Agricultural Feasibility Analysis in China: A GIS-based Spatial Fuzzy Multi-Criteria Decision Making Approach Presenter: Fei Carnes Date: July 17,

Glossary 1. Raster A raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as temperature. Rasters are digital aerial photographs, imagery from satellites, digital pictures, or even scanned maps. cell The entire area is divided into a uniform matrix of cells which are organized into a regular grid. All space is represented by cells, even where there is nothing of interest Rows and columns are used to designate their location. All cells must have a value. The number inside a cell represents some value for that cell location. The cell value may be an ID for a feature or it may be an attribute value. There is only one value for each cell. Cells are independent data units. The computer does not know if they are connected or not, but knows their relative position. Each cell has its size and area.

vs. Vector 2. Raster vs. Vector Raster allows to illustrate gradual changes and variation in attributes from one place to another. Raster has a simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table. Raster allows to perform fast overlays with complex datasets Raster is better in advanced spatial and statistical analysis.

3. Spatial Modeling (Raster modeling) Suitablity Analysis Hydrologic Modeling Distance Modeling...

3. Spatial Modeling Suitablity Analysis ( feasiblity \ vulnerability \ sensitivity analysis) Suitablity Analysis ( feasiblity \ vulnerability \ sensitivity analysis) Hydrologic Modeling Distance Modeling... Where are the optimum locations for a new school, landfill? Calculate optimal site locations by identifying possible influential factors. The optimal suitability map may provide new insight into the ideal areas where a new site should be located. To solve...

3. Spatial Modeling Suitablity Analysis HydrologicModeling Hydrologic Modeling Distance Modeling... Provide methods for describing the hydrologic characteristics of a surface. Using an elevation raster data set as input, it is possible to model where water will flow, create watersheds and stream networks, and derive other hydrologic characteristics. Where will the water flow to?To solve...

3. Spatial Modeling Suitablity Analysis Hydrologic Modeling Distance Modeling Distance Modeling... Where will be the areas which has the nearest distance from a emergency helicopter? To solve... Determine the least expensive method for a new road, flight pattern, shipping route, or any factor that is affected by time and cost.

4. Fuzzy Different approaches are used with continuous (quantitative) and categorical (qualitative) data Different functions available (linear, sigmoidal, J-shaped, user-defined) How to standarize? A method to standardized factors based on a series of specific mathematical functions. It reclassifies or transforms the input data to standardized scale ([0,1], [0,10],[0, 255], etc.).

5. Multi-Criteria Decision Making (MCDM) It considers multiple criteria in decision making environment. It provides a framework to represent the decision groups into a single model. GIS-based MCDM integrates the MCDM approach and GIS techniques to solve spatial issues. It has been received considerable attentions among planners since 1990s. This method has been shown in studies related to site determination for a nuclear waste facility, forest conservation.

Objective 1. The main aim of this project is to solve data confidentiality issue. 3. Help people understand raster GIS analysis (spatial modeling). 2. Develop multi-criteria decision making technique using fuzzy approach for agricultural feasibility analysis.

Data & Software Annual Precipitation Accumulated Temperature >10 °C Sunshine Hours Water Resources Elevation Soil PH Soil Depth Soil Drainage Source: Yu Deng, China Academy of Science Source: CGA Weather Hydrology Topography Soil Data ： Software:

IDRISI is a GIS and image processing software, developed by Clark Labs, Clark University. In 1993, IDRISI introduced the first instance of Multi- Criteria and Multi-Objective decision making tools in GIS. Eighteen years later, IDRISI is still the industry leader, responsible for: The first implementation of the Ordered-Weighted Average for multi-criteria evaluation that allows one to balance the relative amount of tradeoff between criteria with decision risk in balancing discordant information. The first implementation of the MOLA heuristic for multi- objective land allocation. The first GIS software implementation of Saatys Analytical Hierarchy Process (AHP). The first GIS software implementation of Saatys Analytical Hierarchy Process (AHP). ArcGIS is a platform for designing and managing solutions through the application of geographic knowledge.

Methodology 1. Data Determination and Processing 2. Criteria Standardization (Fuzzy) 3. Weight Determination 4. Weighted Linear Combination (weighted overlay)

Methodology 1. Data Processing 1) Denoise and reclassify imageries 3) Make sure all the data have the similar extents, and the same coordinate system. 2) Data transformation. e.g. river (shapefile)  distance to river (raster); elevation  slope(degree) Annual Precipitation Accumulated Temperature >10 °C Elevation Slope Distance to River Sunshine HourSoil DepthSoil PHSoil Drainage ……

Methodology 2. Fuzzy Fuzzy evaluates the possibility that each pixel belongs to a fuzzy set by evaluating any of a series of fuzzy set membership functions. --- Idrisi Selva Help Document Fuzzy membership function Annual Precipitation （ ml ） Fuzzy Annual Precipitation [0,1] “0” is assigned to those locations that are definitely not a member of the specified set, “1” is assigned to those values that are definitely a member of the specified set. All the in-between values receive some membership values based on the function.

Methodology 2.1 Fuzzy (for continues\ quantitative data) In Idrisi: “ FUZZY” module provides 4 fuzzy membership function types * Sigmoidal (“ S-Shape”) * J-Shaped* Linear* User-defined a = membership rises above 0; b = membership becomes 1; c = membership falls below 1; d = membership becomes 0 Monotonically increasing Monotonically decreasing Symmetric Control Points:. Control points

Methodology 2.1 Fuzzy (continues data) Annual Precipitation Accumulated Temperature>10 °C ElevationSlope Distance to River Sunshine Hour Sigmoidal increasing ml Sigmoidal Symmetric m Linear 0 7 Sigmoidal decreasing 0 max Linear 0 max Linear Fuzzy Precipitation Fuzzy Temperature Fuzzy Elevation Fuzzy Slope Fuzzy Distance to River Fuzzy Sunshine hour

Methodology 2.2 Fuzzy (for categorical \ qualitiative data) Reclassify and assign new values to each category. For example, land use types. 0 1 Deciduous forest Coniferous forest Cropland

Methodology 2. 2 Fuzzy (categorical data) Soil PH Soil Depth Soil Drainage Fuzzy Soil PH Fuzzy Soil Depth Fuzzy Soil Drainage 8.5 Other [4.5, 5.5) or [7.2,8.5) [5.5, 5.8) or [6.9,7.2)0.8 [5.8, 6.9) 1 Old Values New Values Shallow (10-50cm) Very shallow (<10cm) Moderately deep (50-100cm) Deep ( cm)0.8 Very deep ( cm) 1 Old Values New Values Well Low Old Values New Values … …

Methodology 2. Fuzzy Fuzzy Soil PH Fuzzy Soil Depth Fuzzy Soil Drainage Fuzzy Precipitation Fuzzy Temperature Fuzzy Elevation Fuzzy Slope Fuzzy Distance to River Fuzzy Sunshine hour Feasibility Map

Methodology 3. Weight Determine Determine the weight intuitively BUT it requires looking at all criteria together, this will not allow for negotiation or compromise looking at criteria two at a time. --- How important is each factor? --- We can give different weights to different factors, and all the weights must add up to 1 It lets you compare criteria two at a time. The user specifies the relative importance of one criteria compared to another and does this for all possible combinations of criteria. The procedure will then tell you how consistent are all of your comparisons and it will develop weights for you for each criteria.  Analytic Hierarchy Process (AHP)

Methodology 3. Weight Determine  Analytic Hierarchy Process (AHP) Intensity of Importance DefinitionExplanation 1 Equal importanceTwo activities contribute equally to the objective 3 Moderate importanceExperience and judgments slightly favor one activity over another 5 Strong importanceExperience and judgment strongly favor one activity over another 7 Very strong or demonstrated importance An activity is favored very strongly over another and dominance is demonstrated in practice 9 Extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation 2，4，6，82，4，6，8 Intermediate value between the two adjacent judgmentsWhen compromise is needed Steps: 1. Estimate the pertinent data 2. Create pairwise comparison decision matrix 3. Calculate the weights and check consistency （ CR<0.1) Table1. The fundamental scale

Methodology 3. Weight Determine * Klaus D. Goepel, Singapore * Idrisi --- “Weight” module * …… * By hand

Methodology 3. Weight Determine Ratio Rank 1 Weather accumulated temperature sunshine annual precipitation Hydrology distance to river1 4 3 Topography elevation slope Soil 0.15 Texture PH Depth Drainage0.65 * Klaus D. Goepel, Singapore

4. Weighted Overlay Methodology ai: pixcel value of factor i ； bi: weight of pixel i ； n: numbers of factors S = apply weights to several inputs and combine them into a single output = Factor 1 Factor 2 ( Weight = 0.75) ( Weight = 0.25) Output  Output (top left cell = 2.4) = 2.2* * 0.25

4. Weighted Overlay Methodology * 0.46 * 0.07 * Wn …… feasibility map WeatherHydrology

Overview * W1 * W2 * Wn …… Suitability ratings from different hierarchy …… *W’1 Criteria layer Standardize criteria Final feasibility map *W’n

Result

Summary There are a variety of possible answers to one suitability problem. Different answers for the same problem result from: – Considering criteria to be a factor or constraint – How the factor is standardized (what function, what thresholds) – How each factor is weighted

Limitation 1. Different fuzzy functions apply to different crop production areas. 2. Not considering seasonal influence. 3. Lack of data, such as soil texture.

Thank You ! Questions and Comments?