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--- sistemas:gis [2009/10/18 16:34]
alfred
+++ sistemas:gis [2020/05/09 09:25] (actual)
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 ===== Básico =====
@@ Línea 24: / Línea 25: @@
   * **Datos raster**: Imágenes que han sido tomadas de forma aerea o por satélite, se guardan en un formato de grid (por celdas). No contiene datos implicitos más que los de la posición de cada celda para luego mostrar las imágenes en su posición correcta dentro de la capa, y poder mostrar así los datos de un mapa de forma "vistosa" y real.
   * **Datos vectoriales**: No fotográficos sino generados a partir de coordenadas; a partir de este tipo de datos es más sencillo realizar estudios. Los tipos vectoriales más significativos son el punto (puede representar aeropuertos, pizzerias...), la línea (unión de dos puntos, puede representar carreteras, ríos, itinerarios, caminos...) y los polígonos (unión de un número indeterminado de líneas, para representar poblaciones, fronteras, ciudades...). Es más caro de mantener que los datos raster.
-  * **Features**: Los elementos independientes que se muestran en el mapa, estos pueden contener atributos espaciales y no espaciales.
+  * **Features**: Los elementos independientes que se muestran en el mapa, estos pueden contener atributos espaciales (puntos, línias...) y no espaciales.
   * **OGC** (Open Geospatial Consortium): Conjunto de organizaciones que marcan los estándares GIS.
-  * **WKT** (Well Known Text): Sintaxis en texto plano para describir los objetos espaciales.
 === Servicios ===
   * **WMS** (Web Map Service), es un protocolo para pasar imágenes raster.
@@ Línea 39: / Línea 39: @@
   * **Georeferenciación lineal** (LRS (Linear Reference System)): es una forma de referenciar a través de elementos lineales. Los elementos se localizan por un punto conocido como //milepoint// o un evento lineal //segmento//.
   * **Ortofoto**, foto realizada desde el aire (desde avión o satelite) que mediante una corrección corresponde con el mapa.
+  * **WKT** (Well Known Text): Sintaxis en texto plano para describir los objetos espaciales.
@@ Línea 45: / Línea 47: @@
     * El ''.shp'' que contiene los datos geográficos en formato vectorial.
     * El ''.shx'' es el fichero índice, por cada registro en el .shp hay uno en este.
-    * El ''.dbf'' contiene los datos enlazados y no geoespaciales en formato dBASE (legible por MS Excell).
+    * El ''.dbf'' contiene los datos enlazados y no geoespaciales en formato dBASE (legible por MS Excel).
     * Puede existir un cuarto, el ''.prj'' que indica la proyección del fichero y está escrito en WKT.
   * **GML** (Geographic Markup Language), en formato XML, abierto y expuesto como ficheros de texto. Sobretodo se utiliza en servicios web.
-  * **KML** (eyhole Markup Language) , parecido al GML ya que también está basado en XML, es el utilizaod por Google Eart.
+  * **KML** (Keyhole Markup Language) , parecido al GML ya que también está basado en XML, es el utilizado por Google Eart. [[tags:kml|Entrada en la wiki]].
@@ Línea 77: / Línea 79: @@
-==== Escalado y resolución ====
-:!:
-<code>
-Scale and Resolution in the Analog World
-When dealing with paper maps, pixels aren’t of much use to us as a
+==== Otros conceptos ====
-unit of measure; the size of your map is generally measured in inches
+La **escala** (el escalado) de un mapa se expresa en un ratio 1:1000, el cual significa que 1 unidad en el mapa equivale a 1000 de esa unidad en el suelo. Por ejemplo pongamos un mapa de 30cm por 30cm, en este se muestra un área de 30km por 30km, entonces la escala es de 30:3.000.000 o lo que es lo mismo 1:100.000 (dividiendo las dos partes del ratio por la parte de la izquierda), es decir 100.000cm es en realidad 1.000m o, lo que es lo mismo, 1km. Y si en el mapa mostrasemos el doble de espacio la escala sería 1:200.000 (un mapa de menos **resolución**).
-or centimeters. However, the physical size of your map is only half of the
-equation—you’ll still want to know how much ground space the paper
-map represents. What you’re looking for is the scale of the map. This is
-commonly expressed as a ratio: 1:1000 means that 1 unit on the map
-is equivalent to 1,000 of those same units on the ground.
-This notion of ratios comes up again and again in cartography. For
-example, let’s say you have a 30 cm by 30 cm (1 foot by 1 foot) paper
-map that shows you a 30 km by 30 km (18.6 miles by 18.6 miles) area
-on the ground. Our map scale is 30:3,000,000.
-That looks bit odd, doesn’t it? Map scales are usually reduced so that
-the left side of the ratio is 1. Dividing both sides of the ratio by 30
-gives us a more normal-looking map scale of 1:100,000—1 cm on the
-map represents 100,000 cm on the ground. To further refine this ratio,
-,000 cm is really 1,000 m, which is 1 km. You might see a scale
-for this hypothetical map expressed as “1 centimeter on the map rep-
-resents 1 kilometer on the ground,” but the least ambiguous way to
-express the scale is to say simply it is 1:100,000 and leave the inter-
-pretation up to the reader.
-Now let’s say that you want to see a bigger area of the earth on your
-map. If you want to see twice as much ground space per side (60 km),
-you have two options: you could double the physical size of your map
-to 60 cm, or you could cram 60 km into the same 30 cm map. In the
-first case, you are maintaining the same scale as the earlier map. If
-you hold the size of your map constant, something has got to give.
-That something is the level of detail or the resolution of the map. Your
-effective scale is now 1:200,000 (1 cm on the map represents 2 km on
-the ground). You have a lower-resolution map—in other words, you can
-see less detail.
-If you move in the opposite direction—increasing your resolution—
-either your map will get progressively larger or you will be able to see
-less total ground space on the same-sized map. Do you see how if you
-hold the size of your map constant, there is an inverse relationship
-between the resolution and the total ground space? You can see either
-less total earth at a higher level of detail or more total earth at a lower
-level of detail. (This should remind you of our imaginary basketball
-globe story earlier in the book.)
-This magic ratio explains why statewide highway maps are so darn big.
-They have to be 2 to 3 feet on a side to display all of the highways
-at a resolution that you can see easily. But highway map resolution
-doesn’t allow you to navigate your way through local neighborhoods;
-no single piece of paper could realistically hold that level of detail. If
-you’ve ever been out house hunting in your real estate agent’s car, the
-agent probably has a thick neighborhood guide that fits ten to twelve
-city blocks to a standard 8.5 by 11 page.
-Scale and Resolution in the Digital World
+===== Geodatabases =====
-Let’s now move our focus back to digital mapping. Digital images are
-measured in pixels (a combination of the two words picture element).
-Pixels are like degrees in that they are a relative unit of measure when
-it comes to distance. For example, my laptop screen optimally runs at
-a resolution of 1280 by 854. I have a 15-inch PowerBook G4, so we
-can figure out the dots per inch (DPI) of my monitor using some pretty
-simple math: 1,280 pixels divided by 15 inches gives me a DPI of about
-. (Historically, people have used 72 DPI as a benchmark for computer
-displays, but as you’ll see in a moment that number can be changed
-with the click of a button.)
-I use my laptop for presentations quite a bit, but I have yet to find an
-LCD projector that will allow me to run at native resolution. If I’m lucky,
-I’ll get knocked down to 1024 by 800, but more often than not, I end
-up running at 800 by 600. Obviously, the physical size of my laptop
-screen doesn’t change, but my resolution and corresponding DPI takes
-a pretty big hit. A 15-inch screen displaying 800 pixels yields a DPI of
-a little more than 53. Just like my paper map in the previous section,
-I lose total desktop space (ground space), but I can see everything else
-in much greater detail (resolution). When I disconnect the projector, my
-desktop gets much bigger, but my individual icons get much smaller.
-Looking now at raster images, we still need a way to express “this
-much on my screen represents this much on the ground.” Unfortu-
-nately, as we just learned, expressing things in inches or centimeters
-can be problematic. The only two absolutes we have are the dimensions
-of the image in pixels and the ground space that each pixel represents.
-Since you can’t very well measure ground space in pixels, we lose the
-traditional notion of a scale ratio. Instead, we talk about ground sample
-distance (GSD).
-For example, we know that a typical DOQQ is 8,000 pixels across in
-image space and 8 kilometers across in ground space. This gives us a
-GSD of 1 meter per pixel. Regardless of your screen resolution, your
-image resolution will always be 1 pixel = 1 meter. (For more informa-
-tion on DOQQs, see either Terraserver-USA’s About page7 or the USGS
-Factsheet.8 Both are chock-full of geobabble that shouldn’t scare you
-in the least if you’ve made it this far.)
-Most of the DOQQs date from the mid-1990s. The USGS has been sys-
-tematically updating its data set with newer, higher-resolution, multi-
-spectral imagery. The Urban Areas data set generally dates from 2000
-and later. Its GSD ranges from 0.5 meters (roughly 1.5 feet) down to
-.15 meters (6 inches). As storage gets cheaper and sensors get more
-powerful, the USGS will update its data set accordingly. It keeps the
-DOQQ data set around for now because it has more complete cov-
-erage of the United States, but eventually the panchromatic country-
-wide mosaic will be completely replaced by the newer high-resolution
-imagery. (For more information, see the fact sheet about high-resolu-
-tion orthoimagery.9 )
-If you want to prove to yourself that the multispectral rasters on Terra-
-server-USA are higher resolution than the panchromatic DOQQs, go
-back to your view of the state capitol. Zoom in as far as you can on the
-Aerial data set, and then flip over to the Urban Areas tab. You should
-have a couple more clicks to zoom in. Did you also notice that once
-you zoomed into the maximum resolution on the Urban Areas tab, the
-Aerial tab disappeared? Zoom a couple of clicks out, and the other tab
-should reappear.
-So, what’s going on? The mapmakers wanted to make sure that you
-didn’t exceed the native resolution of the imagery. Downsampling
-(zooming out) doesn’t pose much risk—if you want to see a lower-
-resolution snapshot of the imagery, you can easily adjust the GSD with-
-out affecting the quality of the output. Of course, you’ll see less detail,
-but then again that’s what you asked for, isn’t it? You are losing detail,
-but the original image has all of the data necessary to safely show you
-the data at the newly requested resolution.
-On the other hand, upsampling the data beyond the native resolution
-can cause serious output issues. By zooming closer than what the
-imagery can support, the pixels get blocky and generally icky looking.
-Your image gets pixelated because you’re asking to see more informa-
-tion than the image can provide.
-Both Google Maps and Terraserver-USA optimize performance by pre-
-downsampling the data to a series of fixed levels. This is called pyra-
-miding your data set; each time you reduce the resolution but don’t
-increase the ground space coverage, the total width and height of your
-image is reduced. At native 1-meter resolution, a DOQQ is 8,000 pixels
-by 8,000 pixels. If you downsample the image to 2-meter resolution,
-your image is now 4,000 by 4,000 pixels. If you downsample to 4-meter
-resolution, your image drops to 2,000 by 2,000 pixels. Hence, you have
-the pyramid effect.
-Screen Resolution vs. Print Resolution
-As if all of this image resizing isn’t complicated enough, there is one
-more gotcha waiting to getcha. That gotcha shows up once you try
-to create a “dead-tree” (printed) edition of your raster. Earlier in this
-section we talked about typical screen resolutions in DPI. My laptop’s
-native DPI is about 85 but can drop down to 55 based on what the
-external projector can support. If you’ve looked at your printer specs
-recently, you know that printers generally start at 300 DPI and can go
-up to 600 DPI or higher. This means that the physical size of your map
-can vary greatly between what you can see on your screen and what
-comes from your printer.
-Our trusty DOQQ is about 94 inches wide on my screen, or close to
-feet wide (8,000 pixels at 85 DPI)—that’s a lot of scrolling. However,
-that same DOQQ printed out at 600 DPI is just more than 13 inches
-wide. The focus of this book is on digital mapmaking, but it’s nice to
-know what will happen when your users press Ctrl+P.
-</code>
-===== Geodatabases =====
 ===== Servicios OGC =====
+==== Servicios WMS ====
+O //Web Map Service//, permite a los clientes utilizar sus datos para crear mapas configurables, estos mapas podrán enlazar datos de distintos fuentes WMS para así crear un resultado más complejo. \\
+Los servidores WMS interactuan con los clientes mediante el protocolo HTTP, definiendo las posibles peticiones que le pueden llegar a realizar y por cada una de ellas los parámetros que admiten. La especificación WMS indica que al menos han de permitir los dos siguientes tipos de peticiones:
+  - **GetCapabilities** que retorna un XML con metadatos de la información del servidor WMS.
+  - **GetMap** que retorna una imágen del mapa de acuerdo a los parámetros indicados por el usuario.
+Existen otras opcionales:
+  - **GetFeatureInfo** retorna información sobre una feature en una posición.
+  - **DescribeLayer** que retorna la descripción de un XML de una o más capas de mapas.
+  - **GetLegendGraphic** que retorna la imágen de la leyenda.
 ===== Como... =====
@@ Línea 268: / Línea 132: @@
 ==== Descarga y uso de rasters ====
 ==== Software ====
@@ Línea 279: / Línea 145: @@
   * [[http://jump-pilot.sourceforge.net/|OpenJump]]
   * [[http://www.gvsig.gva.es/|gvSIG]]
-  * [[http://openmap.bbn.com/|OpenMap]], visor en Java muy sencillo.
+  * [[http://openmap.bbn.com/|OpenMap]], visor en Java.
+  * [[http://www.thecarbonproject.com/gaia.php|Gaia]], otro visor bastante sencillo pero efectivo.
 === Librerías ===
   * GDAL: Librería para la lectura\escritura de gráficos geoespaciales en C++.
   * [[http://openev.sourceforge.net/|OpenEV]], Grupo de herramientas para mostrar imágenes georeferenciadas (desarrollada en Python con GDAL y OpenGL).

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