# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 4 Discussion  
Under Cluster Analysis, the evaluation of crop residue is based on the yearly production rate (secondary data collected from 43 townships in the Yangon Region, Myanmar). Residue to production ratio RPR and crop production are used to calculate the generation of crop residues. In this study, the production value is collected from the data for 2019 of the general administrative office, which is on the Myanmar Information Management Unit website. Moreover, RPR values are gathered from different countries and different pieces of literature. The disadvantage of this method using RPR values is that different crops may have varied RPR values depending on weather variations, crop types, water availability, soil quality, and farming methods [38]. Therefore, estimating the number of residues using an RPR may result in inaccurate estimates. Thus, the more accurate generation calculation should also depend on the more reliable statistical production data and precise testing results RPR might improve.  
Another way to calculate the crop residue which uses crop production is based on the cropped area and is frequently used for woody residues from perennial crops [38]. This approach assumes that the amount of crop and residue harvested from a cropping area can vary significantly depending on the management style (traditional or advanced) and crop variety. The remote sensing method using satellite images is also an efficient  
Figure 10: Township-based Clusters and Locations of Treatment Facilities.  
and cost-effective way to obtain information concerning crop residue and crop condition cover [39].  
In GIS cluster analysis, there are four steps in terms of Spatial Auto Correlation Morans I; Calculate Distance Band for Neighbor Count; Incremental Spatial Autocorrelations, and Cluster and Outlier Analysis (Anselin Local Moran I) in this study. However, instead of action four of Cluster and Outlier Analysis (Anselin Local Moran I), Hot Spot Analysis (Getis-Ord Gi*) can also be used to identify the extraordinary and hot spots.  
In GIS suitability analysis, the constraint variables and the factor variables are modeled. When determining whether the land has the potential to meet the criteria, there is no set standard for the assessment of conditions and factors or other considerations. The constraints depend not only on the regulations but also on the sustainability consideration. In this study, evaluating the weight of factors is the AHP method. For factors, the different multicriteria decision-making (MCDM) can be used to solve site selection problems of weighting in terms of preference ranking organization method for enrichment evaluation (PROMETHEE), ELimination Et Choirx Traduisant la REalite (ELECTRE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and others.  
GIS cluster and suitability analysis need vector spatial data that can be classified from a satellite image or related department. In this study, land use and land cover data are mainly from the Environmental Systems Research Institute (Esri) 2020 land cover, which has an accuracy of 87 percent. The other data are downloaded from the Myanmar Information Management Unit.  
In this study, The GIS cluster analysis can identify the rice straw as the promising feedstock among crop residues. Furthermore, the township-based clusters are helpful for strategic planning and the suitable location of treatment facilities. Therefore, connecting the cluster of biomass waste with viable facilities should be studied in the future.