http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Learning GeoSpatial analysis with Python
an effective guide to geographic information system and remote sensing analysis using Python 3
An effective guide to geographic information systems and remote sensing analysis using Python 3About This Book Construct applications for GIS development by exploiting Python This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution systemno compiling of C libraries necessary This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is ForIf you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you. Familarity with digital mapping and analysis using Python or another scripting language for automation or crunching data manually is appreciatedWhat You Will Learn Automate Geospatial analysis workflows using Python Code the simplest possible GIS in 60 lines of Python Mold thematic maps with Python tools Get hold of the various forms that geospatial data comes in Produce elevation contours using Python tools Create flood inundation models Apply Geospatial analysis to find out about real-time data tracking and for storm chasingIn DetailGeospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors. Style and approach This is a practical, hands-on tutorial that teaches you all about Geospatial analysis interactively using Python.
Machine generated contents note: Geospatial analysis and our world -- Beyond disasters -- History of geospatial analysis -- Geographic information systems -- Remote sensing -- Elevation data -- Computer-aided drafting -- Geospatial analysis and computer programming -- Object-oriented programming for geospatial analysis -- Importance of geospatial analysis -- Geographic information system concepts -- Thematic maps -- Spatial databases -- Spatial indexing -- Metadata -- Map projections -- Rendering -- Remote sensing concepts -- Images as data -- Remote sensing and color -- Common vector GIS concepts -- Data structures -- Buffer -- Dissolve -- Generalize -- Intersection -- Merge -- Point in polygon -- Union -- Join -- Geospatial rules about polygons -- Common raster data concepts -- Band math -- Change detection -- Histogram -- Feature extraction -- Supervised classification -- Unsupervised classification -- Creating the simplest possible Python GIS -- Getting started with Python
Note continued: Building SimpleGIS -- Step by step -- Summary -- An overview of common data formats -- Data structures -- Common traits -- Geolocation -- Subject information -- Spatial indexing -- Indexing algorithms -- Quadtree index -- R-tree index -- Grids -- Overviews -- Metadata -- File structure -- Vector data -- Shapefiles -- CAD files -- Tag-based and markup-based formats -- GeoJSON -- Raster data -- TIFF files -- JPEG, GIF, BMP, and PNG -- Compressed formats -- ASCII Grids -- World files -- Point cloud data -- Web services -- Summary -- Data access -- GDAL -- OGR -- Computational geometry -- The PROJ. 4 projection library -- CGAL -- JTS -- GEOS -- PostGIS -- Other spatially-enabled databases -- Oracle spatial and graph -- ArcSDE -- Microsoft SQL Server -- MySQL -- SpatiaLite -- Routing -- Esri Network Analyst and Spatial Analyst -- pgRouting -- Desktop tools (including visualization) -- Quantum GIS -- OpenEV -- GRASS GIS -- uDig -- gySIG -- OpenJUMP
Note continued: Google Earth -- NASA World Wind -- ArcG IS -- Metadata management -- GeoNetwork -- CatMDEdit -- Summary -- Installing third-party Python modules -- Installing GDAL -- Windows -- Linux -- Mac OS X -- Python networking libraries for acquiring data -- The Python urllib module -- FTP -- ZIP and TAR files -- Python markup and tag-based parsers -- The minidom module -- ElementTree -- Building XML -- Well-known text (WKT) -- Python JSON libraries -- The json module -- The geojson module -- OGR -- PyShp -- dbfpy -- Shapely -- Fiona -- GDAL -- NumPy -- PIL -- PNGCanvas -- GeoPandas -- PyMySQL -- PyFPDF -- Spectral Python -- Summary -- Measuring distance -- Pythagorean theorem -- Haversine formula -- Vincenty's formula -- Calculating line direction -- Coordinate conversion -- Reprojection -- Editing shapefiles -- Accessing the shapefile -- Reading shapefile attributes -- Reading shapefile geometry -- Changing a shapefile -- Adding fields -- Merging shapefiles
Note continued: Merging shapefiles with dbfpy -- Splitting shapefiles -- Subsetting spatially -- Performing selections -- Point in polygon formula -- Bounding Box Selections -- Attribute selections -- Creating images for visualization -- Dot density calculations -- Choropleth maps -- Using spreadsheets -- Using GPS data -- Geocoding -- Summary -- Swapping image bands -- Creating histograms -- Performing a histogram stretch -- Clipping images -- Classifying images -- Extracting features from images -- Change detection -- Summary -- ASCII Grid files -- Reading grids -- Writing grids -- Creating a shaded relief -- Creating elevation contours -- Working with LIDAR -- Creating a grid from LIDAR -- Using PIL to visualize LIDAR -- Creating a triangulated irregular network -- Summary -- Creating a Normalized Difference Vegetative Index -- Setting up the framework -- Loading the data -- Rasterizing the shapefile -- Clipping the bands -- Using the NDVI formula
Note continued: Classifying the NDVI -- Additional functions -- Loading the NDVI -- Preparing the NDVI -- Creating classes -- Creating a flood inundation model -- The flood fill function -- Making a flood -- Creating a color hillshade -- Least cost path analysis -- Setting up the test grid -- The simple A* algorithm -- Generating the test path -- Viewing the test output -- The real-world example -- Loading the grid -- Defining the helper functions -- The real-world A* algorithm -- Generating a real-world path -- Routing along streets -- Geolocating photos -- Summary -- Tracking vehicles -- The NextBus agency list -- The NextBus route list -- NextBus vehicle locations -- Mapping NextBus locations -- Storm chasing -- Reports from the field -- Summary -- A typical GPS report -- Working with GPX-Reporter.py -- Stepping through the program -- The initial setup -- Working with utility functions -- Parsing the GPX -- Getting the bounding box
Note continued: Downloading map and elevation images -- Creating the hillshade -- Creating maps -- Measuring the elevation -- Measuring the distance -- Retrieving weather data -- Summary.
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