Link to download the Barangay boundaries shapefile is given below.ĭownload Philippines Barangay Boundaries Shapefile It has various administrative levels with Barangay being the smallest administrative division. Philippines is a Southeast Asian country with Manila as a capital. You need to login for downloading the shapefile.ĭownload Philippines National Outline Boundary Shapefile Download Philippines Barangay and Municipalities Shapefile.All data available are in GCS datum EPSG:4326 WGS84 CRS (Coordinate Reference System).You can also download these data in KML, GeoJSON or CSV formats. Here in this post you can download Philippines based polygon shapefiles of various administrative levels – Barangay divisions, Municipalities, Province and Philippines national outline boundary. As the feature in the cell becomes more dominantly urban, the cell is attributed the value for developed land, hence the pink shading.ĭata analysis such as extracting slope and aspect from Digital Elevation Models occurs with raster datasets.Shapefile is the most commonly used GIS vector format used for spatial analysis. Other features such as developed land, water or other vegetation types may be present on the ground in that area. This means that the dominate feature in that cell area was chamise vegetation. In the image above the dark green cell represents chamise vegetation. The stairstepping look comes from the transition of the cells from one value to another. Unlike vector data, raster data is formed by each cell receiving the value of the feature that dominates the cell. The name derives from the image of exactly that, the square cells along the borders of different value types look like a staircase viewed from the side. What results from the effect of converting spatial data location information into a cell based raster format is called stairstepping. The vegetation data was derived from NDVI classification of a satellite image. Raster data showing vegetation classification. Continuous data examples are temperature and elevation measurements. There are also three types of raster datasets: thematic data, spectral data, and pictures (imagery). An example of discrete raster data is population density. Raster data is cell-based and this data category also includes aerial and satellite imagery. There are two types of raster data: continuous and discrete. Raster data (also known as grid data) represents the fourth type of feature: surfaces. If a higher degree of spatial resolution is needed, a street curbwidth file would be used to show the width of the road as well as any features such as medians and right-of-ways (or sidewalks). Line features of a street centerline file only represent the physical location of the street.
They help reduce clutter by simplifying data locations.Īs the features are zoomed in to, the point location of a school is more realistically represented by a series of building footprints showing the physical location of the campus. Map made with Natural Earth Data.īoth line and point feature data represent polygon data at a much smaller scale. With maps presented at a larger scale, city locations are represented as a polygon to show the extent of each city. In the example below roads are distinguished from the stream network by designating the roads as a solid black line and the hydrology a dashed blue line. Symbology most commonly used to distinguish arc features from one another are line types (solid lines versus dashed lines) and combinations using colors and line thicknesses. Line features have a starting and ending point. Common examples would be road centerlines and hydrology. Line features only have one dimension and therefore can only be used to measure length. Common examples would be rivers, trails, and streets. Line (or arc) data is used to represent linear features.
Map: Caitlin Dempsey using Natural Earth Data. In GIS, point data can be used to show the geographic location of cities. For instance, point locations could represent city locations or place names. Point features are also used to represent abstract points. Examples would be schools, points of interest, and in the example below, bridge and culvert locations. Point data is most commonly used to represent nonadjacent features and to represent discrete data points. Points have zero dimensions, therefore you can measure neither length or area with this dataset.