Where would you open a new Pizza Restaurant in Buenos Aires?
- Autores
- Maraggi, Santiago
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The goal of the work is to extract an initial guideline to determine the best place to open a new Pizza Restaurant in Buenos Aires. In this work the K-Means algorithm was applied to classify the neighborhoods of Buenos Aires, according with their most common venue types. For each neighborhood, top 50 most common venue types were established, and then, 10 neighborhood clusters were obtained with the mentioned algorithm in order to provide some clues about which neighborhoods could be best investment options.
Sociedad Argentina de Informática - Materia
-
Ciencias Informáticas
Business analysis
City venue types
Neighborhood clustering - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/3.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/114889
Ver los metadatos del registro completo
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Where would you open a new Pizza Restaurant in Buenos Aires?Maraggi, SantiagoCiencias InformáticasBusiness analysisCity venue typesNeighborhood clusteringThe goal of the work is to extract an initial guideline to determine the best place to open a new Pizza Restaurant in Buenos Aires. In this work the K-Means algorithm was applied to classify the neighborhoods of Buenos Aires, according with their most common venue types. For each neighborhood, top 50 most common venue types were established, and then, 10 neighborhood clusters were obtained with the mentioned algorithm in order to provide some clues about which neighborhoods could be best investment options.Sociedad Argentina de Informática2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf110-114http://sedici.unlp.edu.ar/handle/10915/114889enginfo:eu-repo/semantics/altIdentifier/url/http://49jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-12.pdfinfo:eu-repo/semantics/altIdentifier/issn/2683-8966info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-04-15T11:33:58Zoai:sedici.unlp.edu.ar:10915/114889Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-04-15 11:33:58.636SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Where would you open a new Pizza Restaurant in Buenos Aires? |
| title |
Where would you open a new Pizza Restaurant in Buenos Aires? |
| spellingShingle |
Where would you open a new Pizza Restaurant in Buenos Aires? Maraggi, Santiago Ciencias Informáticas Business analysis City venue types Neighborhood clustering |
| title_short |
Where would you open a new Pizza Restaurant in Buenos Aires? |
| title_full |
Where would you open a new Pizza Restaurant in Buenos Aires? |
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Where would you open a new Pizza Restaurant in Buenos Aires? |
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Where would you open a new Pizza Restaurant in Buenos Aires? |
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Where would you open a new Pizza Restaurant in Buenos Aires? |
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Maraggi, Santiago |
| author |
Maraggi, Santiago |
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Maraggi, Santiago |
| author_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Business analysis City venue types Neighborhood clustering |
| topic |
Ciencias Informáticas Business analysis City venue types Neighborhood clustering |
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The goal of the work is to extract an initial guideline to determine the best place to open a new Pizza Restaurant in Buenos Aires. In this work the K-Means algorithm was applied to classify the neighborhoods of Buenos Aires, according with their most common venue types. For each neighborhood, top 50 most common venue types were established, and then, 10 neighborhood clusters were obtained with the mentioned algorithm in order to provide some clues about which neighborhoods could be best investment options. Sociedad Argentina de Informática |
| description |
The goal of the work is to extract an initial guideline to determine the best place to open a new Pizza Restaurant in Buenos Aires. In this work the K-Means algorithm was applied to classify the neighborhoods of Buenos Aires, according with their most common venue types. For each neighborhood, top 50 most common venue types were established, and then, 10 neighborhood clusters were obtained with the mentioned algorithm in order to provide some clues about which neighborhoods could be best investment options. |
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2020 |
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2020-10 |
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eng |
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