WSIS Forum 2026
Rapport généré par l'IA

Dialing up Mobile Phone Data for Official Statistics

5 intervenants
Résumé

Résumé

La séance a porté sur la manière d'utiliser les données mobiles (MPD) pour les statistiques officielles grâce à la collaboration, à des cadres de gouvernance, à des outils pratiques et à des cas d’usage nationaux . Esperanza Magpantay a expliqué que les travaux soutenus par l’ONU sur les MPD sont en cours depuis 2014 par l’intermédiaire d’une équipe spéciale élaborant des guides méthodologiques dans six domaines statistiques, et qu’un projet conjoint UIT-Banque mondiale vise désormais à aider au moins 30 pays à utiliser durablement ces données d’ici 2030 . Paul Blanchard a soutenu que la discussion devrait aller au-delà de l’expérimentation technique pour s’orienter vers la mise en place de systèmes durables de MPD dotés d’une gouvernance solide . Il a expliqué que les données de téléphonie mobile dont des enregistrements générés lorsque des appareils interagissent avec des réseaux mobiles et a souligné leur valeur pour les statistiques des TIC, de mobilité et de migration, ainsi que pour l’action publique au sens large . Il a présenté un cadre de gouvernance structuré autour de piliers stratégiques, juridiques et techniques afin de rendre les systèmes durables, conformes au droit et efficaces sur le plan opérationnel . Il a ajouté que ce cadre est conçu comme une boîte à outils pratique, qu'il comprend un rapport, des modèles de protocoles d'ententes et des listes de contrôle opérationnelles couvrant la méthodologie, l’infrastructure, la confidentialité et les responsabilités en matière de sécurité . Daniel Power a illustré la valeur des MDP pour les politiques publiques à travers un travail de long terme en République démocratique du Congo, à Haïti et au Ghana . En RDC, Flowminder a utilisé des filières existantes et un partenariat avec Vodacom Congo pour analyser la mobilité liée à Ebola, produisant des résultats qui prédisaient fortement la propagation ultérieure de la maladie . Il a également décrit un modèle préservant la vie privée dans lequel des enregistrements pseudonymisés sont traités sur des serveurs sécurisés dans les locaux de l’opérateur et seuls des résultats agrégés sont partagés, réduisant ainsi les risques pour les gouvernements et les abonnés . Au Ghana, il a indiqué que le partenariat avait mûri au point que le service statistique pouvait accéder à des données agrégées via API pour les statistiques officielles, les discussions s’étendant aussi à une modélisation par IA des déplacements liés aux inondations . Fredrik Eriksson a ensuite présenté le flux de travail pratique de l’UIT pour produire des indicateurs TIC à partir de MPD, en insistant sur les contrôles de qualité des données et la détection du domicile comme étape cruciale reliant des données d’événements anonymes aux statistiques géographiques . Il a noté que les notebooks peuvent générer des indicateurs d’usage d’internet par géographie, technologie et autres ventilations, tandis que les recherches en cours portent sur des biais tels que la possession de plusieurs cartes SIM, la couverture des opérateurs et la représentation incomplète de la population . Il a souligné que les outils, les données synthétiques, les méthodologies, les exemples et le code source sont librement accessibles via le portail de l’UIT et GitHub, parallèlement à des formations destinées à aider les pays à apprendre en pratiquant . Dans la discussion, les intervenants ont indiqué qu’une mise en œuvre durable dépend de la compréhension des cadres réglementaires locaux, de l’alignement des parties prenantes et de la formalisation des règles au moyen d’instruments tels que des protocoles d'entente intégrant la protection de la vie privée dès la conception . Daniel Power a ajouté que Flowminder applique le RGPD comme norme minimale parallèlement au droit local, tandis que la confiance avec les opérateurs mobiles et un échange de valeur clair sont essentiels pour accéder à des données de haute qualité . En conclusion, Esperanza Magpantay a souligné que les données de téléphonie mobile ne sont pas destinées à remplacer les enquêtes traditionnelles, mais à les compléter ou les renforcer, notamment en améliorant l’actualité et la précision géographique et en comblant les lacunes là où les enquêtes sont trop coûteuses ou absentes .

Points clés

- L’objectif principal de la session était de faire progresser l’utilisation des données de téléphonie mobile (MPD) pour les statistiques officielles en passant d’expériences isolées à des systèmes nationaux durables. Esperanza Magpantay a expliqué que ce travail est mandaté par la Commission de statistique des Nations Unies depuis 2014, couvre six domaines statistiques et est mené avec la Banque mondiale dans 25 pays, avec un objectif de 30 d’ici 2030. - Paul Blanchard a présenté un cadre de gouvernance des MDP centré sur la mise en place de systèmes MPD durables, juridiquement conformes et opérationnels, plutôt que sur la discussion d’algorithmes techniques. Il a défini les systèmes MPD comme des processus transformant des enregistrements bruts détenus par les opérateurs en statistiques anonymisées destinées à l’élaboration des politiques publiques, et a indiqué que le cadre s’organise autour de trois piliers : stratégique, juridique et technique. - Un point majeur de la discussion a porté sur la manière d’opérationnaliser la gouvernance dans la pratique. Paul Blanchard a indiqué que le cadre n’est pas seulement conceptuel, mais comprend aussi des outils pratiques tels que des rapports de cadrage, des modèles de protocoles d'ententes et des listes de contrôle opérationnelles couvrant la méthodologie, le codage, l’infrastructure, le transfert de données et les garanties de confidentialité.

Lors des questions-réponses, il a ajouté que l’engagement des parties prenantes doit clarifier le contexte réglementaire, la volonté de partager et d’utiliser les données, et formaliser les arrangements par des protocoles d'ententes intégrant la protection de la vie privée dès la conception. - Daniel Power a illustré des cas d’usage concrets à fort impact en RDC, à Haïti et au Ghana, montrant comment des partenariats de long terme avec les opérateurs peuvent soutenir la réponse humanitaire et les statistiques officielles. En RDC, les données de téléphonie mobile ont aidé à prévoir la propagation d’Ebola et ont appuyé le plaidoyer en faveur de l’allocation de vaccins ; à Haïti, les données ont été utilisées pour les ouragans, les tremblements de terre, le choléra et les déplacements liés aux groupes armés ; et au Ghana, un partenariat avancé a permis au service statistique d’accéder à des données agrégées via API et d’explorer des modèles d’IA pour la mobilité liée aux inondations. - La confidentialité, la conformité juridique et la relation entre les MPD et les statistiques traditionnelles ont été des thèmes récurrents. Daniel Power a soutenu qu’un travail à fort impact peut être réalisé tout en conservant les données individuelles dans les locaux des opérateurs de réseaux mobiles, réduisant ainsi les risques pour la vie privée, et a insisté sur le respect à la fois du RGPD et du droit local.

Fredrik Eriksson a mis en avant les questions techniques liées à la production d’indicateurs TIC à partir de données anonymes, en particulier les contrôles de qualité des données, la détection du domicile et la correction des biais tels que la possession de plusieurs cartes SIM et la couverture inégale des opérateurs. Esperanza Magpantay a conclu que les MPD devraient compléter plutôt que remplacer les enquêtes traditionnelles, tout en offrant une meilleure actualité et une précision géographique accrue, en particulier là où les enquêtes sont trop coûteuses ou absentes. L’objectif global de la discussion était de montrer comment les données de téléphonie mobile peuvent être utilisées de manière responsable et durable pour les statistiques officielles et les politiques publiques, en combinant cadres de gouvernance, outils pratiques de mise en œuvre et cas d’usage nationaux afin d’encourager une adoption plus large par les gouvernements et les systèmes statistiques.

Les intervenants ont adopté un ton professionnel, collaboratif et orienté vers les solutions tout au long de la discussion. Celle-ci a commencé comme une vue d’ensemble informative et stratégique, puis des exemples concrets et des éléments techniques ont été présentés. Ensuite, les intervenants ont répondu avec prudence aux questions du public sur le consentement, la confidentialité, les réticences institutionnelles et l’accès aux données. La discussion s’est achevée sur une note constructive et encourageante : la complémentarité des MPD avec les sources de données traditionnelles a été mise en avant et les intervenants ont été invités à poursuivre l’engagement.

Intervenants

- Esperanza Magpantay - Modératrice de la session ; impliquée dans les travaux de l’UIT sur les données de téléphonie mobile pour les statistiques officielles ; a introduit la session et le projet conjoint avec la Banque mondiale. - Paul Blanchard - Économiste du développement à la Banque mondiale ; a présenté la gouvernance des données de téléphonie mobile et les systèmes durables de données de téléphonie mobile. - Daniel Power - Directeur général de Flowminder Foundation ; travaille à aider les opérateurs mobiles à ouvrir des données à des fins humanitaires, de développement, gouvernementales et statistiques. - Fredrik Eriksson - Data scientist à l’UIT ; a présenté des outils pratiques et des notebooks Jupyter pour produire des indicateurs TIC à partir de données de téléphonie mobile. - Public - Participants posant des questions, notamment : - Francesca Chacano - Avocate spécialisée en politique numérique ; déléguée de l’Internet Society ; du Pérou. - Boris Engelson - Journaliste freelance local. Intervenants supplémentaires : - Aucun.

Intervenants
EM
Esperanza Magpantay
126 wpm · 10 min
PB
Paul Blanchard
161 wpm · 12 min
DP
Daniel Power
162 wpm · 12 min
FE
Fredrik Eriksson
163 wpm · 5 min
A
Audience
139 wpm · 4 min

La session s’est concentrée sur la manière dont les données mobiles (MPD) peuvent être utilisées de façon plus systématique pour les statistiques officielles et les politiques publiques. La discussion a permis de mettre en avant l'importance de passer de projets test à des mise en œuvre concrètes et durables . Esperanza Magpantay a ouvert la séance et a indiqué que ce travail est en cours depuis 2014 dans le cadre d’un mandat de la Commission de statistique des Nations Unies visant à explorer de nouvelles sources de données pouvant compléter les statistiques officielles, et que les données mobiles constituent l’une des principales sources développées dans ce contexte . Elle a déclaré que l’équipe chargée de cette question a déjà produit des orientations méthodologiques dans six domaines, notamment la migration, les contextes de catastrophe, les transports dans les trajets entre le domicile et le lieu de travail, la société de l’information et la dynamique démographique . Elle a également noté que la séance était organisée avec la Banque mondiale dans le cadre d’un projet conjoint de l'UIT et de la Banque mondiale, qui travaillent avec 25 pays, et qui ont pour objectif de permettre à au moins 30 pays d’utiliser durablement les données mobiles dans les statistiques officielles d’ici 2030 . Elle a ensuite donné la parole à Paul Blanchard concernant la gouvernance, puis à Daniel Power concernant des exemples d'utilisation à échelle nationale. Enfin, elle a donné la parole à Fredrik Eriksson pour qu'il s'exprime sur les outils et les méthodes statistiques .

Paul Blanchard a déclaré qu’il se concentrerait non pas sur les algorithmes mais sur la gouvernance, et il a avancé que le principal défi est de savoir comment construire des systèmes MPD durables plutôt que de réaliser des analyses ponctuelles . Il a expliqué que les MPD sont des enregistrements créés chaque fois qu’un appareil mobile interagit avec un réseau, y compris les relevés détaillés d’appels, les enregistrements de données et les données de signalisation, que les opérateurs de réseaux mobiles collectent généralement à des fins opérationnelles mais qui peuvent aussi être utilisées pour les statistiques officielles et les politiques publiques . Il a précisé que ces enregistrements contiennent habituellement des identifiants, des horodatages, des identifiants de cellule et des emplacements d’antennes, qui permettent d’analyser des mouvements de populations utiles pour des domaines tels que les TIC, la migration et les statistiques de mobilité . Un système MPD est un processus qui transforme des données brutes détenues par les opérateurs en produits statistiques anonymisés à destination des décideurs . Il a souligné que, dans le cadre du programme DDF MPD, l’objectif est d’aller au-delà des expérimentations ad hoc, des projets de recherche et des accords ponctuels d’accès aux données, pour construire à la place des systèmes pouvant fonctionner durablement et à grande échelle .

Paul Blanchard a proposé un cadre de gouvernance des MPD destiné à soutenir la transition . Il a indiqué que les pays cherchant à développer à grande échelle des systèmes MPD ont besoin d’une manière plus structurée de penser la mise en œuvre tout en conservant une marge d’adaptation aux différents contextes nationaux . Le cadre repose sur un pilier stratégique, un pilier juridique et un pilier technique . Le pilier stratégique couvre les règles de haut niveau et les dispositifs de gouvernance relatifs à l’orientation, à la supervision, aux relations internes et externes, ainsi qu’à l’approbation de nouveaux produits ou cas d’usage . Le pilier juridique porte sur la conformité avec les cadres de protection des données et de régulation, y compris la classification des données traitées, l’identification de la base légale, l’application de principes tels que la minimisation des données, la limitation de la conservation et la limitation de la finalité, ainsi que la clarification des rôles tels que responsable du traitement et sous-traitant . Le pilier technique concerne les dispositifs opérationnels nécessaires pour mettre en œuvre le système dans les domaines de la méthodologie, des logiciels, de l’infrastructure, de la vie privée et de la sécurité .

Il a précisé que le cadre était conçu comme un ensemble d’outils d’appui pratique, et non comme un simple modèle conceptuel . Le projet produira un rapport détaillé sur le cadre, des modèles de protocoles d'ententes et des listes de contrôle opérationnelles pour aider les pays à organiser la mise en œuvre . Il a illustré ces listes de contrôle par des exemples indiquant qui est responsable de la conception méthodologique, de l’assurance qualité, des exigences en matière de données, du codage, de l’exécution du code, de la documentation et du contrôle de version, ainsi que des tâches d’infrastructure telles que l’ingestion des données, l’extraction, le transfert sécurisé, la structuration des données et la gestion de l’infrastructure . Dans l’ensemble, il a présenté la gouvernance comme la combinaison de mesures organisationnelles et techniques nécessaires pour rendre les systèmes MPD durables, conformes au droit et opérationnels dans la pratique .

Daniel Power a ensuite présenté des exemples nationaux de Flowminder, qu’il a décrit comme une petite organisation à but non lucratif travaillant à aider les opérateurs mobiles à ouvrir leurs données à des usages humanitaires et de développement et à relier plus étroitement ces données à l’usage gouvernemental, et pas seulement aux bureaux de statistique . Il s’est concentré sur la République démocratique du Congo, Haïti et le Ghana, et a décrit de bonnes avancées dans les trois pays . Ses exemples ont montré à la fois la valeur pratique des MPD et l’importance de disposer déjà de partenariats permanents, de pipelines de données sécurisés et de modèles opérationnels préservant la vie privée .

Daniel Power a ensuite abordé le cas de l'épidémie d'Ebola en République démocratique du Congo et a expliqué qu’au début de l’épidémie dans l’est du pays, les informations conventionnelles sur les mouvements de population étaient très limitées, alors même que l’une des questions les plus urgentes était de savoir où la maladie risquait de se propager ensuite . Comme Flowminder disposait déjà de pipelines et d’un partenariat avec Vodacom Congo, l’organisation avait déjà des informations sur la connectivité et les mouvements de population dans l’est de la RDC et a pu procéder immédiatement à des analyses, y compris une analyse de cohorte portant sur des centaines de milliers d’abonnés présents dans la région touchée par l’épidémie . À l’aide d’une métrique appelée intensity, combinant le temps passé et le nombre de personnes de la cohorte visitant d’autres zones de santé, l’équipe a produit une indication classée des liens probables entre la zone touchée et d’autres zones . Une semaine plus tard, dix zones de santé supplémentaires ont signalé des cas d’Ebola, et huit de ces dix zones figuraient parmi les seize premières identifiées dans l’analyse précédente . Daniel Power a également donné un deuxième exemple en RDC concernant la planification vaccinale . Il a indiqué que le dernier recensement datait de 1984 et que les projections démographiques étaient uniformes pour l’ensemble du pays alors que la croissance de la population ne l’était pas . Dans le Haut-Katanga, les estimations démographiques ainsi obtenues ont aidé les médecins à plaider pour davantage de vaccins et de soutien opérationnel, comme du carburant pour les motos utilisées dans la prestation de services . Cela a montré comment des estimations dérivées des MPD peuvent fournir des informations liées à la population lorsque d’anciennes projections de recensement ne sont plus assez efficaces .

Daniel Power a ensuite abordé la situation d'Haïti et a indiqué que c’est là que le travail de Flowminder a commencé et que l’organisation y entretient un partenariat avec Digicel depuis 16 ans . Il a décrit Digicel Haïti comme le premier opérateur à avoir autorisé l’utilisation de ses données dans la réponse humanitaire . Les cas d’usage ont notamment concerné les ouragans, les tremblements de terre, le choléra et des déplacements plus récents liés à la violence des gangs, en particulier en dehors de Port-au-Prince . Il a ajouté qu’avec le soutien de la Banque mondiale, des travaux sont en cours pour établir un protocole d'entente avec l’institut national de statistique afin que ces résultats puissent alimenter plus directement les modèles officiels . Le Ghana a été présenté comme l’exemple institutionnel le plus abouti . Daniel Power a indiqué qu’il existait une forte adhésion des pouvoirs publics, une relation de long terme avec le Ghana Statistical Service et des capacités élevées . Dans ce cas, le service statistique peut accéder aux données agrégées de l’opérateur via une API et les utiliser directement pour les statistiques officielles . Il a déclaré que le partenariat avait mûri au point que les discussions portaient désormais sur une modélisation plus avancée incluant l’IA et l’apprentissage automatique pour prévoir comment les populations pourraient se déplacer en réponse aux inondations .

Daniel Power a également expliqué de quelle manière Flowminder préservait la vie privée des utilisateurs . Il a expliqué que Flowminder installe généralement un serveur sécurisé dans les locaux de l’opérateur . L’opérateur transfère les relevés détaillés d’appels pertinents dans cet environnement sous forme pseudonymisée, avec suppression des numéros de téléphone et remplacement par un hash cohérent afin que les schémas de déplacement puissent encore être analysés sans identifier directement les individus . Les données sont ensuite géoréférencées à l’aide des emplacements des antennes-relais et accessibles uniquement à un petit nombre d’analystes habilités en matière de sécurité dans les locaux de l’opérateur . Seuls des résultats agrégés sont exportés pour effectuer un rapport et pour les cartographier . Daniel Power est ensuite revenu sur ce point et a soutenu que les acteurs publics n’ont pas besoin d’un accès direct aux données individuelles des abonnés pour obtenir des résultats utiles, puisque l’analyse peut être effectuée alors que les enregistrements au niveau des abonnés restent sous le contrôle de l’opérateur .

Daniel Power a également décrit deux outils de mise en œuvre développés avec la Banque mondiale . Le premier était un cadre d’évaluation de la maturité couvrant les dispositifs juridiques, l’alignement des parties prenantes, l’infrastructure, la conception des cas d’usage et la durabilité, destiné à aider les pays à identifier les points faibles et à orienter les ressources de manière plus stratégique . Le second était une théorie du changement visant à aider à cartographier la manière de passer d’une situation en cours à un dispositif institutionnel souhaité et à expliciter les hypothèses . Dans une courte transition vers l’exposé suivant, Esperanza Magpantay est revenue sur l’exemple de la RDC et a indiqué que les données de téléphonie mobile avaient pu être utilisées pendant l'épidémie d'Ebola parce que les discussions nécessaires avaient déjà eu lieu avant l’épidémie, et que les outils de gouvernance sont utiles précisément dans ces échanges préalables entre parties prenantes .

Fredrik Eriksson a ensuite pris la paroles pour aborder le flux de travail statistique que l’UIT développe pour les indicateurs TIC à partir des MPD . Il a commencé par les contrôles de qualité des données, notant que des schémas inhabituels peuvent révéler des problèmes de traitement, d’horodatage ou de données manquantes . Il a donné l’exemple selon lequel, si un graphique ne présente pas la forme attendue, cela peut indiquer un problème de traitement ou de données . Il a ensuite déclaré qu’une fois la qualité des données suffisamment bonne, les événements du réseau peuvent être transformés en indicateurs . Il a décrit la détection du domicile comme l’étape méthodologique clé, car elle permet de relier des séquences d’événements anonymes à des zones géographiques significatives . Comme les données sont anonymes, la localisation du domicile doit être déduite du comportement plutôt que lue directement . Il a indiqué qu’il existe de nombreux algorithmes de détection du domicile et que les notebooks de l’UIT accordent la priorité à l’activité en semaine, surtout la nuit, en soirée et tôt le matin, en partant de l’hypothèse que ce sont les moments où les personnes ont le plus de chances d’être chez elles . Une fois la détection du domicile achevée, le flux de travail peut produire des indicateurs d’usage d’internet par géographie, technologie, groupe d’âge et autres ventilations en fonction des données disponibles .

Fredrik Eriksson a également insisté sur les limites et le programme de recherche autour de ces méthodes . Il a indiqué que les MPD comportent des biais, notamment la possession de plusieurs cartes SIM, l’usage de plusieurs appareils, les différences de part de marché des opérateurs et de couverture réseau, ainsi que le fait que certaines personnes ne possèdent pas du tout de téléphone mobile . Pour répondre à ces problèmes, l’UIT mène des recherches sur la déduplication des abonnés, l’évaluation de similarité, l’appariement de trajectoires, les méthodes de post-ajustement ou de repondération . Il a déclaré que ces méthodes seraient ajoutées aux notebooks une fois validées avec les partenaires et testées dans les pays . Il a conclu en soulignant que les outils sont ouverts et conçus pour renforcer les capacités des pays . Le portail MPD de l’UIT comprend des notebooks, des données synthétiques, des méthodologies, des exemples nationaux et un dépôt GitHub contenant le code source, qu’il a indiqué être à nouveau mis à jour dans les deux prochaines semaines . Il a également précisé que l’UIT organise des formations pratiques en petits groupes, généralement pour un maximum de 20 à 30 participants, à l’aide de Project Jupyter .

La séance de questions-réponses a commencé par une question de Francesca Chacano sur le consentement et les différences entre cadres juridiques à mesure que les pays passent de l’expérimentation à une utilisation plus durable des MPD, en particulier là où les régimes de protection des données sont faibles . Paul Blanchard a d’abord demandé si elle parlait du consentement des individus . Il a ensuite répondu en expliquant que l’engagement avec les pays commence par la compréhension du cadre réglementaire local et de la volonté de chaque acteur de l’écosystème de partager, traiter ou utiliser les données . Il a identifié comme acteurs clés les opérateurs de réseaux mobiles, les régulateurs des TIC, les instituts nationaux de statistique et parfois les ministères sectoriels tels que les ministères de la santé . Il a indiqué que le principal outil formel est le protocole d'entente, au sein duquel sont intégrés des règles, des protocoles et des mesures de protection de la vie privée . Sa réponse n’a pas résolu directement la question du consentement en tant que doctrine juridique, mais s’est plutôt concentrée sur la cartographie réglementaire, les rôles des parties prenantes et les accords formels .

Daniel Power a ajouté que Flowminder, en tant qu’organisation basée en Europe, considère le Règlement général sur la protection des données (RGPD) comme une norme minimale et n’abaisse pas cette norme simplement parce que les cadres locaux peuvent être plus faibles . Il a indiqué que l’organisation doit se conformer à la fois au RGPD et au droit local, et qu’elle doit également nouer des relations avec les acteurs publics lui permettant de comprendre les besoins locaux et de plaider pour les bonnes pratiques . Il a aussi souligné que les relations de long terme avec les opérateurs dépendent de la confiance et de la capacité des opérateurs à montrer aux abonnés que le partenariat crée un bénéfice public . Il a ajouté que les méthodes de préservation de la vie privée et les méthodes cryptographiques évoluent rapidement, rendant l’accès et l’utilisation sécurisés de plus en plus réalisables .

Une deuxième intervention du public a élargi la discussion à un problème plus large : celui des administrations assises sur d’importants volumes de données inutilisées en raison de la résistance du public, des préoccupations de sécurité et de la réticence professionnelle à partager l’information . Paul Blanchard a répondu qu’il s’agissait d’une question plus vaste de gouvernance des données, mais il a affirmé que l’objectif dans l’écosystème MPD est de faciliter l’accès grâce à des dispositifs de gouvernance sécurisés conçus autour des besoins des utilisateurs . Daniel Power a relié la question au financement et aux incitations, expliquant qu’après le changement d’administration aux États-Unis, les financements ont été réduits pour de nombreux domaines, y compris les données, et que le gouvernement britannique avait suivi une orientation similaire . Il a soutenu que la poursuite des investissements est importante et que les opérateurs mobiles ont eux aussi besoin d’un véritable échange de valeur s’ils doivent fournir volontairement des données de qualité, en temps utile . Il a cité Vodacom Congo comme exemple d’un opérateur fournissant des données gratuitement tout en obtenant en retour une valeur réputationnelle et en matière de responsabilité sociale d’entreprise . Fredrik Eriksson a répondu que le problème n’est souvent pas l’absence de données, mais l’absence de combinaison et d’analyse des données, et quil est essentiel de démontrer les avantages des cas d’usage .

Un dernier intervenant a demandé si les MPD peuvent remplacer les enquêtes traditionnelles et s’il faudrait les préférer aux enquêtes de terrain pour estimer le nombre d’utilisateurs d’internet . Esperanza Magpantay a répondu clairement que les MPD ne sont pas destinées à remplacer les sources statistiques traditionnelles, mais à les compléter . Elle a indiqué que les MPD offrent de l’actualité et une précision géographique, puisque les résultats peuvent être produits dès lors que l’accès existe et peuvent souvent être ventilés spatialement plus facilement qu'avec les enquêtes de terrain . Elle a ajouté que là où les enquêtes sont absentes ou trop coûteuses, les MPD deviennent particulièrement précieuses et peuvent servir de source principale pour l’UIT dans la production de données sur l’usage d’internet .

En conclusion, Esperanza Magpantay a déclaré que la session avait réuni des approches de gouvernance, des cas d’usage opérationnels et des outils pratiques, et elle a encouragé les participants à poursuivre la discussion au-delà de la salle . Elle les a invités à consulter les documents de la session, à explorer les outils partagés par les intervenants et à rejoindre des forums en cours tels que l’Équipe spéciale sur les données mobile, qui accueille à la fois les pays et les experts souhaitant apprendre ou partager leur expérience . Dans l’ensemble, la discussion a montré un large accord sur le fait que les MPD peuvent être utiles pour les statistiques officielles et les politiques publiques, en particulier pour des résultats plus rapides et géographiquement plus détaillés, mais que les progrès dépendent d’une gouvernance durable, d’une conception de système préservant la vie privée, de partenariats de long terme, de capacités techniques et de cas d’usage publics clairement définis .

Esperanza Magpantay
Thank you. Recording in progress. Good afternoon. Good morning, everyone. Welcome to this session on dialing up mobile phone data for statistics. My name is Esperanza Magpantay. I'll be moderating this session, which is also available remotely. Our plan is to have three presentations from our distinguished intervenants. And then at the end of the session, I will open the floor for questions and discussions, both for our remote participants and in the room. So just to give some quick introduction as to this session. So the whole. mobile phone data communities working together to use mobile phone data for official statistics. This work has been going on for a number of years now, as we were mandated by the UN Statistical Commission, which is the highest policymaking body on statistics, to work on using new data sources to complement or supplement official statistics. And mobile phone data is one of these new data sources. We have been working since 2014 through a task team, which is called Task Team on Mobile Phone Data, where we develop a number of methodological guides covering now six topics of statistics, including migration to reason, disaster context, transport, and commuting, as well as information society, which is our area. and population dynamics. So all these six areas, we are exploring ways how this can be used to – how mobile phone data could be used for these areas of statistics. And today we are organizing this session in collaboration with the World Bank, where we have a joint project on the use of mobile phone data for policy and statistics. And in this project, we are working with 25 countries now, and our objective is to have at least 30 countries by 2030 where they can sustainably use mobile phone data in the production of official statistics. And so this afternoon you will be hearing from three intervenants. First is from Paul Blanchard, who is a development economist from the World Bank. And then second, we will have Daniela. Daniela Power, who is the managing director from Flowminder Foundation, who is also our partner in this work. And then third, our colleague, Frederick Erickson, who is our data scientist here at ITU, where he will be showing you some of the practical tools that we have developed in connection to the indicator that we monitor using mobile phone data. And this tool will be useful for you or your counterparts in your country in using this new data source. So, without further ado, I'd like to pass the floor to Paul so that he can share with us the work that he is leading with regards to the MPD, mobile phone data, we call it MPD, which is on data governance. Paul, over to you.
Paul Blanchard
Thank you. Thank you so much, Fran. This one? There. Okay. There you go. Okay. Yeah, thank you so much. So to get started, we won't talk about the technicals of mobile phone data, of how to transform them and the algorithm, but more on the governance side. That is how to build sustainable mobile phone data systems. So we have developed what we have called the mobile phone data governance framework that provides conceptual basis and practical tools to support the implementation of MPD systems. So a very quick introduction to mobile phone data, assuming that many of you are already familiar with these type of data sources. So what we call mobile phone data are those records that are generated whenever a mobile device interacts with a mobile network. And so that's what we call usually the call data records, the data data records, the signaling data, et cetera. So these data are collected and held by mobile network operators for usually billing purposes. But we have seen many instances and examples. Where these data can be repurposed for national statistics purposes and to support public policy. We have seen examples of ICT statistics, of course, that have been produced with this data, but also mobility statistics, migration statistics, and many more. What you're seeing on the screen is an example of what this data looks like with usually a user identifier, a timestamp, a cell ID, which is the antenna that processed the event, and the longitude and latitude of that antenna. So we understand that this carries information that can be useful for any kind of mobility statistics and others. So now, from a system perspective, systematic perspective, what is a mobile phone data system? What do we mean by that? So essentially, it's a box that allows to transform raw MPD held by mobile network operators into anonymized statistics that represent actionable insights for public policy. So the MPD system is everything that is in between, and that should be viewed as an integrated data production system. So one thing that I would like to highlight here is that we're operating under this context of the DDF MPD program, where we try to develop MPD systems, moving away from ad hoc experiments and research projects, this one -off data access that allows us to demonstrate the usefulness of this data. We're trying to go beyond this and build actually sustainable systems. So in this context, what is a mobile phone data system governance, and what do we mean by that? So it's essentially everything that is in this black box. So we can think of the organizational structure that we need, the coordination among a complex ecosystem of stakeholders, the rules, the protocols, the processes for many different things that need to be in place for this system to work. So in a nutshell, that's the set of organizational and technical measures that create a sustainable, legally -controlled, compliant, and operational data production system. So now that I've highlighted what... what is mobile phone data, what is a mobile phone data system, what we mean by building a governance framework, we have developed this NPD governance framework. So essentially, NPD systems are now expanding and developing and producing these systems at scale, requires some level of standardization. And the need for building a framework like this emerged because we need to have a unified framework for thinking about this system to help country teams effectively think about this problem and approach the construction of an NPD system in a structured manner. So this is what the NPD governance framework provides, and it's a comprehensive organizational blueprint for NPD systems. It is structured across three different pillars, the strategic pillar, the legal pillar, and the technical pillar. So now let me give you just a few more details about what these systems are. What these pillars are. so when we think you know building this system from the ground up what do we need for this system to work and again we want a system that is sustainable that is legally compliant and that is operationally efficient and that it delivers what we want it to deliver so in the strategic pillar it is all the set of rules and the government vehicles and that allow to govern the system the high level oversight the high level decision making so it provides one strategic direction taking the decisions to decide what the system must output managing external relations would it be with the with the public citizens but also internally within governments with data protection authorities etc and finally it handles executive gatekeeping and authorizations but the high level ones so think about you know approving a new use case allowing the system to produce a new set of statistics for a particular topic the second pillar is the legal pillar and these has to do with not what makes the system efficient, but what the system must comply with to be aligned with the regulatory framework that is in place in a given context. And a few elements that I've listed here are taken from what is typically inside the Data Protection Acts that have been enacted, especially across Africa, and that are inspired largely from the GDPR. So think about characterizing legally what the data are. There are personal data. There are sensitive personal data. There are sometimes very specific definitions that we need to identify and understand what's the data that we're processing with respect to these definitions. Second is the lawfulness of processing. We need a legal basis that is usually included in this law and that enables the processing of this data. Then we have the compliance with core principles, and you know probably a few of them. There's the data minimization. There's the data minimization. the storage limitation, the purpose limitation, and all of these principles that we have to abide with. And finally, legal roles and responsibilities, data processors, data controllers, et cetera, and privacy and security obligations. And these are not very specific usually, right? It's usually about implementing sufficient technical measures, period. So that's why we have also the technical pillar. And this one is really the operational level listing of all of the roles and responsibilities and tasks that we need to organize, assign clear responsibilities, and stipulate the means with which these responsibilities are going to be fulfilled. So there's three big domains, methodology and software, data and infrastructure, privacy and security safeguards. So this is the overview of the governance framework. Now, it's not just a conceptual framework, but we wanted to make this a practical engagement tool as well so that it can effectively support country teams in implementing MPD systems. So this project is going to deliver one framework report that details all of the elements that I've talked about, but also MOU templates that have been very useful so far in engaging with country teams to provide like a benchmark on how to formalize these agreements. And finally, operational checklists. And on that last element, I just wanted to provide an example of what this checklist could look like. So a technical checklist basically lists out all of the key operational responsibilities that we need to think about and that we need to organize to make this system effectively efficient and operational. So I will just list them out and we can talk about these more. It's just to illustrate the tool. So, for instance, in the first domain methodology and software, we need to take care of methodologies. So we need to take care of methodological design, quality assurance framework, who's going to do the data requirement specification, which can be fairly technical, who's going to do the coding, which is completely central in this system, who's going to execute the code, which can be a different entity than the entity that develops the code. Who's going to take care of the documentation and version control? And the second domain on the data and infrastructure, who's going to take care of the data ingestion and extraction, data structuring, secure transfer channel, data transfer, infrastructure management, which is also a key pillar in those systems, et cetera. And then same thing about privacy and security safeguards, understanding at the disaggregated levels, what are the aspects that we need to ensure for the system to be operational. So in a nutshell, so the key takeaways is that what we have developed is a structured, comprehensive, yet flexible conceptual basis to think about and organize complex MPD systems. It's not a one -size -fits -all framework, but it's very much adaptable to country context. It's articulated across these three pillars, strategic, legal, and technical, but it also provides a practical toolkit for effectively using this framework in practical terms and appropriately. And that's what we're going to
Esperanza Magpantay
Thank you so much, Paul, for sharing with us the recent work on MPD data governance and all the tool studies that are produced under the GD app work. And now we would like to hear from Danielle, who will be sharing with us one of the use cases related to mobile phone data implementation. Danielle, over to you.
Daniel Power
Hi, everyone. I might need some help on getting my screen to share. Let's try that again. Maybe I'll just start talking whilst I figure this out. Hi, everyone. I had a false sense of confidence. I thought I'd figured it out beforehand. I'll start introducing myself, maybe, if you wouldn't mind. Thanks very much. So my name's Daniel Power. It's great to be here. I'm the managing director of an organization called Flowminder. We're a small, not -for -profit, and we specialize in working with mobile operators and helping them open up their data for humanitarian development applications. And we're particularly interested in helping that data feed into government use, not just statistics offices. And in this very short overview, I kind of want to motivate the work we're all interested in. Maybe skip on to the next slide, please. Okay, thanks so much. Great. Thank you. Great. Yeah, I'm going to motivate the work a little bit. I'm going to talk. A little bit. Paul set me up really well for a question I want to pose to the room. and then give you some resources that you might find useful with some QR codes, which I'm probably going to have about 10 seconds to show them, so have your cameras ready if you want them. You can also drop me a line. You'll find our email addresses on our website. So, yeah, we work in 20 countries over the years, and I'm going to focus specifically on three countries where we've got long -term operational engagements, the Congo, Haiti, and Ghana. And this is very recent work that I find exciting and I want to share with the room. You'll no doubt be aware of the Ebola outbreak, which started in May, in the eastern DRC, in an area where there's very limited data on population movement. And, of course, one of the immediate questions is, where will the disease spread to next? And thanks to our partnership with Vodacom Congo, well, first of all, we already had information on COVID, connectivity, and population movement within that area. off the Congo thanks to our pipelines which were already set up. We were also able to start a cohort analysis immediately. We identified hundreds of thousands of subscribers which were in this outbreak region in the east and the map shows a metric we call intensity. It's a product of the amount of time spent and the number of people from that cohort who visited the other 500 health zones in the DRC. And this turned out to be an extremely strong predictor of disease spread, the movement of the population. A week after this report was released, 10 more health zones were reporting cases of Ebola and 8 of those 10 were in the top 16 of the list that we had produced the week earlier. So a disease which is spread through population movement of course can be highly understood through populated data which is strong on population mobility. Good. Very briefly on how we work, our typical way of working, certainly in the three countries that I'm talking about, we set up a secure server on a mobile network operator's premises. So they move call to detail records as the data of interest. It's pseudonymized, which means that the telephone number is removed and hashed in a consistent manner. So all the records relating to one individual are hashed so that we can see a pattern of movement. It's geo -referenced with cell tower locations and aggregated by a limited number of security cleared analysts on this server on the mobile network operator's premises before it is aggregated and then exported to produce reports and maps combined with other data and applied to specific use cases. Good. So extremely light touch now. I'm going to... Thanks. Talk about the three countries that I want to highlight. So in the Congo, we've worked with Vodacom since 2018. we've done a wide range of applications there we work closely with the regulator ARPTSA they've given us non -objection for releasing data on a monthly basis and maybe just to pull out another success story I think the last census in DRC is 1984, I hope I've got that right and population density in the DRC is broadly understood with a projection on that census data which is uniform for the country and of course population growth is not uniform particularly in the south -east where there's been huge growth due to mining which is visible on this map in the bottom middle and vaccines are provided to doctors in proportion to these projected population numbers and there's not sufficient in an area where there's been large population growth so doctors in Hokotango were able to advocate on the basis of our data the data we produce with Vodacom for sufficient vaccines we were there in February and doctors were telling us that they were finally able to get the vaccine finally able to vaccinate more children as a result of the resources that they've been able to advocate for Interesting, it wasn't just vaccines. It was also things like petrol for motorbikes that they needed in order to deliver the services they were offering. In Haiti, this is where it started. Digicel in Haiti, we've been partnering with them for 16 years now. Incredible. And they were the first operator to enable their data to be used for humanitarian response work. Our work there is funded through the government's FAES. That's the Economic and Social Assistance Fund. And thanks to a partnership with the World Bank, we're now looking to get a MOU in place with the Statistics Office so that these data, which have been produced for a long time now, can feed into their models. And, yeah, in terms of use cases, hurricanes, earthquakes, cholera spread, and now gang violence, the ongoing gang violence we're seeing now, which is causing displacement, particularly outside of Port -au -Prince. Thank you. there's a kind of maturity improvement story in the slides i'm showing ghana is where there's the strongest governmental buy -in and there's a long -term relationship with ghana statistical service and they've got high capacity they can access the data through an api aggregated data through an api and they're able to use mobile operator to date operator data to inform official statistics in ghana and in fact the conditions the the partnership is sufficiently mature now that we're not just looking to get these you know partners on board we're looking at the next level and we're in conversations with ghana so it's gss and nadmo the disaster management agency on how to corporate incorporate artificial intelligence techniques particularly machine learning to build models of how population will move when shocked by floods for example uh so excited to see what we can do there And then just to return to this diagram, the point I wanted to make in relation to data governance is that all these decades' worth of work, very many different use cases, high -impact work, is achievable with a very similar solution in each country, whereby the individual level data of the subscribers is held at the mobile network operator's premises. And the government actors don't need to access it. They don't need to take on the risks associated with getting access to individual level data from their citizens or from the subscribers of the mobile network operator. And the case I would make is that given that it is possible to achieve high -impact work and protect subscriber privacy, why do it any other way? Of course, I advocate this as a technical partner which can facilitate this when a mobile network operator doesn't necessarily have the skills. Good. I promised two resources very quickly. In partnership with the World Bank as well, we developed a maturity assessment framework. Very similar to the model that Paul showed, this maps out all the different dimensions which are necessary for a mobile operator partnership with government to work. And there's a lot there. So you've got legal stakeholder engagement alignment, particularly finding good value propositions that work for all of the stakeholders that are involved, the politics there, data infrastructure, the use case and sustainability. So there's a lot of detail behind this that you can use to ensure that you allocate the scarce resources that you will have to develop a program appropriately and not just invest them all where people are shouting the loudest. Make sure you address the weak links in that chain as well. And very similarly, a complementary tool is a theory of change, which maps out a model of how to get from where you may be to where you want to be and the assumptions that come within that. Great. Thanks very much. There's my details. And, of course, you can get hold of
Esperanza Magpantay
Thank you, Daniel. Very interesting use cases. You saw how mobile phone data can be used in all these applications. And I think what I'd like to emphasize there is that, for example, for the case of DRC, they were able to use mobile phone data because there has been some discussions that went on before the issue of Ebola came. So that long discussion has to take place, and that is where the MPD data governance and all the tools that Daniel shared will come handy when you talk to the stakeholders. And now moving on to the practical use case or the practical tool that can be used specifically for information society, I'd like to ask my colleague, Frederick Erickson, to share with us the work that we have been focusing on in the ITU. Thank you. Over to you, Frederick.
Fredrik Eriksson
chart doesn't necessarily look like an elephant or the elephant shape, then there might be some issues relating to either processing the data or maybe different timestamps or other sort of missing data there is. So some simple checks here. And it's only really once we have the good quality data that we can actually move on to the next stage and start calculating the different indicators. Now, once the data has passed this data quality assurance stage, we can begin transforming these network events into the ICT indicators. And this is, in a way, probably the most important step in the entire workflow. The home detection is really the bridge between the mobile phone data and the statistics. Before home detection, mobile phone data are really sequences of events in time and space. And this is space for different subscribers. But once we can determine the home location, then subscribers can be mapped to different areas and we can allow us to combine the data with different population and other reference data. Now, the challenge, of course, is that the data is anonymous. So the anonymous mobile phone data do not explicitly tell us where people live. So the home location has to infer this from the user behavior. Many home detection algorithms exist. They're all making different assumptions about human behavior. The notebooks that we use use an algorithm that gives greater importance to the weekday activities and also specifically events during the night and the evening and the early morning. Because those are the times when you're typically at home and where we can more define where a person might live. And from there, we can start generating indicators of internet use here. For example, looking at. Geographic areas we can look at by technologies. We can look at age groups and other different breakdowns depending on the data that we have. And all of these visualizations are also included, including this one shown on the technology composition on Internet use is included in the notebooks. But calculating indicators is not necessarily the end of the story. Like with every type of data source, we do have some biases. There might be some people that have multiple SIM cards in their phone or you have multiple devices. Or there can be different coverages or different market shares between the operators. And then naturally, there's also people that actually don't have a mobile phone in some countries. So how do we deal with that? And these are some of the research areas that we're looking at. For example, trying to develop methods on subscriber deduplication, looking at some privacy announcing technologies, and also looking at subscriber similarity scores and trajectory matching, trying to see if we can identify. Some of the different subscribers that might have two SIM cards, but it's actually the same subscriber, for example. We're also looking specifically for ICT indicators. We're looking at developing post -adjustments and re -rating measures to better try to infer the results towards the whole population. And then also, once these methods are validated with partners and also countries tested, then we will introduce them more into the notebooks themselves. The last slide, sorry, is that everything I've shown here is open. It's openly available, the MPD portal at the ITU. There's a link down there below. It includes a lot more information on both access to the notebooks, also looking at you can download some fake synthetic data, developing methodologies, and a lot of country examples as well. The GitHub repository provides the entire source code as well. It will be updated also again in the next couple of weeks. We also organize some trainings for smaller groups of maximum 20, 30 in terms of... ...hands -on exercises on the Jupyter notebooks themselves. because the goal in general is, of course, to help countries start using mobile phone data for official statistics, and one of the best ways to learn is just learning by doing. Thank you.
Esperanza Magpantay
Thank you so much, Frederic. This is indeed one of the key outputs that we produce in the ITU that is specifically relevant for this audience where we are looking at Internet use, so percentage of the population using the Internet, which is one of the indicators of the WSIS community. So with that, I'd like to thank our three intervenants and would like to open now the floor for questions. The remote participants, if you can raise your hand and I'll call you, and the room as well. So over to you. And maybe while we are waiting for the questions to come in. Yes, we have one there and then one here. I'll start with her and then I'll come to you. Yes, please.
Audience
Thank you so much. I am Francesca Chacano, digital policy lawyer and Internet Society delegate. I am from Peru. And my question was related to as countries move between this experimentation to sustainable data gathering for these purposes, like how do you handle like the consent and the different frameworks? Like what have been your experience doing this in maybe especially thinking about other countries that might not have that much strong like data protection frameworks? Thank you.
Esperanza Magpantay
Thank you so much. And then I'll ask our colleague to also ask the question and then I'll give it to our intervenants.
Audience
Yes, Boris Engelson. I am just a local journalist freelance. Even. Before the advent of new technologies. Even at the time of all technologies or even at the time of no technology at all there were huge reservoir of data dormant in administrations so there is a census and last time there was one in Switzerland, people were so outraged how dare the government even consider taking our data that it could not be fought through but for years I thought that WHO had as main activity to cross the data from all the hospitals and ministries to do epidemiology, then after 30 years I was told no no no they never do that, they never will for security whenever you try to use security data it's undemocratic after all the thieves need to survive etc. what about this reluctance? The question is why have we used so little for one or two hundred years, huge pools of data which were there? And while you were talking, my answer is not very reassuring. Learned jobs, learned professions are always afraid of sharing data and being bypassed by even rough data. So if this is true, then it will be very difficult to implement whatever progress in society, especially if learned people are in command or at the podium.
Esperanza Magpantay
Thank you so much for those questions. Maybe Paul, I'll start with you if you would like to address the two questions.
Paul Blanchard
So maybe on the first question, did you say the word consent or I couldn't hear well. Did you talk about the consent of individuals? And then on the way that we engage in these operations. So Daniel has shown a couple of engagement tools that are already available, and that allows to see people around the table, understand what are the willingness of the different stakeholders. So in this ecosystem, we have different key actors, right? We have the mobile network operators, which are private actors. Usually the ICT regulator, the National Statistical Office, and sometimes some line ministries that could be interested in specific applications. Think about the Ministry of Health, for instance. So the primary engagement events that we organized are about understanding what is the regulatory framework, what is the willingness of each actor to share data, to process data, or to use those, and then organize the formalization. The formalization of whatever agreement we may come to. So the primary formalization tool, as I mentioned, is the MOU. So within these framework agreements, we agree on a set of rules and protocols that organize the system and within which the privacy by design is embedded. So that kind of relates to the second question about thinking about the security of this data. So, I mean, there's two different points in your question, I guess. The first one about we have had a lot of different data sources that we haven't used anyway. So why use a new one that is, you know, even more expensive?
Audience
I didn't say why use it. I just said that if they were not used so far, it means there might be some factors, some reasons why they were not used, which might survive. But I will be very happy. And by the way, I tried to get data from the World Bank, which is one of the best international organizations. However, to compare the cost of bidding. tram lines because here in my country I feel we spend too much but I could never even have collect simple data on the comparative cost of one kilometer of tram lines throughout the world, index on the wages, etc. So these are very simple examples which are absolutely going to the dead end whatever we do and whichever technology we use.
Paul Blanchard
That's a broader data governance concern then. But in the ecosystem of more data systems that we try to implement, we try to facilitate this data access and making sure that this data can effectively be used because they are designed by the final users which are the decision makers, the line ministries of application. Also one thing that I wanted to highlight in terms of data security and privacy is that as the data is expanding, so are the techniques for privacy preservation, the privacy enhancing technologies, the cryptography methods that we can apply to protect the data. So these things are also evolving at a pace that is quite significant. So I'm hopeful that, again, as these data become more available, our ability to actually protect this data and to put in place the governance systems that allow certain data access in a very secure way is going to evolve as well.
Esperanza Magpantay
Thank you so much, Paul. Conscious of the time, I'll give the floor to Daniel and then immediately to Frederick to answer those two questions.
Daniel Power
Thanks. I'll try and be brief. On the first point, in talking as a kind of smaller organization that works in a world of much bigger players, I think it's important that we are governed by the GDPR. It's a European -based organization, and that's the minimum standard. We don't lower that standard because it does not apply in a country that we may be working. We need to be compliant with the GDPR. We need to be compliant with local law. And we need to build relationships with government actors in the countries where we're working and both learn and understand what is necessary to work in that country, but also be advocates for best practice to take data privacy very seriously. And this is an ethical perspective. And for our organization, it's also necessary because our relationship with mobile network operators is built on trust. That's how we've been able to deliver the impact we have for 15 years. We are trusted with their data. They need to be able to reassure concerned subscribers that partnership with our organization is net benefit, is beneficial. I'll leave it there because I think Paul covered the majority of the answer very well. On the broader question, I mean, there's a lot to unpack there as well. And certainly we're answering this question. Eighteen months after the change in the U .S. administration. I think we're going to have to wait and see what happens. I think we're going to have to wait and see what happens. I think we're going to have to wait and see what happens. when funding for everything was cut, funding for data was cut. The British government, I'm sorry to say, followed suit. And I would just make the case that we need to continue to invest in data because we need to understand that the few dollars which we have are invested well. So I will just bang the drum for that and take the opportunity. Mobile network operators, the work we're talking about relies on them opening up their data in a manner on which high -quality work can be conducted. And I think that means that it needs to be non -begrudging. The mobile network operators should not have their hand behind their back. Otherwise, you may not see the quality of data that you need to get out at the frequency for high -quality work. And so what enables that? And that's a value exchange with mobile network operators so that they are willing and happy, in fact, glad to open up their data. And Vodacom. I'll ask you a question. I'll promote them because they make the data available to us without charge. the benefits of them is through their kind of corporate social responsibility or external affairs the profile of Vodacom within the Congo. Thanks.
Fredrik Eriksson
Thank you so much. Yeah I was just thinking about the last question actually I think the first one has been answered. Obviously in that sense it's a very philosophical question which is quite interesting and having worked in data so many many years I certainly don't believe necessarily in many cases that there's a scarcity of data. There's a scarcity of combining data as you say and now it's analyzing a lot of data. There's also a lot of the cultural aspects but I think I just wanted to highlight and actually go back to Daniel's there. This is also something when mobile phone data was starting in terms of the value of that data source and I can see that just showing now also the examples from Congo it's become clear that this data source has a great value in that sense and I think that will ... improve sort of the aptitude for working with this sort of data as well and be able to combine it with other data sources and really see the value of that. So really showcasing the benefits just like what Daniel showed I think is key.
Audience
We trust you very much.
Esperanza Magpantay
That's reassuring. Thank you so much. Thank you so much. I think we don't have any questions from our remote participants and I think this will be our last question before I conclude the session.
Audience
Thank you. So we have two questions please. The first one can be mobile phone data replace traditional surveys or should they be considered as a complementary data source? The second question is about the number of internet users please. the number of internet users. So would you recommend using mobile phone data to calculate this number or traditional field surveys, please?
Esperanza Magpantay
I'll pass the floor quickly to or I can answer coming also from the ITU. So the work that we are doing on mobile phone data, we're not saying it will replace traditional data sources. It will be used hand -in -hand with traditional data sources. It can be a complement. It can be a supplement to traditional data sources. And the beauty of using mobile phone data is you can have increased timeliness, so you can produce the data whenever you want, provided you have access to the data, plus you will have geographic granularity of the data. And in terms of internet use, we are not saying use mobile phone data instead of traditional data sources. The same logic, you can use both, but in the absence of traditional survey, because survey is very expensive, and we see from experiences that countries are not collecting it from traditional surveys. We have to find a way to produce the data, and mobile phone data is the prime source for us, and that's why we invested in producing tools and helping countries use this new data source. So, I think with that, I would like to conclude the session. There has been a lot of discussions, a lot of use cases, a lot of materials. The discussion doesn't stop here. We invite you to check all the materials that will be available in the session website. My colleague has also shared some of the links and also other intervenants. I invite you to check on those and continue the discussion. There are other forums that you can join. For example, the task team, where we welcome countries and experts who would like to learn or who would like to share their experiences, and those avenues are all open for you. But with that, I'd like to conclude the session, and thank you for being here, both for the
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1

The knowledge base supports this overall framing. Several sources note that mobile network data is seen as a valuable structured big-data source for official statistics and SDG monitoring, especially when integrated into national statistical systems rather than treated as isolated experiments [S16] and [S82].

2

The knowledge base confirms the broader UN statistical push to explore new data sources, especially big data, for official statistics and SDG monitoring, although it does not independently verify the specific 2014 start date or exact mandate wording. ITU and UN statistical discussions explicitly encouraged exploration of big data and other new sources while keeping national official statistics central [S66], and later UN data-for-SDG discussions highlighted the same direction [S16] and [S81].

3

This is consistent with the knowledge base. Mobile data is repeatedly identified as a high-potential structured big-data source for monitoring, policy design, and official statistics, alongside sources such as satellite imagery [S16] and [S82].

4

The knowledge base supports the substance of this definition, even if it does not list all categories in the same formulation. It explains that mobile providers hold telephone traffic details and geolocation information derived from phones connecting to local base stations, which can reveal time, duration, and location patterns [S96]. It also notes that states can identify mobile phones within an area through network-based methods, confirming that network interaction creates traceable records [S95].

5

The knowledge base corroborates the mobility-analysis aspect. It explains that mobile providers can retain call timing and location-related information based on connections to base stations, and that tracking base stations can infer a user's location within a region or even more precisely under triangulation [S96].

6

The knowledge base strongly supports the second half of this claim: mobile network data can be valuable for public policy, SDG monitoring, and official statistics [S16] and [S82]. It also shows that operators hold such data in the normal course of providing service, since they maintain traffic details and location-related records as part of network operations and, in some jurisdictions, data retention [S96].

7

This aligns closely with the wider UN data-policy context in the knowledge base. Multiple sources stress the need to scale existing projects, create long-term statistical infrastructure, and avoid one-time funding or fragmented approaches when integrating new data sources into official statistics [S81] and [S83].

8

The knowledge base does not verify this exact three-pillar framework, but it provides supporting context for why such pillars matter. UN and policy discussions repeatedly emphasise legal frameworks, standards, trust, capacity, institutional reform, and technical modernisation as prerequisites for using big data and other non-traditional sources in official statistics [S81], [S82], and [S83].

9

The need for a strong legal and privacy framework is well supported. The knowledge base highlights significant privacy and surveillance risks associated with mobile communications and location data, including interception, retention, and tracking, underscoring why any official use of MPD must address data protection and regulatory compliance [S20], [S95], and [S96].

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WS #83 the Relevance of Dpgs for Advancing Regional DPI Approaches — Preuves India stack signed MOUs with Cuba, Colombia, Suriname, Trinidad and Tobago, and Barbados; represents practically a subregion with strong open source communities in the Caribbean Major discussion point Cros...
AI, smart cities, and the surveillance trade-off — Without deliberate intervention, AI will reproduce and amplify those patterns at scale. As cities around the world rush to deploy AI systems, these questions can’t be postponed until the algorithms are already making d...
[WebDebate #22 summary] Algorithmic diplomacy: Better geopolitical analysis? Concerns about human rights? — Diplomats must understand they are dealing with a cross border phenomenon which has its own belief system. The established order of things for diplomats and the technology sector of the Internet differs, the internet gov...
GSMA — The GSM Association is an industry organisation that represents the interests of mobile network operators worldwide. More than 750 mobile operators are full GSMA members and a further 400 companies in the broader mobile ...
Rapport of the Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression, Frank La Rue* (A/HRC/23/40) — The initiative of the European standards-setting authority, the European Telecommunications Standards Institute, to compel cloud providers22 to build “lawful interception capabilities” int...
Comprehensive study on cybercrime — The printout of information located on a computer or other storage device might not technically be regarded as ‘original.’ In some jurisdictions, however, the best evidence rule does not operate to exclude printouts...
Empowering change with data: Measuring youth digital mobility — For example, the government published a PDF land database related to a government project involving creating artificial land to solve housing problems. Yet, researchers had to buy geo-maps from the government, since they...

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