Composite Several Layers of Data and Information Visualization (Sumber:Summarize Information between Grids, P.J. Ersts, American Museum of Natural History,

Presentasi berjudul: "Composite Several Layers of Data and Information Visualization (Sumber:Summarize Information between Grids, P.J. Ersts, American Museum of Natural History,"— Transcript presentasi:

1 Composite Several Layers of Data and Information Visualization (Sumber:Summarize Information between Grids, P.J. Ersts, American Museum of Natural History, Visualizing Megatransect Data, J.M. Fay, Megatransect leader, ArcUser,The Magazine for ESRI Software Users, October-December, 2001, pp. 36, 52-53) Faculty of Computer Science University of Indonesia Dr. Aniati Murni

2 Composite Several Layers of Data –Objective I : to find an area with a specific characteristic The suitability rule for residential area: peta arahan: penggunaan tanah untuk permukiman (ada penduduk yang membutuhkan perumahan) dan dekat jaringan jalan; dan peta hutan: daerah semak belukar dan bukan daerah hutan lindung; dan peta morfologi: tipe morfologi merupakan tanah datar; dan peta bencana alam: bebas erosi dan bebas banjir; dan peta sanitasi: ada tempat pembuangan sampah dan ada sumber air minum yang sehat; dan peta fasilitas umum: ada sekolah, rumah sakit, tempat kerja. dan diatas merupakan operator proses komposit.

3 Composite Several Layers of Data –Objective II : change analysis based on temporal data The process is done based on a method for summarizing several grids / layers using ArcView 3.3 Spatial Analysis. Change analysis is done to observe the health change of coral over time, with respect to depth, and the distance from the shore. The program can be written using Avenue script or using Summarize Zones function available in ArcView Spatial Analysis. Grids (see next slide) can be created using a basic algebraic equation and the Map Calculator facility of ArcView Spatial Analysis. Grid concept is related to the raster format of satellite digital image.

4 Summarization of Information between Grids

5 Analysis of land cover changes –Area to be observed cover 9 km from the rivers –The changes of land cover and land use are observed over time with respect to the elevation –Steps to solve the problem: Gather the temporal (the year of 1990 and 2000) data set of land cover (1=forest, 2=non-forest, 3=water, 4=cloud, 5=shadow) and land use Gather the elevation data set Create a new theme by buffering rivers to a distance of 9 km Rasterize all data using the same-size grids Make the data available for statistical analysis

6 Analysis of land cover changes (continuation) –Analyzing change over time: If for the year of 1999: a grid located in land cover layer has a value 1 (forest) and the same / corresponding grid for the year of 2000 has a value of 3 water) then the output grid has the value of 13 (meaning that forest has changed to water) Combining grids using this method generates cell values that demonstrate change over time. The area for each class can be obtained by multiplying the number of cells containing a specific value of the corresponding class code by the square of cell size) With this method we can regress combined data back to the original grids.

7 A formulae of the grid summarization Notation: Year Land Cover Grid: 1990LC and 2000LC have the value between 1 and 5; Elevation: E has the value between 0 and 3000; River Buffers: RB has the value between 1 and 9; and Grid Column and Row: X and Y have the value between 1 and the Image Resolutions. NewGrid(x,y) = (1990LC(x,y) * 10) + 2000LC(x,y) 4-Grid vectorization produce the following NewGrid(x,y): NewGrid(x,y) = (RB(x,y) * 1000000) + (E(x,y) * 100) + (1990LC(x,y) * 10) + 2000LC(x,y) We have used scale factors of 1000000 for RB, of 100 for E, of 10 for 1990LC and of 1 for 2000LC. Use the following data: 1990LC(100,100)=1, 2000LC(100,100)=3, E(100,100)=2000, RB(100,100)=2 then we got the NewGrid (100,100)=2200013. The value of NewGrid shows that the RB=2, E=2000, 1990LC=1 and 2000LC=3.

8 A new field can be created to the attribute table of the point theme. The new field containing the scale factors to obtain the value of the original supporting themes. The computation can be later done using the Map Calculator of ArcView functions. To export the data, the grid-theme file should be converted to a point-theme file, where in this file each record represents one grid cell (x,y, theme value). The conversion can be done by GRID2PT, an Avenue script. Avenue script is a script language provided by the ArcView. The GRID2PT is written by Jeff Ardron that is available from the ArcScript page of the ESRI Web Site. The point-theme file has a DBF format that can be exported to various GIS software packages such as MapInfo, ArcInfo... Preparing Data for Export

9 A formulae for obtaining the value of the original supporting theme which is done by Map Calculator function. RB = (NewGrid / 1000000) truncate E = ((NewGrid - (RB * 1000000)) / 100) truncate 1990LC=((NewGrid - (RB*1000000) - (E*100) / 10) truncate 2000LC=(NewGrid-(RB*1000000)-(E*100)-(1990LC*10)) Preparing Data for Export (continuation)

10 Megatransect merupakan expedisi yang dilakukan di daerah Central Afrika, yaitu di Odzala National Park. Megatransect project leadernya adalah J. Michael Fay. Data yang diperoleh terdiri dari data audio, videa, catatan / text, dan yang penting adalah data yang direkam dengan bantuan GPS, sehingga data dapat langsung dipetakan. Data yang diambil mulai dari flora, fauna, vegetation, man population density, elephant population density. Salah satu hasil pemetaan menggambarkan bahwa ada hubungan terbalik antara area dengan densitas populasi gajah dan area dengan densitas populasi manusia. Megatransect Data Visualization

11 Poligon yang berwarna putih menunjukkan Odzala National Park Garis hitam melintasi area Odzala National Park adalah rute ekspedisi Megatransect

12 Megatransect Data Visualization aa Densitas populasi gajah dinya- takan dengan warna merah. Garis putih merupakan rute Mega- transect