Lecture-10_Bb_1SlidePerPage

# Lecture-10_Bb_1SlidePerPage - Spatial Analysis IV Spatial...

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Unformatted text preview: Spatial Analysis IV Spatial Analysis IV Optimization Optimization Analysis of patterns to create improved ‘_________’ You’ll recall that GIS applications that focus upon design are described as uses uses. • As opposed to those that advance science which are uses termed as Methods that derive practical solutions to that derive practical solutions to everyday problems These methods are often implemented as part of as part of a (SDSS) (SDSS) Enables GIS to provide instant feedback GIS to provide instant feedback upon ‘what if’ scenarios. Optimization: Area Suitability Area Suitability Determine which areas are best suited to certain by taking into consideration many relevant . • e.g. The best potential locations for the Th th construction of a park Many different methods have been different methods have been developed to evaluate site suitability • We’ll look at a few examples… Optimization: Area Suitability Boolean Overlay Based Analysis Operation is via polygon overlay, which is a Mapping) process Boolean overlay (or The major steps: major steps: • Data Preprocessing – Collect datasets that representing the individual requirements • Suitability Ranking – The suitability of each dataset is determined using logic (0/1, or Yes/NO) – e.g. only public owned land is suitable when looking at ownership ownership • Overlay – The of the suitable areas from each dataset will be the areas that all the pertinent requirements pertinent requirements Optimization: Area Suitability Simple example: Problem statement: select a site for a new park statement: select site for new park Criteria: on public land and current landuse type being A Data layers: ownership, landuses A Owner X Owner Y Public B Optimization: Area Suitability Find suitable areas solely based on ownership Owner X Unsuitable Owner Y Public Suitable Find suitable areas solely based on landuse type Unsuitable A B Suitable Optimization: Area Suitability Overlay The intersection of the suitable areas from each dataset will be the areas that satisfy all the pertinent requirements the areas that satisfy all the pertinent requirements The major drawback is that the boolean logic (i.e. a site is either suitable or its not) produces abrupt it it th that don’t reflect the nature of many controlling factors Optimization: Area Suitability Weighted Overlay If the parameters under consideration are nature nature… in • precipitation, temperature, elevation, distance etc. …then the overlay of will, generally speaking, be insufficient insufficient. objects • The overlay of continuous data requires continuous datasets – Raster; Spatial Analyst! Optimization: Area Suitability Conversion Spatial Analyst permits the conversion of Discrete Data into Continuous Data This is useful if you’re attempting to BOTH data types types Optimization: Area Suitability Weighted Overlay (Cont.) Indexing: • The datasets that are evaluated may be quite : – – – – Land Cost (\$) Proximity to services (miles) Slope (degrees) (degrees) Etc. • Overlaying these factors in their will not produce a meaningful result! ill • Thus an ordinal applied to each of the datasets form is – For example, on an Index of 1-10, 1 would be the _______ suitable and 10 would be the __________! Optimization: Area Suitability Weighted Overlay (Cont.) Weighting: • Some criteria are more important than others. • This order of importance order of importance may be implemented by your Indexed datasets. • e.g. 2 datasets upon a scale of of 1-3 are are by by a % of influence, then to create an output: Top Left Cell (2*0.75)+(3*0.25)=2.25 Optimization: Area Suitability Weighted Overlay (Cont.) (Cont.) Example: • Identify potential locations for a new school Optimization: Routing Problems Routing Problems Ensures that the path of a vehicle is the most one possible! • Especially important for service vehicles, delivery trucks etc. whose routes differ from day to day day to day. Constrained to a given • Discrete rather than continuous rather than continuous . Optimization: Routing Problems Routing Problems (Cont.) Based upon the • Distance or Time • The links within the network will have links within the network will have attributes that will allow the GIS to make an intelligent decision: – Length, Speed Limit, Traffic Lights, etc. Optimization: Routing Problems Routing Problems (Cont.) The traveling salesman problem • Find the shortest tour from an , through a set of , and back to the . • The potential number of tours will grow with each additional destination with each additional destination. – Eventually, it will become untenable to evaluate all the potential options. • Thus, GIS are designed to employ GIS ______ – Algorithms designed to work , and to come to providing the best answer, without guaranteeing that the best solution will be found. Optimization: Routing Problems Routing service technicians for Schindler Elevator Schindler Elevator GIS GIS is used to partition the day’s workload among workload among the the crews and trucks (color coding) and to optimize optimize the route to minimize time and cost. ...
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