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{subsubsection}{\numberline {2.5.2}NDWI}{10}{subsubsection.2.5.2}% -\contentsline {paragraph}{\numberline {2.5.2.1}Cálculo}{10}{paragraph.2.5.2.1}% +\contentsline {subsubsection}{\numberline {2.5.2}NDWI}{11}{subsubsection.2.5.2}% +\contentsline {paragraph}{\numberline {2.5.2.1}Cálculo}{11}{paragraph.2.5.2.1}% \contentsline {paragraph}{\numberline {2.5.2.2}Clasificación}{11}{paragraph.2.5.2.2}% \contentsline {subsubsection}{\numberline {2.5.3}NDBI}{11}{subsubsection.2.5.3}% \contentsline {paragraph}{\numberline {2.5.3.1}Cálculo}{11}{paragraph.2.5.3.1}% -\contentsline {subsubsection}{\numberline {2.5.4}NDBaI}{11}{subsubsection.2.5.4}% -\contentsline {paragraph}{\numberline {2.5.4.1}Cálculo}{11}{paragraph.2.5.4.1}% +\contentsline {subsubsection}{\numberline {2.5.4}NDBaI}{12}{subsubsection.2.5.4}% +\contentsline {paragraph}{\numberline {2.5.4.1}Cálculo}{12}{paragraph.2.5.4.1}% \contentsline {subsubsection}{\numberline {2.5.5}NDMI}{12}{subsubsection.2.5.5}% -\contentsline {paragraph}{\numberline {2.5.5.1}Cálculo}{12}{paragraph.2.5.5.1}% -\contentsline {subsection}{\numberline {2.6}Clasificación de superficies}{12}{subsection.2.6}% -\contentsline {subsection}{\numberline {2.7}Campo de atracción}{13}{subsection.2.7}% -\contentsline {subsubsection}{\numberline {2.7.1}Identificación de viviendas}{13}{subsubsection.2.7.1}% -\contentsline {subsubsection}{\numberline {2.7.2}Cálculo del campo de atracción}{15}{subsubsection.2.7.2}% -\contentsline {section}{\numberline {3}Conclusiones}{17}{section.3}% +\contentsline {paragraph}{\numberline {2.5.5.1}Cálculo}{13}{paragraph.2.5.5.1}% +\contentsline {subsection}{\numberline {2.6}Clasificación de superficies}{13}{subsection.2.6}% +\contentsline {subsection}{\numberline {2.7}Campo de atracción}{14}{subsection.2.7}% +\contentsline {subsubsection}{\numberline {2.7.1}Identificación de viviendas}{15}{subsubsection.2.7.1}% +\contentsline {subsubsection}{\numberline {2.7.2}Cálculo del campo de atracción}{16}{subsubsection.2.7.2}% +\contentsline {subsection}{\numberline {2.8}Densidad Inicial}{17}{subsection.2.8}% +\contentsline {subsubsection}{\numberline {2.8.1}Lectura y Procesamiento de Datos}{17}{subsubsection.2.8.1}% +\contentsline {subsubsection}{\numberline {2.8.2}Interpolación Gaussiana}{18}{subsubsection.2.8.2}% +\contentsline {subsubsection}{\numberline {2.8.3}Visualización de Resultados}{18}{subsubsection.2.8.3}% +\contentsline {section}{\numberline {3}Conclusiones}{20}{section.3}% diff --git a/Informe/Images/densidad.png b/Informe/Images/densidad.png new file mode 100644 index 0000000..9c2d00b Binary files /dev/null and b/Informe/Images/densidad.png differ diff --git a/Informe/conclusiones.aux b/Informe/conclusiones.aux index b48e003..65be321 100644 --- a/Informe/conclusiones.aux +++ b/Informe/conclusiones.aux @@ -1,9 +1,9 @@ \relax \providecommand\hyper@newdestlabel[2]{} -\@writefile{toc}{\contentsline {section}{\numberline {3}Conclusiones}{17}{section.3}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {3}Conclusiones}{20}{section.3}\protected@file@percent } \@setckpt{conclusiones}{ -\setcounter{page}{18} -\setcounter{equation}{0} +\setcounter{page}{21} +\setcounter{equation}{1} \setcounter{enumi}{0} \setcounter{enumii}{0} \setcounter{enumiii}{0} @@ -16,11 +16,11 @@ \setcounter{subsubsection}{0} \setcounter{paragraph}{0} \setcounter{subparagraph}{0} -\setcounter{figure}{9} +\setcounter{figure}{10} \setcounter{table}{2} \setcounter{Item}{0} \setcounter{Hfootnote}{0} -\setcounter{bookmark@seq@number}{19} +\setcounter{bookmark@seq@number}{23} \setcounter{tabx@nest}{0} \setcounter{listtotal}{0} \setcounter{listcount}{0} diff --git a/Informe/conclusiones.tex b/Informe/conclusiones.tex index e42a5ce..2a0fb1e 100644 --- a/Informe/conclusiones.tex +++ b/Informe/conclusiones.tex @@ -1,3 +1,15 @@ \section{Conclusiones} -3 Conclusiones +Este informe concluye el trabajo desarrollado en el marco del cursado de la materia TIC y Geomática. El objetivo central fue aplicar un modelo epidemiológico para predecir el crecimiento y difusión del vector que transmite el dengue en la localidad de Oro Verde, mediante el uso de sensores remotos y técnicas geoespaciales. + +Se logró una delimitación precisa del área de estudio en Oro Verde, utilizando datos georeferenciados de ovitrampas y el procesamiento de estos. La obtención de imágenes satelitales de Landsat 8 fue fundamental para el desarrollo del trabajo. + +La aplicación de diversos índices espectrales fue crucial para evaluar diferentes características del terreno, proporcionando una visión integral del área de interés, identificando factores que afectan la distribución del vector. + +Un aspecto notable fue la implementación de técnicas matemáticas para suplir el submuestreo experimental. Estas técnicas permitieron interpolar y modelar la densidad espacial de huevos de mosquitos, proporcionando una estimación para áreas donde no se contaba con datos experimentales. + +Se implementaron gráficos que permiten una visualización clara y comprensible de las variables involucradas en la distribución espacial del vector del dengue. + +En resumen, este proyecto ha demostrado la efectividad de combinar datos satelitales y herramientas geoespaciales para analizar y comprender la distribución espacial de los vectores. Se sientan las bases para futuras investigaciones y estrategias de control en regiones afectadas por esta enfermedad. + +Este trabajo representa la culminación del proceso formativo en la materia TIC y Geomática, aplicando los conocimientos adquiridos a una problemática real y relevante para la salud pública. La experiencia obtenida a lo largo del desarrollo de este proyecto ha sido invaluables, proporcionando una comprensión profunda de las técnicas geoespaciales y su aplicación práctica en el campo de la epidemiología. \ No newline at end of file diff --git a/Informe/desarrollo.aux b/Informe/desarrollo.aux index 31f1d7e..0a95c21 100644 --- a/Informe/desarrollo.aux +++ b/Informe/desarrollo.aux @@ -28,39 +28,45 @@ \newlabel{clasificacion}{{2.5.1.2}{10}{Clasificación}{paragraph.2.5.1.2}{}} \@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Criterio de clasificación en base al NDVI.\relax }}{10}{table.caption.7}\protected@file@percent } \newlabel{table:ndvi-criterio}{{2}{10}{Criterio de clasificación en base al NDVI.\relax }{table.caption.7}{}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.5.2}NDWI}{10}{subsubsection.2.5.2}\protected@file@percent } -\@writefile{toc}{\contentsline {paragraph}{\numberline 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Se utilizaron dos tipos de datos: una imagen rasterizada en formato TIF y un archivo CSV con coordenadas y conteos de huevos. + +El archivo CSV contiene datos puntuales con coordenadas y conteos de huevos, los cuales se filtraron por una fecha específica a modo de ejemplo para enfocarse en un momento determinado del estudio, que se corresponde con aquella en la que más huevos se registraron. + +\subsubsection{Interpolación Gaussiana} + +Para calcular la densidad espacial de los huevos, se empleó una técnica de interpolación basada en una función gaussiana bidimensional. La función gaussiana es una representación matemática que describe una distribución normal en dos dimensiones, caracterizada por su forma de campana: + +\begin{equation} + G(x, y) = \exp\left(-\frac{(x - x_0)^2 + (y - y_0)^2}{2\sigma^2}\right) +\end{equation} + +Se utilizó esta función para modelar la distribución espacial de los huevos alrededor de cada punto de datos. + +\subsubsection{Visualización de Resultados} + +Finalmente, los resultados se visualizaron superponiendo el mapa de densidad calculado sobre la imagen TIF original. Esta visualización permite observar la distribución espacial de la densidad de huevos en la región de estudio (Figura \ref{fig:densidad}). + +\begin{figure}[H] + \includegraphics[width=\textwidth]{densidad.png} + \centering + \caption{Densidad inicial para la región de interés.} + \label{fig:densidad} +\end{figure} + + diff --git a/README.md b/README.md index 9a57ef6..8ea2bd3 100644 --- a/README.md +++ b/README.md @@ -1,81 +1,33 @@ -# Análisis de Densidad de Mosquitos con Datos de Ovitrampas y Landsat 8 - -Este proyecto tiene como objetivo analizar la densidad de mosquitos en la región de Oro Verde, Entre Ríos, Argentina, utilizando datos recolectados con ovitrampas y combinándolos con imágenes satelitales del Landsat 8. - - --------- - -## Decisiones - -Por la ubicación: - -- Path: 227 -- Row: 82 +
-![Path and row](imgs/pathrow.png) - -Definición de la ROI: -Se tomó los datos de las ovitrampas para todos los períodos de tiempo respecto a los cuales se tenían datos y se dejó un márgen de 1km para definir un área de interés cuadrada. Este cálculo está en *visualizarOvitrampas.ipynb*. - -La idea es: -Tomar el mismo enfoque y ver si podemos aplicarlo a una sola imagen y luego a otras imagenes de diferentes resolución temporal. Toma una imagen, la expresa en RGB. - ------ - -## Partes - -- [X] Designación del área de estudio - - [X ] Definir ROI. Substraer de las imagenes la región de interés. -- [X] Obtención de las imagenes -- [ ] Describir las bandas, cada banda para qué es útil. Comparar con Landsat 5 y 8, los rangos y hacer la comparativa. No estaría ma citar y hacer un cuadro comparativo para después extrapolar. -- [ ] Análisis visual. Lo que tiene. Mostrar la imagen en color real -- [ ] No describir el modelo porque ya existe pero si explicar como el modelo se podría aplicar a otro lado, con los datos que tenemos. Si faltan datos reales, no importa, podríamos completar con datos falsos. -- [ ] Obtener índice urbano para obtener la *constante de atracción*. Habiendo obtenido el índice urbano clasificar vivienda si o no, una vez hecha la clasificación puedo calcular como hicieron ellos con la ventana el valor de atracción. -- [ ] Identificar clases de rugosidad -- [ ] Definir si la densidad de mosquitos se tiene en cuenta. - - [ ] Dentro de las opciones de clasifficación del QGIS tenemos para elegir como clasificar según los vecinos (Maxima Verosimilitud) - - [ ] En vez de usar la clasificación puedo tomar la de las áreas. -- [ ] Obtener los valores del viento, solo la velocidad. - ------ +# Análisis de Densidad de Mosquitos con Datos de Ovitrampas y Landsat 8 -## Modelo +[Descripción](#️-descripción) • [Estructura de Directorios](#-estructura-de-directorios) • [Links de Interés](#paperclip-links-de-interés) • [Contacto](#-contacto) • [Referencias](#-referencias) -$$\frac{\partial \rho(P,t)}{\partial t}=\triangledown .(D_R \triangledown \rho)-\triangledown.(\rho D_W V)- \triangledown . (\rho K_H \triangledown H) + \alpha - \beta$$ -Donde: +
+
+ Logo UNER +
+
-| Simbolo | Variable | Valor | -| :--------: | --------------------------- | -------------------------- | -| $$P$$ | Densidad de mosquitos | No homogéneo | -| $$\alpha$$ | Tasa de nacimientos | $6 (m²/dia)$ | -| $$\beta$$ | Tasa de muertes | 0.2 | -| $$V$$ | Velocidad Viento Superficie | No homogéneo | -| $$K_H$$ | Tensor de atracción | 100 | -| $$H$$ | Campo de atracción | No homogéneo | -| $$D_R$$ | Tensor de difusión | No homogéneo / ver Tabla 2 | -| $$D_W$$ | Tensor de rugosidad | No homogéneo / ver Tabla 2 | +
-| Clases de Rugosidad del Paisaje | $D_W$ | $D_R$ | -| :-----------------------------: | :---: | :---: | -| Agua | 1 | 0 | -| Suelo Expuesto | 1 | 0.2 | -| Vegetación Baja | 0.7 | 0.3 | -| Bosques Abiertos | 0.5 | 0.5 | -| Selva de Yungas | 0.3 | 0.7 | +## 🛰️ Descripción -- Todos los tensores son considerados como escalares. - - $D_R$ y $D_W$ dependen del paisaje, considerándose dependientes de las clases de uso del suelo. -- El campo de atracción es un valor por pixel de un raster de 200x200. - - Esto entiendo que es porque usan una ROI de 5.7km por 5.7km, esto con una resolución espacial de 30metros daría 190 píxeles. - - Yo voy a tener que definir estos tamaños según el área de interés que defina. +Este proyecto tiene como objetivo analizar la densidad de mosquitos en la región de Oro Verde, Entre Ríos, Argentina, utilizando datos recolectados con ovitrampas y combinándolos con imágenes satelitales del Landsat 8. +## 📂 Estructura de directorios -El factor de propagación es 150x150 por día. +- ***Imagenes/***: +- ***Informe/***: +- ***data/***: +- ***imgs/***: +- ***modulos/***: +- main.py: ------ +### 📬 Contacto ------ +### 📚 Referencias -## Referencia diff --git a/campoDeAtraccion.png b/campoDeAtraccion.png deleted file mode 100644 index b3fb67b..0000000 Binary files a/campoDeAtraccion.png and /dev/null differ diff --git a/imgs/logouner.png b/imgs/logouner.png new file mode 100644 index 0000000..8fefca1 Binary files /dev/null and b/imgs/logouner.png differ diff --git a/modulos/visualizar_nro_huevos.ipynb b/modulos/visualizar_nro_huevos.ipynb index ef962a6..eabc6e2 100644 --- a/modulos/visualizar_nro_huevos.ipynb +++ b/modulos/visualizar_nro_huevos.ipynb @@ -117,9 +117,17 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/justo/.local/lib/python3.10/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "data": { "image/png": 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", diff --git a/qgisptoject.qgz b/qgisptoject.qgz deleted file mode 100644 index d95c010..0000000 Binary files a/qgisptoject.qgz and /dev/null differ