Free vector maps of Indonesia available in Adobe Illustrator, EPS, PDF, PNG and JPG formats to download. Download Data Lidar Indonesia Map. 5/31/2017 0 Comments Mass. GIS Data - Li. Vector data showing the outlines of major watersheds (river basins).

This data collection consists of Lidar Point Cloud (LPC) projects as provided to the USGS. These point cloud files contain all the original lidar points collected, with the original spatial reference and units preserved. These data may have been used as the source of updates to the National Elevation Dataset (NED), which serves as the elevation layer of the National Map. Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of geo-referenced x, y coordiniates and z (elevation), as well as other attributes for each point.

A few older projects in this collection are in ASCII format. Please refer to for additional information on the.LAS file format. Access & Use Information. Dates Metadata Date December 7, 2016 Metadata Created Date September 18, 2014 Metadata Updated Date August 25, 2017 Reference Date(s) January 1, 2016 (publication) Frequency Of Update asNeeded Metadata Source • ISO-19139 ISO-19139 Metadata • FGDC Original FGDC Metadata Harvested from Graphic Preview • • • • • • • • • • • • • • • • • • • • • • • • • • • Additional Metadata Resource Type Dataset Metadata Date December 7, 2016 Metadata Created Date September 18, 2014 Metadata Updated Date August 25, 2017 Reference Date(s) January 1, 2016 (publication) Responsible Party U.S. Geological Survey (Point of Contact) Contact Email. Access Constraints Use Constraints: There is no guarantee or warranty concerning the accuracy of the data.

Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Any user who modifies the data is obligated to describe the types of modifications they perform. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the USGS. Please refer to for the USGS disclaimer., Access Constraints: None.

Any downloading and use of these data signifies a user's agreement to comprehension and compliance of the USGS Standard Disclaimer. Ensure all portions of metadata are read and clearly understood before using these data in order to protect both user and USGS interests. Bbox East Long -60 Bbox North Lat 72 Bbox South Lat 13 Bbox West Long -180 Collection Metadata true Coupled Resource Frequency Of Update asNeeded Graphic Preview Description Thumbnail JPG image Graphic Preview File Graphic Preview Type JPEG Guid Harvest Object Id b6b87fa4-a707-4067-9bf0-e0f290fd0cf4 Harvest Source Id a2562cf8-3758-4dfd-bd42-746fc62be645 Harvest Source Title USGS Lidar Point Cloud LAS Harvest Source Licence Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty, expressed or implied, is made by either regarding the utility of the data on any system, nor shall the act of distribution constitute any such warranty. The USGS will warranty the delivery of this product in computer-readable format and will offer appropriate adjustment of credit when the product is determined unreadable by correctly adjusted computer peripherals, or when the physical medium is delivered in damaged condition. Requests for adjustments of credit must be made within 90 days from the date of this shipment from the ordering site.

Metadata Language Metadata Type geospatial Progress completed Spatial Data Service Type Spatial Reference System Spatial Harvester True Temporal Extent Begin 2004-01-01 Didn't find what you're looking for? Suggest a dataset.

Modelling the real world: Infinite number of ways to map things, people describe things differently based on viewpoints and experience, more closely we look at the world the more detailed it is, we can lose reality where is is hard to maintain accuracy - Ways of dealing with problem? -- Conceptualise the problem in a logical way: Identifiable things are classified on their descriptive attributes, classes are generalised, how things are organised- hierarchies, ID structured relationships and associations between things -- common conceptual representations are formalised as a data model. Positional accuracy- high or low- difference in measured geographic location of an entity from its true position. Absolute accuracy: accuracy with coordinate system; relative accuracy- positioning of geographic entities relative to each other - attribute accuracy- Accuracy of the non-spatial attributes of geographic features- what they are - logical consistency= concerned with determinig the faithfullness of the data structure for a data set. This involves spatial data inconsistencies such as incorrect line intersections, duplicate lines or boundaries or gaps in lines- spatial errors- editing tools correct these errors- snapping lines together - completeness- how complete the data set is. Includes consideration of holes in the data, unclassified areas, compilation procedures tay may have caused data to be eliminated- eg african data. Color symbolisation of data patterns reveals: Grouping - group data based on similar characteristics, e.g.

Close values for rental prices in Brisbane Differentiation - identify values that stand out from average or are unique, e.g. Social disadvantage in west Ipswich Arrangement - partition data with respect to ordering relationship (time or spatial), e.g. Medium suburb house prices - Mapped patterns are easy to see but require statistics to quantify averages, measure variation in values and significance of pattern; and identify how values are distributed (bell shaped or skewed). Equal Interval: Equal sized classes over a range. Emphasizes the fractional amount of an attribute value relative to range of values. (Suited to uniformly distributed data) - Quantile: Each class contains an equal number of features, give ranked grouping.

Counts the number of values and divides them into equal sized classes. (Suited to comparative ranks) - Natural Breaks: Breaks into groups by minimizing the variance within classes. Statistical techniques that gives a similarity grouping of similar values. - Standard Deviation: Computes standard deviation for the data.

Then sets the first class break at the mean value. Places subsequent class breaks are placed at the interval you specify above and below the mean until all data values are contained within the class boundaries. (Suited to normally distributed data). DBMS's ▫ Tools and services to store and manage database ▫ Example systems are Microsoft SQL Server, Oracle, PostgresSQL ▫ ArcGIS Server builds geodatabases on DBMS Relational data model is most popular type ▫ Developed in the 70's with well founded set-theory foundation ▫ Has limitations but still most prevalent model today ▫ Supports geo-relational database, attribute fields can be any type including geometry for points, polygons, etc. Relational tables required to have: ▫ Minimal (normalised) form (e.g. No repeating fields or redundant data) ▫ Represent a valid state of world (DBMS maintains this) ▫ Maintain a valid state representation of modelled application ▫ Unique data in each row (often designated primary key). Suppose you are modelling a parcel database.

You record the ID, address, owner, etc. But in cases there are joint owners. Autodata 2 12 Na Srpskom Beba. So a database may look like: -- Have an owner 2 column - Normalisation Rule 1: Eliminate repeating groups (in columns) - Normalise rule 2: remove redundant values in rows - Normalise rube 3: Eliminate non-dependent columns - What did we achieve? -- Unique primary keys (underlined fields) ▫ All other data is wholly dependant upon the primary key ▫ No data redundancy, consistent updates!

▫ BUT it means complex queries to answer questions. One-one An instance of an entity relates to a unique instance of other entity. Minimally 1 table required to represent data in DBMS One-Many: An instance of an entity relates to many instances of an other (and visa-versa for many-to-one) 2 tables required to represent data in DBMS Examples of owner-parcel relationships - Many-Many: Many instances of one entity relate to multiple instances of another - 3 tables required to represent data in DBMS, i.e. Link table is needed Examples of owner-parcel relationships Other examples: Many soil samples are taken at a property (m.1) A regional authority may cover many local councils and a local council may be involved in many regional authorities (m.n). Built on DBMS Minimally supports a data type for geometry, i.e. Point, line or polygon ▫ GIS is special application organised around concepts of geographical data and maps Dataset collection of feature classes with bounding extent and projection/datum Feature class is table with geometry attribute Feature is a row with geometry value Complex feature class may be constructed by relationship between feature classes (connected geometry is called topologically structured) i.e.

Utility water main with pipes (line) and valves (point) - Individual rows = individual feature. Based on same principle as 'attribute join', i.e. Selection based upon a condition, i.e. Objects intersect, overlap, etc. Example: Given rain monitoring gauge stations (stn's) in watersheds SQL query to find watersheds in floodplain areas. -- select station.id from station, watershed --where intersects (station. Geometry, watershed.geometry) and watershed.landform='floodplain' In ArcGIS this can be mimicked from user interface with the 'Interactive selection method', i.e.

Do 'select by location' and then from current selection 'select by attribute'. Two primary ways to model geographic phenomena ▫ Continuous surface (represented as raster) ▫ Discrete objects (represented as vector) ▫ Raster is most common logical representation for continuous field e.g. Because it has a simple uniform representation and is easy to work with! - What is advantage of raster analysis ▫ Ability to analyse continuous surface variable, i.e. Slopes, density, etc. ▫ Surprising flexibility to perform other spatial analysis on proximity, hydrological models, filtering local trends i.e. Hot spots, travel time to destination, etc.

Two inputs: 1. Zone raster: defines the zones locations and values, must be an integer type. Values 1,2,3 define zones --Features classes may also be used as zonal input.

Value raster: input values used in calculating the output for each zone - Result: Result raster: statistic by zone as raster and table output, i.e. Summation, average, etc. Note: Output tables allow normal table operations so you can add fields and do calculations. These can be used as input to further raster analysis.

Data Summary (Table operation that aggregates data) Data summary with tables ▫ Group on nominal or ordinal attribute and summarise on other attributes ▫ Methods for data summary: Nominal/ordinal - count, mode, variety, etc. Interval/ratio - statistical summary for sum, average, minimum, variation - Table summary is query that selects a column (category) and summarises other columns: ▫ Numerical: Sum, Average Standard deviation, Minimum, etc. ▫ Categorical (not all avail. In ArcGIS): Count, First, Majority, Mode As SQL use the GROUP BY clause, and sum data.

Vector feature analysis follows set theoretic queries for subset, aggregate and disaggregate operations. ▫ The common element in spatial aggregation/disaggregation is space This type of analysis is common is GIS ▫ It integrates spatial data from different sources ▫ Analysis involves overlay and query/summary Some cautions for using spatial analysis ▫ Be careful with integrating different measure types and scales, i.e.

Follow common sense rules for data analysis ▫ Limitations to analysis with generalised spatial data, i.e. Be aware of ecological fallacy. Traditionally vertical aerial photography with stereo-image model from overlapping photos and fitted to ground control to digitize x,y,z More flexibility today with digital cameras and positioning (GPS and inertial measurements, and just some ground control) Data captured by fixed frame camera or continuous strip scanner (latter must be processed by supplier) 3D models created by correlating picture elements in overlapping vertical images Also oblique imagery (i.e. At 45°) is popular for multiview or continuous perspective viewing (see Pictometry). More precise radiometric resolution for more sensitive sensor detectors Sensors sample different wavelength ()intervals to detect different parts of the electromagnetic spectra and depending on the strength (amount) of returned energy, i.e. Need larger -interval for thermal vs visible Radiance varies for different wavelengths and due to atmospheric absorption One wavelength interval is captured as one image band More bands means higher spectral coverage Finer the wavelength interval for band the higher the spectral resolution -e.g. Hyperspectral measures very narrow wavelength intervals.