| Titre : | GIS and Machine Learning for Small Area Classifications in Developing Countries |
| Auteurs : | Ojo Adegbola, Auteur |
| Support: | Livre |
| Editeur : | CRC press, 2021 |
| ISBN/ISSN/EAN : | 978-0-367-65232-6 |
| Format : | 246P / 24 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Index. décimale : | 624 (Génie civil (aspects techniques de l'architecture du paysage, de l'utilisation d'énergie dans la construction ; construction, génie des bâtiments et des travaux publics, moyens de communication, techniques de l'architecture du paysage, travaux publics)) |
| Mots-clés: | GIS ; Machine Learning ; Small Area ; Classifications ; Developing Countries |
| Résumé : | Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. |
Exemplaires (1)
| Cote | Support | Localisation | Disponibilité | Emplacement |
|---|---|---|---|---|
| T8.624/7071 | Livre | Bibliothèque centrale El Allia | Disponible | Magazin |
