Mata Kuliah di S2 PJ:1. SIG 1: Basisdata dan IDS2. Analisis Spasial3. 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 IDS2.Analisis Spasial3.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

• 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.

GIS Functions

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

The true value of GIS lies in their ability to analyze spatial data using

the techniques of spatial analysis. Spatial analysis provides the value-

added products from existing datasets”

Goodchild, 1988

Spatial analysis

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

• 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)

More about spatial analysis…

• 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)

tUGAS/tOOxZhsLS_8C.pdf

Copyright C. Schweik 2011

(Some material adapted from

Heywood et al 1998; Theobald,

1999 )

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

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. Spatial Autocorrelation

2. Geographically Weighted Regression

3. Spatial Metrics

4. Voronoi Method

5. 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.

Michael Piasecki, 2014

Sumber : Skidmore (2002)

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

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.

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

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

• 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/

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 …

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

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

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

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)

where:

SLOPE STABILITY

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

The search for patterns or anomalies,

leading to new ideas

Manipulation of data to reveal what

would otherwise be invisible

invisible

Multiple stages, perhaps representing

different points in time

Implementing ideas and hypotheses

Experimenting with policy options and

scenarios

SourceLongley et al. (2005)

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

Modeling Process and its ComponentsPrior 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 EstimationCalibration 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

• Habitat Modeling

• 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

Michael Piasecki, 2014

Spatial Analyst Site Suitability Lecture - YouTube.MKV

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

Michael Piasecki, 2014

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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