Mata Kuliah di S2 PJ: 1. SIG 1: Basisdata dan IDS 2. Analisis Spasial 3. Pemodelan Spasial. Mata Kuliah Pemodelan Spasial: SKKNI BIDANG IG

Mata Kuliah di S2 PJ: 1. SIG 1: Basisdata dan IDS 2. Analisis Spasial 3. Pemodelan Spasial Mata Kuliah Pemodelan Spasial: S1 Fak. Geografi UGM SIG 2 (Lanjut) S2 PJ Fak. Geografi UGM SIG 2: Pemodelan Spasial S2 MTG UPN Analisis Spasial : SIG

North Carolina State University Geospatial Analysis and Modeling

SKKNI BIDANG IG

GIS 1: Fundamentals of GIS, data structures and operations

GIS 2: GIS modeling, raster based approaches, concepts and techniques of modeling for complex spatial problems GIS 3: GIS programming, developing and implementing new functionality and methods for GIS and spatial modeling

(Sumber: http://www.colorado.edu/geography/class_homepages/geog_4203_s08/class2_spatialAnMod.pdf)

Description: Using GIS to create descriptive models of the world --representations of reality as it exists. Analysis: Using GIS to answer a question or test an hypothesis. Often involves creating a new conceptual output layer, (or table or chart), the values of which are some transformation of the values in the descriptive input layer. --e.g. buffer or slope or aspect layers Prediction: Using GIS capabilities to create a predictive model of a real world process, that is, a model capable of reproducing processes and/or making predictions or projections as to how the world might appear. --e.g. flood models, fire spread models, urban growth models

The GIS Levels at Geog … GIS 1: Fundamentals of GIS, data structures and operations GIS 2: GIS modeling, raster based approaches, concepts and techniques of modeling for complex spatial problems GIS 3: GIS programming, developing and implementing new functionality and methods for GIS and spatial modeling http://www.colorado.edu/geography/class_homepages/geog_4203_s08/

BEDA: 1.SIG 1: Basisdata dan IDS 2.Analisis Spasial 3.Pemodelan Spasial

1. SIG 1: Basisdata dan IDS

Spatial Data Infrastructure (SDI) Technology, policies, criteria, standards and people necessary to promote geospatial data sharing throughout all levels of government, the private and nonprofit sectors, and academia. It provides a base or structure of practices and relationships among data producers and users that facilitates data sharing and use. It is a set of actions and new ways of accessing, sharing and using geographic data that enables far more comprehensive analysis of data to help decision-makers chose the best course(s) of action. (Federal Geographic Data Committee (FGDC) http://www.fgdc.gov/nsdi/nsdi.html, accessed July 12, 2006) Geographic Information Systems (GIS) Information systems that manage, manipulate and analyze spatial data. (Theobald, 2005, p. 2). Generic GIS can be viewed as a number of specialized spatial routines laid over a standard relational data base management system. (Goodchild, 1985) (Current GIS increasingly rely on object oriented technology.)

Zorica Nedović-Budić and Nama Raj Budhathoki

2. Analisis Spasial

GIS Functions • Data Capture

– The input of data into a GIS can be achieved through many different methods of gathering. For example, aerial photography, scanning, digitizing, GPS or global positioning system is just a few of the ways a GIS user could obtain data.

• Data Storage – Some data is stored such as a map in a drawer, while others, such as digital data, can be as a hardcopy, stored on CD or on your hard drive.

• Data Manipulation – The digital geographical data can be edited, this allows for many attribute to be added, edited, or deleted to the specification of the project.

• Query And Analysis – GIS was used widely in decision making process for the new commission districts. We use population data to help establish an equal representation of population to area for each district.

• Visualization – This represents the ability to display your data, your maps, and information.

Spatial analysis is how we understand our world—mapping where things are, how they relate, what it all means, and what actions to take. From computational analysis of geographic patterns to finding optimum routes, site selection, and advanced predictive modeling, spatial analysis is at the very heart of geographic information system (GIS) technology. www.esri.com/products/arcgis-capabilities/spatial-analysis

Spatial analysis The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Spatial analysis extracts or creates new information from spatial data. GIS Dictionary, ESRI

Spatial analysis The true value of GIS lies in their ability to analyze spatial data using the techniques of spatial analysis. Spatial analysis provides the valueadded products from existing datasets” Goodchild, 1988

Spatial analysis is a set of techniques for analyzing spatial data. Goodchild (et al.) 1987/1992

GIS or Spatial analysis: application of operations or functions to spatial data to add value, support decisions, and reveal patterns. Geoprocessing (according to ESRI): GIS operation in which new data is derived from existing data. http://news.uk.msn.com/monks-protest-in-burma.aspx

Spatial analysis: Way in which we turn raw data into useful information – A set of techniques whose results are dependent on the locations of the objects being analyzed – Variety of methods – Powerful computers – Intelligent users Christine Erlien

More about spatial analysis… • Some methods are highly mathematical. • All effective spatial analysis requires an intelligent user, not just a powerful computer. • “Spatial analysis is best seen as a collaboration between the computer and the human, in which both play vital roles.” (Geographic Information Systems and Science, Wiley, 2001)

• Spatial analysis the crux of GIS because it includes all of the transformations, manipulations, and methods that can be applied to geographic data to add value to them, to support decisions, and to reveal patterns and anomalies that are not immediately obvious o Spatial analysis is the process by which we turn raw data into useful information, Examples: John Snow map of cholera

http://dusk.geo.orst.edu/gis/465lec.html

• Spatial analysis is the means of adding value to geographic data. • It turns data into information • Spatial analysis can reveal things that might otherwise be invisible. It can make what is implicit explicit.

http://dusk.geo.orst.edu/gis/465lec.html

Pada tahun 1854, Dr. John Snow menghadapi permasalahan bencana kolera yang terjadi di distrik Soho, London. Secara teori ada 2 kemungkinan penyebab penularan penyakit kolera disana, yaitu: 1. yang paling populer masyarakat disana percaya bahwa kolera disebabkan kontaminasi udara kotor dari areal bekas pekuburan kuno di pusat kota. 2. pendapat Dr. John Snow yang memperhatikan kemungkinan pemakaian air dari sumur-sumur yang ada di kota tersebut.

Dr John Snow (Source: John Snow, Inc. www.jsi.com)

A modern replica of the pump that led Snow to the inference that drinking water transmitted cholera, located in what is now Broadwick Street in Soho, London (Source: John Snow Inc. www.jsi.com)

Kemudian Dr. John Snow menarik garis-garis hubungan antara korban dengan kedekatan ke lokasi pekuburan dan sumur.

Akhirnya, terungkap di atas peta sebuah pola yang sangat kuat menggambarkan hubungan antara korban dengan sumber air sumur yang diduga terkontaminasi. Setelah menutup sumur tersebut pasien berkurang drastis, setelah diteliti, ternyata saluran kotoran rumah yang ditanam 22 kaki telah bocor memasuki sumber air permukaan sedangkan sumur digali hanya selisih 6 kaki saja (28 kaki) menyebabkan air yang terambil adalah bagian yang terkontaminasi.

Density of cholera deaths using a 100 m kernel density function

1. Search (thematic search, search by region) 2. Location analysis (buffer, corridor, overlay) 3. Terrain analysis (slope/aspect, drainage network)

4. Flow analysis (connectivity, shortest path) 5. Distribution (nearest neighbor, proximity, change detection)

    

6. Spatial analysis/statistics (pattern, centrality, similarity, topology)



7. Measurements (distance, perimeter, shape, adjacency, direction)



     

1. Measurements 2. Layer statistics 3. Queries 4. Buffering (vector); Proximity (raster) 5. Filtering (raster) 6. Map overlay (layer on layer selections) 7. Transformations 8. Reclassification 9. Network analysis 10. Spatial interpolation 11. Grid (raster) analysis 12. Surface analysis 13. Analytic modeling

            

Copyright C. Schweik 2011 (Some material adapted from Heywood et al 1998; Theobald, 1999 )

1. Data Retrieval 2. Map Generalization 3. Map Abstractions 4. Map Sheet Manipulation 5. Buffer Generation 6. Polgygon Overlay And Dissolve 7. Grid Cell Analysis 8. Measurement 9. Digital Terrain Analysis 10. Output Techniques

         

3. Pemodelan Spasial

1. 2. 3. 4. 5.

Spatial Autocorrelation Geographically Weighted Regression Spatial Metrics Voronoi Method Multi-criteria Decision Making and Analytical Hierarchy Process 6. Fuzzy Logic 7. Cellular Automata 8. Artificial Neural Network 9. Weight of Evidence 10. Markov Chain 11. Agent Based Model

What is a model? • A simplification of nature. • A representation of a set of objects and their relationships. • A model is a way of describing something that cannot be directly observed.

• A model is a way of communicating complex ideas.

Gary L., 2006

Definition of “Model”

• Simplified, idealized representation of a part of the real world • Learning Tool

• Experimental Tool • Constantly tested by comparison with the real world

• Useful insofar as they explain or simulate the real world

Michael Piasecki, 2014

Models come in many, many flavors • Analysis Models – Step‐by‐step description of how problems are solved.

• Representation models – images, dioramas, wind‐tunnel models, flow channels, sand tables, maps, globes. • Conceptual models ‐ no numerical values or formulas

• Theoretical models ‐ with numerical values or formula • Empirical models ‐ based on observations, but the mechanism may be unknown. o Statistical, e.g. Regression Models o Rule‐based Models o Models based on many measurements (e.g. USLE, RUSLE)

• Physical‐mathematical models ‐ based on physical laws, first principles

• Stochastic models ‐ bases on the concept of randomness and probability: Random numbers simulate variation.


Sumber : Skidmore (2002)

Model Model merupakan representasi dari beberapa bagian dunia nyata, hal ini dikarenakan representasi dari sebuah model memiliki karakteristik yang sama dengan dunia nyata dalam SIG model yang biasa digunakan adalah peta, peta merupakan representasi miniature dari beberapa bagian yang ada di dunia nyata (de by, 2004). Model yang direlasikan dengan koordinat geografi dinamakan model spasial. Sedangkan Proses untuk memanipulasi dan menganalisis data spasial atau data geografis untuk menghasilkan informasi yang berguna untuk memecahkan masalah yang kompleks dinamakan pemodelan spasial. Skidmore (2002) menagatakan bahwa berdasarkan terminologi model yang ditemukan dalam SIG model disini merupakan model logic (deduktif dan induktif) dan model berdasarkan pada pengolahanya (deterministik dan stokastik) sebagaimana pada tabel taksonomi sebuah model

Model deduktif Model deduktif menarik kesimpulan yang spesifik (yang menghasilkan proporsi baru) dari sebuah set yang umum. Dengan kata lain model deduktif berasal dari kebenaran kebanaran umum untuk menghasilkan sebuah kesimpulan yang lebih spesifik. Model induktif Logika argument induktif dianggap identic dengan metode alam, fisik dan ilmu-ilmu social. Argument induktif memperoleh kesimpulan dan fakta tertentu dari fakta fakta tertentu yang muncul sebagai bukti dari suatu kesimpulan. Dengan kata lain serangkaian fakta dapat digunakan untuk memperoleh atau membuktikan pernyataan umum. Model deterministik Model deterministik memiliki output yang tetap untuk masukan yang spesifik. Model deterministik berasal secara empiris dari pengukuran plot lapangan, meskipun aturan ataupun pengetahuan dapat dirumuskan dalam sistem pakar dan secara konsisten akan menghasilkan output yang diberikan untuk masukan yang spesifik. Model deterministic bisa merupakan model induktif maupun deduktif

Model Stokasitik Inti dari sebuah model stokastik adalah jika input data atau parameter model bervariasi secara acak dan outputnya juga bervariasi. Model stokasik semakin sering digunakan dalam sebuah pemodelan seperti model jaringan saraf umumnya dilakukan dengan menggunakan backpropagation (BP).

• A model is a representation of reality • Models are created as a simplified, manageable view of reality • Models help you understand, describe, or predict how things work in the real world

There are three types of data analysis: • Descriptive (business intelligence and data mining) • Predictive (forecasting) • Prescriptive (optimization and simulation)

Descriptive Analytics - What is going on? • Descriptive analytics deals with organizing, manipulating, visualizing, and describing actual data. • descriptive analytics is synonymous with business intelligence. It also includes dashboards, reports, and advanced visualizations. Predictive Analytics – What will happen? • Predictive analytics deals with predicting unknown data based on actual data and other knowledge. • Predictive analytics is generally performed using statistical methods such as regressions and simulations, probabilistic models, and increasingly data mining or machine learning techniques. Prescriptive Analytics – OK, what should we do? • Prescriptive analytics deals with prescribing optimal or near-optimal business actions based on actual and/or predicted data. • This is the elusive answer to the “so what?” • It is not about predicting what will happen, it is about deciding what should be done. • Prescriptive analytics is primarily the realm of optimization and mathematical programming (MP) which includes linear programming (LP), integer programming (IP), non-linear programming (NLP), constraint programming (CP) etc. It also includes heuristic algorithms for sub-optimal solutions, and simulation based and stochastic optimization. • Additionally, decision trees, what-if analyses, and scenario planning are less sophisticated forms of prescriptive analytics.

Modeling • Models allow planners to experiment with 'what-if' scenarios. • Models can be used for dynamic simulation, providing decision makers with dramatic visualizations of alternative futures.

Any model forecast should be accompanied by a realistic measure of uncertainty.

Groundwater vulnerability model in an area of Ohio, USA. The model combines GIS layers representing that affect the groundwater, and displays the results as a map of vulnerability ratings

• Spatial data manipulation – The basic functions of any GIS (e.g. data projection).

• Spatial data analysis – Descriptive and exploratory functions using maps (e.g. overlays).

• Spatial statistical analysis – Uses statistical methods to determine if spatial data are “typical” or “unexpected” relative to a statistical model.

• Spatial modelling – Constructing models to predict spatial outcomes. © J.M. Piwowar

Analytical procedures applied with GIS. It is the set of procedures that simulates real-world conditions within a GIS using the spatial relationships of geographic features. http://www.webopedia.com/TERM/S/spatial_modeling.html

spatial modeling A methodology or set of analytical procedures used to derive information about spatial relationships between geographic phenomena.

http://support.esri.com/en/knowledgebase/GISDictionary/term/spatial%20modeling

What do Spatial Models Do? • Using spatial data • Making use of combined functional capabilities such as analytical tools for spatial and non-spatial computation, GIS and programming languages • The focus is on the meaning of the model - modeling is more than just applying analytical tools • Representing meaningful features, events and processes in geographical space

http://www.colorado.edu/geography/class_homepages/geog_4203_s08/

So what about Spatial Modeling …? • ‘Modeling’ per se is one of the most overloaded terms anywhere • Reason enough to think about what exactly we think of by referring to spatial modeling

• Generally, a model is a (simplified) description of reality (static reproduction, conceptual description) • Modeling can (or should) be considered as a process …


What is GIS Modeling? GIS Modeling is a PROCESS Need of a way to “think spatially” How to represent (abstract) our world in a GIS? What are the visible or functional patterns What are the spatial relationships between representations in the geographic space? • What can these relationships tell us and how can we combine/measure/examine them to derive meaningful models? • As always, a structure is helpful!! • • • • •


Sistem adalah proses, penggunaan kata system bisa di deskripsikan sebagai ide atau konstruksi. Model proses paling sederhana dari sebuah system didasarkan pada input, output, dan system itu sendiri-yang ditampilkan sebagai proses. Scott (1996) mengatakan sistem terdiri dari unsur-unsur seperti: masukan (input) , pengolahan (processing) , serta keluaran (output). Ciri pokok sistem menurut Gapspert ada empat, yaitu: 1. sistem itu beroperasi dalam suatu lingkungan, 2. terdiri atas unsur-unsur, 3. ditandai dengan saling berhubungan dan 4. mempunyai satu fungsi atau tujuan utama.

Model sistem

Cara mempelajari sistem

Peran Vegetasi dalam sistem tanah longsor (landslide)?

The benefits of models • Model manipulation is much easier than manipulating a real system • Models enable the compression of time • The cost of modeling analysis is much lower • The cost of making mistakes during a trial-and-error experiment is much lower when models are used than with real systems

• With modeling, a manager can estimate the risks resulting from specific actions within the uncertainty of the business environment • Mathematical models enable the analysis of a very large number of possible solutions

SLOPE STABILITY

where: FS

Safety factor for slope stability

h

Thickness of cover soil perpendicular to the slope (m)

β

Slope inclination angle (o)

δ

Geomembrane-soil interface friction angle (o)

c

Geomembrane-soil interface adhesion (kPa)

γ

Total unit weight of the cover soil (kN/m3)

ug

LFG pressure (kPa)

https://www.feat.nl/examples/slope_stab/slope_stab.html

http://www.landfilldesign.com/cgi-bin/gasstability.pl?cover_soil_thick=1&slope=60&geomem_angle=22&geomem_adhes=0&cover_soil_weight=16&lfg_press=2

Difference between analysis and modeling Analysis

Modeling

A static approach at one point in time

Multiple stages, perhaps representing different points in time

The search for patterns or anomalies, leading to new ideas

Implementing ideas and hypotheses

Manipulation of data to reveal what would otherwise be invisible invisible

Experimenting with policy options and scenarios

SourceLongley et al. (2005)

Modeling Process and its Components Prior to carrying out the modeling process it is helpful to find answers to four questions (DeMers 1,5): • What is the model to tell us (explaining, predicting relationships or consequences / evaluating situations for resource uses,…)? Or simply: Do we understand what the problem is? • What type of data do I need?

• How to create a design to put the model together? • How to apply existing tools, carefully and appropriately to derive meaningful models? • Validation and verification as important steps are touched later


Verification (a model matches its design) To check, confirm or prove the truth of something. To establish, prove, substantiate, attest, corroborate, support, confirm.

Validation (a model matches the data) To meet some criterion/criteria associated with the model and or the data/observations. In general, validation is the process of checking if something satisfies a certain criterion. Examples would be: checking if a statement is true, if an appliance works as intended, if a computer system is secure, or if computer data is compliant with an open standard.

Calibration and Estimation Calibration is the generic process of validation and verification. Estimation is the process or method of generating a precise estimate of some parameter characterising the model.

• Step 1. Stating the problem. – What is the goal? • Step 2. Breaking the problem down. – What are the objectives. – What are the objects and their interactions (process model). – What datasets (data model and presentation model) will be needed

• Step 3. Exploring the datasets – What is contained in the datasets – what relationships between the datasets • Step 4. Performing analysis (spatial analysis) – Which tools to run the process models and build a overall model • Step 5. Verifying the model’s result – Does any thing in the model need to be changed? – If yes, go back to step 4 • Step 6. Implementing the result

1. Evaluate the real-world situation you intend to analyze 2. Conceptualize in terms appropriate to a computer-based analitycal approach

3. Organize the logical approach to the analysis 4. Implement the spesific software steps.

GIS Modeling • Representation Modeling • Exploratory Data Analysis • Environmental Modeling  Environmental Risk Assessment  Atmospheric Modeling  Soil Erosion Modeling  Hydrological o Topographic Modeling

o Watershed Analysis o Dynamic Modeling

• Land‐water interactions

• Human‐Environment Modeling  Land Suitability Modeling  Land‐use/land‐cover change o Economic models (Walker)

o Agent‐based models

 Archaeological Modeling  Decision‐Support Systems o Land allocation o Agroforestry (Ellis)

• Business/Economic Modeling – Thrall • Emergency Management

• Habitat Modeling












Finding the suitable forest land for harvesting Criteria: • Can harvest with in 300 ft . of roads • Can’t harvest with in 500 ft. of streams Required datasets: • Roads • Streams • Forest

Spatial multi-criteria decision analysis for safe school site selection

Air Pollution Index (API) reading

Bukhari , Z., Rodzi A. M., Noordin A. Spatial and Numerical Modeling Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia 43400 Serdang, Selangor, Malaysia, 2010

Schematic illustration of a multi-objective, multi-criteria evaluation system, under the main objective heading of slope stability analysis and with several possible outcomes (or environmental states)

Habitat Analysis To identify prime habitat areas for the endangered pickled strumpet (Trollopensis bibulosa) in New Castle County Field biologists have given you the following habitat criteria: 1. slope of 2 degrees or less, and 2. either freshwater wetland (LULC1=6) with elevation > 16 feet or forest (LULC1=4) within 250 meters of streams, and 3. at least 200 meters from primary roads (select CFCC categories up to A36 plus A63 highway on-off ramps) and at least 100 meters from all other roads (switch the selection) and at least 100 meters from all rail lines.

https://sites.google.com/a/udel.edu/apec480-s15/a2

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Mata Kuliah di S2 PJ: 1. SIG 1: Basisdata dan IDS 2. Analisis Spasial 3. Pemodelan Spasial. Mata Kuliah Pemodelan Spasial: SKKNI BIDANG IG

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