Detalhes bibliográficos
| Ano de defesa: |
2022 |
| Autor(a) principal: |
OLIVEIRA, Augusto César Ferreira de Miranda
 |
| Orientador(a): |
CUNHA FILHO, Moacyr |
| Banca de defesa: |
LUCENA, Edson Hilan Gomes de,
OLIVEIRA, Fábio Henrique Portella Corrêa,
PISCOYA, Victor Casimiro |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biometria e Estatística Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8748
|
Resumo: |
Spatial data analysis refers to the process of finding patterns, detecting anomalies, or testing hypotheses and theories by observing phenomena associated with a specific geographic area or location. Understanding the spatial distribution of phenomena is still a challenge frequently reported in several areas of knowledge due to access to accurate, historical, timely, and compatible spatial data. Another challenge is also related to spatial data organization in an intelligent way to speed up the consultation process and the construction of spatial analyses. In this sense, this work aimed to propose a web approach for data processing and construction of spatial analysis of area and points. To design the analyses, two methods/algorithms were built, one for generating spatial point data and another for associating data to its spatial context, that is, to its micro-regions and meso-regions. The proposed approach was developed using the JavaScript programming language. Choroplectic mapping methods; kernel density estimation; and correlation dimension were used to construct the analyses. The approach was validated using data from three diseases (cystic fibrosis, congenital adrenal hyperplasia, and hemoglobinopathies) from a neonatal screening program in southern Brazil. Data were collected between 2004 and 2020. The approach developed proved to be relevant in the context of spatial analysis, enabling speed in processing, data organization, and, consequently, in the construction of significant results that can be used in public policies that directly impact people’s quality of life and health challenges. The approach also showed high replication potential for other study contexts. |
