Species Distribution Mapping Habitat Suitability

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4.3. Discussion

The discussion of SUITSTAT functionality assessment is given in this section. A brief comment is given also to javan gibbon survey and habitat suitability analysis result.

4.3.1. System Performance

The outline of discussion on system performance is similar with the previous section system testing comprises of species distribution mapping and geoprocessing.

4.3.1.1. Species Distribution Mapping

The system embedded the ancillary data as attribute information the data beside position data item inputted in the table while processing. The ancillary data was stored into the attribute table of the distribution shape file data. Figure 43 shows the concerned attribute information. Figure 43. Attribute Information of Species Distribution Dataset The system could only created point using methodological valid data. The system would not be able to create point from data that came from triangle count method where its imaginary lines were not intersected. In that condition, it will show an error message such as shown in the Figure 44. Figure 44. Error Message in Point Creation Some features which potential to be developed in the future are related with attribute information. Next development should consider that user could flexibelly insert and chose what information is needed to be embedded in the attribute table. 69

4.3.1.2. Geoprocessing

Each of functionality in geoprocessing group is described in the following text:

4.3.1.2.1. Vector-based Grid Data Preparation

The transformation from vector to grid format is always suffered by the imprecision of grid representation. In very simple form, the accuracy value of flat surface polygon shown in the Figure 30 transformation into the vector-based grid form the vector grid is given by the ratio of the sum of all grid areas 132783443.00 m 2 and polygon area 132795516.19 m 2 , which gave value 0.99991 or almost 100. The accuracy is reasonably large because the edge of polygon is represented by partial grids, as shown in the Figure 45. The partial grid was possibly produced since the grid is actually a polygon as well vector type data. Figure 45. The Partial Grid Created in the Edge of Concerned Polygon Vector-based grid data model certainly has some advantage and disadvantage. Vector-based grid is actually vector data which composed almost by rectangular shape and it has attribute that contains value represent spatial properties. Hence, it inherits the advantage and disadvantage of both vector and raster data type. This issue is briefly provided in the Table 8 below. 70 Table 8. The Advantage and Disadvantage of Vector-based Data Model ADVANTAGE • Mathematical modeling is easy because all entities have a regular shape. • Retrieval, updating, and generalization of graphics and attributes are possible. • Conducting topological operation is possible. DISADVANTAGE • Large data volumes grid size-dependent • The speed of processing is slower than raster data model. The result of embedding process could be evaluated through examining the attribute of output file. The output file should have several fields containing value from chosen spatial analysis computation. Figure below shows the attribute of output file containing expected fields and values. Figure 46. Attribute Table of the Output File from Embedding Process

4.3.1.2.2. Buffering Point

This utility gave expected result to both buffer types rectangular and circular, means that it can be used for further processing, i.e. spatial properties examination. However, there is a different in processing time of spatial properties examination between both types, which circular type took much time longer than the other one such as neighbor analysis. This is happened because of every circular polygon of the output has many vertices as illustrated by Figure 47. 71 Figure 47. The Vertices of Circular Polygon Produced from Buffering Utility

4.3.1.2.3. Dissolve

The first trial of dissolve utility with the certain dataset the dataset that shown in the Figure 31, the output was not satisfying as expected. The output contained remaining lines inside it as shown in the Figure 48. This problem led a refinement in dissolve algorithm. Figure 48. Imperfect Output of Ordinary Dissolve Utility The refinement was built after examining the structure of polygon object of the output dataset. Polygon object was represented in MapObjects as a collection of points in its parts object ESRI, 1996 as illustrated by Figure 49. Normally, one polygon will only have one part that consists of many points. If the points forming a closed polygon and they was set up to a polygon object, the property “area” of the polygon must have an unsigned value positive value or more than 72 zero. Therefore, the solution was emerged when those remaining lines was observed stored as the separated part object. This part was removed when it gave a signed negative value after assigning it as a polygon object. Figure 49. Polygon Object Structure Current dissolve utility still needs refinement. When landcover data at study area was used, it produced imperfect flat surface polygon. Based on the examination to the dissolve output dataset, there are two kinds of errors were found, i.e.: remaining lines and multiparts polygon shown in the Figure 50. Different with the previous case, the remaining lines was not stored as separated parts object, but stored in its points collection. In the other hand, multiparts polygon was represented as a separated parts object. When point collection of this part was assigned as a polygon, its “area” property had a signed value. Unfortunately, removing this part made the expected output polygon dataset was substracted. Polygon object Parts object Point 1 Point 2 Point ... Point n 1 2 3 n 73 Figure 50. The Imperfect Solution of SUITSTAT’s Dissolve Utility These problems show that MapObjects union function is not provided by simple topological error detection algorithm such like “clean” operation in ArcInfo either in separated function. Even the data might have an error since digitation process, it will be useful if SUITSTAT application was provided by such algorithm, since this kind of error is unseen by the user sometimes. Refinement of SUITSTAT’s dissolve utility is needed then. Additionally, this fact could encourage other developers to develop additional function or algorithm to detect such error when similar operations such as dissolve and union function want to be built.

4.3.1.3. Habitat Suitability

In the first time of computation, the habitat suitability score processing following SAW procedure took much time because it involves ecogeographical data extraction. However, once the output data set was created, the process of score could be much faster, because the spatial properties were already already embedded into output dataset. There is slight different on how to make score classification between SUITSTAT and ArcView. In ArcView graduated color is similar with linear classification of this system. ArcView uses actual value in the score field as class breakers, meanwhile SUITSTAT uses open class breaker value based on the incremental division from minimum to maximum score value. 74 The advantage of habitat suitability utility is that could be used for another species, especially for species which has an established exploitation area such as territorial mammal and birds. It could be possible since the system is able to process and conduct spatial properties analysis to any data including point, line, and polygon data types. The shape of sampling units used for modeling javan gibbon habitat suitability is quadrat type. The quadrats were obtained from buffering distribution data where each quadrat has an average area of species home range. It could be simplifying the factual home range the model or representation is not close to the real situation. However, SUITSTAT could read spatial properties of sampling units that has irregular shape polygon. The use of relational database management system RDBMS for future development to the current SUITSTAT system is advantageuos. Utilizing RDBMS make possible to develop sub-system with its own interface to handle data extraction, editing, repository, and retrieval efficiently. It is possible to develop such sub-system by using similar the current utilized softwares for developing SUITSTAT. SUITSTAT system is using a single algorithm to calculate weight PCA and score SAW method, instead of various algorithms or model have been developed by various authors for predicting species distribution or modeling habitat suitability. Some of these algorithms are explicitly described right into implementation and some are not. Consider that every model has limitation, the integration various algorithm or model into a single system is soundly useful. The problem is to solve the standardization of data model which can be used for all algorithms or the development of such system which could read many data model andor formats. All data inputs used in habitat suitability utility of SUITSTAT is vector based data. It will be more useful if it is completed by tools that enable SUITSTAT to read raster data formats and integrates them with the utility. 75

4.3.2. Javan Gibbon Distribution and Habitat Suitability