour répondre à ces problèmes, une vision plus précise de la mobilité humaine est un axe central et majeur, qui a des répercussions sur tous les domaines associés tels que le transport, les sciences sociales, l'urbanisme, les politiques d'aménagement, l'écologie, etc. C'est par ailleurs dans un contexte de contraintes budgétaires fortes que les principaux acteurs de la mobilité sur les territoires cherchent à rationaliser les services de transport, et les déplacements des individus. La mobilité humaine est donc un enjeu stratégique aussi bien pour les collectivités locales que pour les usagers, qu'il faut savoir observer, comprendre, et anticiper.Cette étude de la mobilité passe avant tout par une observation précise des déplacements des usagers sur les territoires. Aujourd'hui les acteurs de la mobilité se tournent principalement vers l'utilisation massive des données utilisateurs. L'utilisation simultanée de données multi-sources, multi-modales, et multi-échelles permet d'entrevoir de nombreuses possibilités, mais cette dernière présente des défis technologiques et scientifiques majeurs. Les modèles de mobilité présentés dans la littérature sont ainsi trop souvent axés sur des zones d'expérimentation limitées, en utilisant des données calibrées, etc. et leur application dans des contextes réels, et à plus large échelle est donc discutable. Nous identifions ainsi deux problématiques majeures qui permettent de répondre à ce besoin d'une meilleure connaissance de la mobilité humaine, mais également à une meilleure application de cette connaissance. La première problématique concerne l'extraction d'informations de mobilité à partir de la fusion de données hétérogènes. La seconde problématique concerne la pertinence de cette fusion dans un contexte réel, et à plus large échelle. Nous apportons différents éléments de réponses à ces problématiques dans cette thèse. Tout d'abord en présentant deux modèles de fusion de données, qui permettent une extraction d'informations pertinentes. Puis, en analysant l'application de ces deux modèles au sein du projet ANR Norm-Atis.Dans cette thèse, nous suivons finalement le développement de toute une chaine de processus. En commençant par une étude de la mobilité humaine, puis des modèles de mobilité, nous présentons deux modèles de fusion de données, et nous analysons leur pertinence dans un cas concret. Le premier modèle que nous proposons permet d'extraire 12 comportements types de mobilité. Il est basé sur un apprentissage non-supervisé de données issues de la téléphonie mobile. Nous validons nos résultats en utilisant des données officielles de l'INSEE, et nous déduisons de nos résultats, des comportements dynamiques qui ne peuvent pas être observés par les données de mobilité traditionnelles. Ce qui est une forte valeur-ajoutée de notre modèle. Le second modèle que nous proposons permet une désagrégation des flux de mobilité en six motifs de mobilité. Il se base sur un apprentissage supervisé des données issues d'enquêtes de déplacements ainsi que des données statiques de description du sursol. Ce modèle est appliqué par la suite aux données agrégés au sein du projet Norm-Atis. Les temps de calculs sont suffisamment performants pour permettre une application de ce modèle dans un contexte temps-réel<br>Today it is a fact that we live in a world where ecological, economic and societal issues are increasingly pressing. At the crossroads of the various guidelines envisaged to address these problems, a more accurate vision of human mobility is a central and major axis, which has repercussions on all related fields such as transport, social sciences, urban planning, management policies, ecology, etc. It is also in the context of strong budgetary constraints that the main actors of mobility on the territories seek to rationalize the transport services and the movements of individuals. Human mobility is therefore a strategic challenge both for local communities and for users, which must be observed, understood and anticipated.This study of mobility is based above all on a precise observation of the movements of users on the territories. Nowadays mobility operators are mainly focusing on the massive use of user data. The simultaneous use of multi-source, multi-modal, and multi-scale data opens many possibilities, but the latter presents major technological and scientific challenges. The mobility models presented in the literature are too often focused on limited experimental areas, using calibrated data, etc., and their application in real contexts and on a larger scale is therefore questionable. We thus identify two major issues that enable us to meet this need for a better knowledge of human mobility, but also to a better application of this knowledge. The first issue concerns the extraction of mobility information from heterogeneous data fusion. The second problem concerns the relevance of this fusion in a real context, and on a larger scale. These issues are addressed in this dissertation: the first, through two data fusion models that allow the extraction of mobility information, the second through the application of these fusion models within the ANR Norm-Atis project.In this thesis, we finally follow the development of a whole chain of processes. Starting with a study of human mobility, and then mobility models, we present two data fusion models, and we analyze their relevance in a concrete case. The first model we propose allows to extract 12 types of mobility behaviors. It is based on an unsupervised learning of mobile phone data. We validate our results using official data from the INSEE, and we infer from our results, dynamic behaviors that can not be observed through traditional mobility data. This is a strong added-value of our model. The second model operates a mobility flows decompositoin into six mobility purposes. It is based on a supervised learning of mobility surveys data and static data from the land use. This model is then applied to the aggregated data within the Norm-Atis project. The computing times are sufficiently powerful to allow an application of this model in a real-time context
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Hammami, Seif Eddine. "Dynamic network resources optimization based on machine learning and cellular data mining." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0015/document.

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Les traces réelles de réseaux cellulaires représentent une mine d’information utile pour améliorer les performances des réseaux. Des traces comme les CDRs (Call detail records) contiennent des informations horodatées sur toutes les interactions des utilisateurs avec le réseau sont exploitées dans cette thèse. Nous avons proposé des nouvelles approches dans l’étude et l’analyse des problématiques des réseaux de télécommunications, qui sont basé sur les traces réelles et des algorithmes d’apprentissage automatique. En effet, un outil global d’analyse de données, pour la classification automatique des stations de base, la prédiction de la charge de réseau et la gestion de la bande passante est proposé ainsi qu’un outil pour la détection automatique des anomalies de réseau. Ces outils ont été validés par des applications directes, et en utilisant différentes topologies de réseaux comme les réseaux WMN et les réseaux basés sur les drone-cells. Nous avons montré ainsi, qu’en utilisant des outils d’analyse de données avancés, il est possible d’optimiser dynamiquement les réseaux mobiles et améliorer la gestion de la bande passante<br>Real datasets of mobile network traces contain valuable information about the network resources usage. These traces may be used to enhance and optimize the network performances. A real dataset of CDR (Call Detail Records) traces, that include spatio-temporal information about mobile users’ activities, are analyzed and exploited in this thesis. Given their large size and the fact that these are real-world datasets, information extracted from these datasets have intensively been used in our work to develop new algorithms that aim to revolutionize the infrastructure management mechanisms and optimize the usage of resource. We propose, in this thesis, a framework for network profiles classification, load prediction and dynamic network planning based on machine learning tools. We also propose a framework for network anomaly detection. These frameworks are validated using different network topologies such as wireless mesh networks (WMN) and drone-cell based networks. We show that using advanced data mining techniques, our frameworks are able to help network operators to manage and optimize dynamically their networks
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Books on the topic "Call detail record"

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Harte, Lawrence, and Avi Ofrane. Introduction To Telecom Billing, Usage Events, Call Detail Records, And Billing Cycles. Althos, 2004.

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Beyene, Surafel. Customer Clustering for a Mobile Telecommunications Company Based on Call Detail Records. Independently Published, 2021.

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Publisher, Creative Designs. Message Log : Telephone Call Log Book : Red & Gold Cover | Phone Call Log Book: 110 Pages To Record Messages, Call History, Details, Follow-Ups Telephone Memo ... Per Page. Createspace Independent Publishing Platform, 2017.

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Mcclallen, Filomena. Simplify Cellular Network Concepts : Systematic Methods to Prepare and Analyze Call Detail Records: Understand Historical Cellular Telephone Data. Independently Published, 2021.

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Ganie, Aasim Ur Rehman, and Irtifa Mukhter Aarif Hussain. Emerging Social Work Debates. Clever Fox Publishing, 2022. http://dx.doi.org/10.52184/cfox.2022.1505.

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Case study is an important pedagogical tool not only to facilitate classroom teaching, but is also a research tool used widely in academia and industry. Every workplace situation calls for decision making and managerial skill. While some situations are more complex and far-reaching than the others, all decisions are equally important for the businesses in the overall landscape. On one hand, strategic decisions call for sharp business acumen and experience; on the other hand, operational decisions call for tact and eye for detail. Businesses employ unique solutions to solve their problem which is often recorded as a case study. These case studies are an effective tool to enhance learning. It stimulates the students to integrate classroom-learning with application orientation to solve real live problems. The growth in case writers coupled with availability of good cases has made industry and academia to embrace case methods. An initiative to support and encourage build indigenous case studies, this book is a compilation of the cases presented at the Management Case Conference organised by PSG Institute of Management in 2021
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Telephone Call Log Book: Phone Tracker Book to Record Details of Messages for Offices, Small Businesses, Companies and Organisations. Independently Published, 2021.

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publishing, Morales calving. Beef Calving Record Book: Farm Record Book to Track Your Calves, Log Book to Keep Track and Record Your Cattle, Cow Ledger, Bull and Calf Details, Breeding, Feed, Health and More. Independently Published, 2021.

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publishing, Morales calving. Beef Calving Record Book: Farm Record Book to Track Your Calves, Log Book to Keep Track and Record Your Cattle, Cow Ledger, Bull and Calf Details, Breeding, Feed, Health and More. Independently Published, 2021.

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publishing, Morales calving. Beef Calving Record Book: Farm Record Book to Track Your Calves, Log Book to Keep Track and Record Your Cattle, Cow Ledger, Bull and Calf Details, Breeding, Feed, Health and More. Independently Published, 2021.

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publishing, Morales calving. Beef Calving Record Book: Farm Record Book to Track Your Calves, Log Book to Keep Track and Record Your Cattle, Cow Ledger, Bull and Calf Details, Breeding, Feed, Health and More. Independently Published, 2021.

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Book chapters on the topic "Call detail record"

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Ayesha, Buddhi, Bhagya Jeewanthi, Charith Chitraranjan, Amal Shehan Perera, and Amal S. Kumarage. "User Localization Based on Call Detail Record." In Intelligent Data Engineering and Automated Learning – IDEAL 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33607-3_45.

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Cochinwala, Munir, and Euthimios Panagos. "Near Real–Time Call Detail Record ETL Flows." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14559-9_9.

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Rafało, Mariusz. "Call Detail Record Generator for Modeling Real-World Telecommunication Scenarios." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94193-1_22.

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Zheng, Lejing, Liguang Su, and Honghui Dong. "Urban Rail Transit Passenger Flow Monitoring Method Based on Call Detail Record Data." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2914-6_45.

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Kuchler, Theresa, and Johannes Stroebel. "Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science." In Handbook of Computational Social Science for Policy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16624-2_21.

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AbstractWe argue that the increasing availability of digital trace data presents substantial opportunities for researchers and policy makers to better understand the importance of social networks and social interactions in fostering economic opportunity and resilience. We review recent research efforts that have studied these questions using data from a wide range of sources, including online social networking platform such as Facebook, call detail record data, and network data from payment systems. We also describe opportunities for expanding these research agendas by using other digital trace data, and discuss various promising paths to increase researcher access to the required data, which is often collected and owned by private corporations.
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Zhang, Huiqi, and Ram Dantu. "Event Detection Based on Call Detail Records." In Behavior Computing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2969-1_19.

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Zhang, Huiqi, and Ram Dantu. "Discovery of Social Groups Using Call Detail Records." In On the Move to Meaningful Internet Systems: OTM 2008 Workshops. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88875-8_72.

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Alaggan, Mohammad, Sébastien Gambs, Stan Matwin, and Mohammed Tuhin. "Sanitization of Call Detail Records via Differentially-Private Bloom Filters." In Data and Applications Security and Privacy XXIX. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20810-7_15.

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Dong, Yuxiao, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, and Nitesh V. Chawla. "Inferring Unusual Crowd Events from Mobile Phone Call Detail Records." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_29.

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Bozcaga, Tugba, Fotini Christia, Elizabeth Harwood, Constantinos Daskalakis, and Christos Papademetriou. "Syrian Refugee Integration in Turkey: Evidence from Call Detail Records." In Guide to Mobile Data Analytics in Refugee Scenarios. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12554-7_12.

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Conference papers on the topic "Call detail record"

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Ma, Qingli, Wen Wang, Qing Yao, Jingdi Zhou, and Lei Quo. "Factor analysis on call detail record." In 2018 27th Wireless and Optical Communication Conference (WOCC). IEEE, 2018. http://dx.doi.org/10.1109/wocc.2018.8372724.

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Qin, Siyang, Youchen Zuo, Yaguan Wang, Xuan Sun, and Honghui Dong. "Travel trajectories analysis based on call detail record data." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978454.

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Wang, Xuzhao, Honghui Dong, Yue Zhou, Kai Liu, Limin Jia, and Yong Qin. "Travel distance characteristics analysis using call detail record data." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7979109.

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Sikder, Ratul, Md Jamal Uddin, and Sajal Halder. "An efficient approach of identifying tourist by call detail record analysis." In 2016 International Workshop on Computational Intelligence (IWCI). IEEE, 2016. http://dx.doi.org/10.1109/iwci.2016.7860354.

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Chen, Shu, Hongwei Wu, Lai Tu, and Benxiong Huang. "Identifying Hot Lines of Urban Spatial Structure Using Cellphone Call Detail Record Data." In 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence & Computing and 2014 IEEE 11th Intl Conf on Autonomic & Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2014. http://dx.doi.org/10.1109/uic-atc-scalcom.2014.88.

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Baharim, Khairul Nizam, Mohd Shafri Kamaruddin, and Faeizah Jusof. "Leveraging Missing Values in Call Detail Record Using Naïve Bayes for Fraud Analysis." In 2008 International Conference on Information Networking. IEEE, 2008. http://dx.doi.org/10.1109/icoin.2008.4472791.

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Vatasoiu, Robert Ionut, Alexandru Vulpe, Robert Florescu, Mari-Anais Sachian, and George Suciu. "Developing a Call Detail Record Generator for Cultural Heritage Preservation and Theft Mitigation: Applications and Implications." In ARES 2024: The 19th International Conference on Availability, Reliability and Security. ACM, 2024. http://dx.doi.org/10.1145/3664476.3669915.

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Siangzhee, Nipakorn, and Tiranee Achalakul. "Performance and reliability improvement of the call detail record processing system: A case study from a telecommunication enterprise." In TENCON 2009 - 2009 IEEE Region 10 Conference. IEEE, 2009. http://dx.doi.org/10.1109/tencon.2009.5395878.

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Tauriainen, Anssi. "Can you hear me now? A call detail record based end-to-end diagnostics system for mobile networks." In 2019 15th International Conference on Network and Service Management (CNSM). IEEE, 2019. http://dx.doi.org/10.23919/cnsm46954.2019.9012749.

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Zhou, Chen, Zhengguang Xu, and Benxiong Huang. "Activity Recognition from Call Detail Record: Relation Between Mobile Behavior Pattern and Social Attribute Using Hierarchical Conditional Random Fields." In Int'l Conference on Cyber, Physical and Social Computing (CPSCom). IEEE, 2010. http://dx.doi.org/10.1109/greencom-cpscom.2010.141.

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Reports on the topic "Call detail record"

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Rathinam, Francis, P. Thissen, and M. Gaarder. Using big data for impact evaluations. Centre of Excellence for Development Impact and Learning (CEDIL), 2021. http://dx.doi.org/10.51744/cmb2.

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The amount of big data available has exploded with recent innovations in satellites, sensors, mobile devices, call detail records, social media applications, and digital business records. Big data offers great potential for examining whether programmes and policies work, particularly in contexts where traditional methods of data collection are challenging. During pandemics, conflicts, and humanitarian emergency situations, data collection can be challenging or even impossible. This CEDIL Methods Brief takes a step-by-step, practical approach to guide researchers designing impact evaluations based on big data. This brief is based on the CEDIL Methods Working Paper on ‘Using big data for evaluating development outcomes: a systematic map’.
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.

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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Kountze, Texas where representatives gathered from BITH, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. The project acquired new 2014 orthoimagery (30-cm, 4-band (RGB and CIR)) from the Hexagon Imagery Program. Supplemental imagery for the interpretation phase included Texas Natural Resources Information System (TNRIS) 2015 50 cm leaf-off 4-band imagery from the Texas Orthoimagery Program (TOP), Farm Service Agency (FSA) 100-cm (2016) and 60 cm (2018) National Aerial Imagery Program (NAIP) imagery, and current and historical true-color Google Earth and Bing Maps imagery. In addition to aerial and satellite imagery, 2017 Neches River Basin Light Detection and Ranging (LiDAR) data was obtained from the United States Geological Survey (USGS) and TNRIS to analyze vegetation structure at BITH. The preliminary vegetation classification included 110 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 304 plots surveyed between 2016 and 2019 and 110 additional observation plots. The final vegetation classification includes 75 USNVC associations and 27 park special types including 80 forest and woodland, 7 shrubland, 12 herbaceous, and 3 sparse vegetation types. The final BITH map consists of 51 map classes. Land cover classes include five types: pasture / hay ground agricultural vegetation; non ? vegetated / barren land, borrow pit, cut bank; developed, open space; developed, low ? high intensity; and water. The 46 vegetation classes represent 102 associations or park specials. Of these, 75 represent natural vegetation associations within the USNVC, and 27 types represent unpublished park specials. Of the 46 vegetation map classes, 26 represent a single USNVC association/park special, 7 map classes contain two USNVC associations/park specials, 4 map classes contain three USNVC associations/park specials, and 9 map classes contain four or more USNVC associations/park specials. Forest and woodland types had an abundance of Pinus taeda, Liquidambar styraciflua, Ilex opaca, Ilex vomitoria, Quercus nigra, and Vitis rotundifolia. Shrubland types were dominated by Pinus taeda, Ilex vomitoria, Triadica sebifera, Liquidambar styraciflua, and/or Callicarpa americana. Herbaceous types had an abundance of Zizaniopsis miliacea, Juncus effusus, Panicum virgatum, and/or Saccharum giganteum. The final BITH vegetation map consists of 7,271 polygons totaling 45,771.8 ha (113,104.6 ac). Mean polygon size is 6.3 ha (15.6 ac). Of the total area, 43,314.4 ha (107,032.2 ac) or 94.6% represent natural or ruderal vegetation. Developed areas such as roads, parking lots, and campgrounds comprise 421.9 ha (1,042.5 ac) or 0.9% of the total. Open water accounts for approximately 2,034.9 ha (5,028.3 ac) or 4.4% of the total mapped area. Within the natural or ruderal vegetation types, forest and woodland types were the most extensive at 43,022.19 ha (106,310.1 ac) or 94.0%, followed by herbaceous vegetation types at 129.7 ha (320.5 ac) or 0.3%, sparse vegetation types at 119.2 ha (294.5 ac) or 0.3%, and shrubland types at 43.4 ha (107.2 ac) or 0.1%. A total of 784 AA samples were collected to evaluate the map?s thematic accuracy. When each AA sample was evaluated for a variety of potential errors, a number of the disagreements were overturned. It was determined that 182 plot records disagreed due to either an erroneous field call or a change in the vegetation since the imagery date, and 79 disagreed due to a true map classification error. Those records identified as incorrect due to an erroneous field call or changes in vegetation were considered correct for the purpose of the AA. As a simple plot count proportion, the reconciled overall accuracy was 89.9% (705/784). The spatially-weighted overall accuracy was 92.1% with a Kappa statistic of 89.6%. This method provides more weight to larger map classes in the park. Five map classes had accuracies below 80%. After discussing preliminary results with the parl, we retained those map classes because the community was rare, the map classes provided desired detail for management or the accuracy was reasonably close to the 80% target. When the 90% AA confidence intervals were included, an additional eight classes had thematic accruacies that extend below 80%. In addition to the vegetation polygon database and map, several products to support park resource management include the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and PLOTS database sampling data. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 15 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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