Spatial Analysis and Modeling 2017 Dasar

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

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 Regardless of where your primary tech interests lie, having a solid understanding of today's most relied-upon computer science languages and programs is essential. https://interestingengineering.com/this-70-hour-computer-science-bundle-will-teach-you-the-fundamentals-of- _{programming?utm_source=Facebook&utm_medium=Facebook_Ads&utm_campaign=Prospecting_Auto_Boosted}

  BEDA:

  

1.SIG 1: Basisdata dan IDS

  2.Analisis Spasial

  3.Pemodelan Spasial

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.)

  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 value-

  added 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:

  

• 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.

  

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

  Density of cholera deaths using a 100 m kernel density function

Spatial analysis can be

• • inductive, to examine empirical evidence in the search for patterns that might support new theories or general

  principles, in this case with regard to disease causation.

• deductive, focusing on the testing of known theories or principles against data
• normative, using spatial analysis to develop or prescribe new or better designs:

Zonasi Sekolah

Tujuan Sistem zonasi :

  agar tidak ada lagi sekolah favorit tertentu melainkan semua sekolah menjadi sekolah favorit

  PPDB berdasarkan sistem zonasi tersebut berdasarkan pada Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia nomor 17 tahun 2017 tentang Penerimaan Peserta Didik Baru pada Taman Kanak-Kanak, SekolahDasar, Sekolah

Menengah Pertama, Sekolah Menengah Atas, Sekolah Menengah Kejuruan, atau Bentuk

Lain yang Sederajat

  Pada pasal 15 ayat (1) disebutkan bahwa skolah yang diselenggarakan oleh pemerintah daerah wajib menerima calon peserta didik yang berdomisili pada radius zona terdekat dari sekolah paling sedikit sebesar 90 persen dari total jumlah keseluruhan peserta didik yang diterima. Selanjutnya pada ayat (5) disebutkan sekolah yang diselenggarakan oleh pemerintah daerah dapat menerima calon peserta didik melalui jalur prestasi yang berdomisili diluar radius zona terdekat dari sekolah paling banyak 5 persen dari total jumlah keseluruhan peserta didik yang diterima. Jalur bagi calon peserta didik yang berdomisili di luar zona terdekat dari sekolah dengan alasan khusus meliputi perpindahan domisili orangtua/wali peserta didik atau terjadi bencana alam/sosial, paling banyak 5 persen dari otal jumlah keseluruhan peserta didik yang diterima

  1. Dinas Pendidikan Kota Yogyakarta telah melakukan pengukuran jarak melalui udara berbasis RW.

  2. Pertimbangannya memang zonasi, tapi nilai ujian sekolah juga jadi pertimbangan untuk masuk sekolah negeri. Kalau jaraknya sama, yang bersaing adalah nilainya

  

3. Terkait sebaran sekolah yang tidak merata, yakni SMP Negeri yang lebih banyak di

sisi utara, menurutnya bukanlah jadi masalah.Pasalnya setiap anak diberikan

keleluasaan untuk memilih SMP Negeri sesuai dengan prioritas pilihannya maksimal di 16 sekolah.

  

4. kuota untuk siswa dalam daerah sebesar 90 persen, termasuk siswa KMS juga ikut

dalam kuota tersebut.

  

5. Sementara 5 persen jalur khusus misalkan orang tuanya merupakan pejabat yang

harus dinas di daerah yang berbeda dan 5 persen lain siswa dari luar daerah.

  6. Jumlah SMP Negeri di Kota Yogyakarta adalah 16 sekolah, sementara jumlah SD Negeri yang ada 89 sekolah dengan potensi lulusan SD sekitar 7.000 siswa.

  Edy Heri Suasana (Kepala Dinas Pendidikan Kota Data:

  1. Agihan Lokasi Sekolah

  2. Daya Tampung Sekolah

  3. Peta administrasi dg unit tekecil RW

  4. Peta Jaringan Jalan

  5. Peta Penduduk Analisis

  1. Buffer

  2. EUCLIDEAN DISTANCE 3. density analysis

  4. Service Area

  5. Cluster and Outlier Analysis (Anselin Local Moran's I)

  6. Spatial Metrics

  7. Thiessen

  8. Hexagon/tessellation

  Spatial analysis has something to do with deriving information from data using the spatial context of the

problem and the data (e.g. their distributions or patterns)

It is exactly this - space - which makes it different

  Spatial analysis is a type of geographical analysis

which seeks to explain patterns of human behavior

and its spatial expression in terms of mathematics

and geometry, that is, locational analysis.

  Physical Geography

• Where and why natural forces occur

  Human Geography

• Where and why human activities occur

The 3

  “W’s” of Geography

• What is where
• Why is it there
• Why do I care

  

  

  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)

        

  

  

  

  

  

  

  

  

  

  

  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

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 miniatur 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) mengatakan 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.

  Sumber : Skidmore (2002)

  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).

  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

• Model: penyederhanaan deskripsi realitas
• Pemodelan: bisa dianggap sebagai proses
• Model Spasial (= Model SIG) menjelaskan sifat dasar dan proses untuk satu set fitur spasial
• Pemodelan Spasial adalah Gambaran matematis tentang struktur dari sebuah fenomena spasial, untuk keperluan prediksi/evaluasi. Disusun berdasarkan pengetahuan spesifik tentang suatu fenomena spasial, biasanya berupa kombinasi dari beberapa operasi spasial terhadap sekumpulan data spasial.
• Tujuannya mempelajari objek atau fenomena spasial di dunia nyata

  

 Model adalah abstraksi dari realitas dengan hanya memusatkan perhatian pada

beberapa sifat dari kehidupan sebenarnya (Simamarta, 1983). Sebagai contoh adalah model pesawat, model rumah (maket) dsb.

  

 Model Spasial adalah model yang menggambarkan sifat dasar dan atau proses

untuk satu set fitur spasial (Bolstad, 2013). Sebagai contoh adalah peta penggunaan lahan, peta administrasi, peta rawan longsor, globe dsb.

   Pemodelan Spasial adalah suatu proses yang dilakukan untuk membangun

model spasial. Lebih menekankan pada prosesnya. Sebagai contoh adalah

pemodelan tsunami dengan bantuan SIG, pembuatan peta rawan bencana tsunami dsb.

   Analisis Spasial adalah sekumpulan metode; perspektif: alat analisis pada kerangka spasial (Longley et al., 2005). Hal ini mencakup Queries, Measurements, Transformations, Descriptive Summaries, Optimization dan Hypothesis. Sebagai contoh adalah buffer, overlay polygon, spatial interpolation dsb.

  Modeling modelling provide

• In the field of GIS,

  understanding of the way the world works with sufficient precision and accuracy to allow prediction and confident decision-making .

• • Modeling concern the way in which analyses are

  carried out using standard functionality

  

Spatial Analysis and Modelling by Tadele

  64 Einstein said "as far as models represent

reality the are in-precise and as far as they

are precise they do not represent reality"

  (source unknown)

  Pemodelan merupakan elemen kunci pada kebanyakan DSS Current trends

  Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions

Model

  Simplified representation — phenomenon,system Model — helps to better understand a phenomenon/system by retaining significant features or relationships of reality

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

  cost of making mistakes during a trial-and-error

experiment is much lower when models are used than

with real systems

• The
>

• • Mathematical models enable the analysis of a very large number of possible solutions.

Analytical procedures applied with 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}

Spatial Analysis:

  successive levels of sophistication

  1. Spatial data description: classic GIS capabilities

• – Spatial queries & measurement,
• – buffering, map layer overlay

  

  2. Exploratory Spatial Data Analysis (ESDA):

• – searching for patterns and possible explanations
• – GeoVisualization through data graphing and mapping

  

• – Descriptive spatial statistics

  3. Spatial statistical analysis and hypothesis testing

• – Are data “to be expected” or are they “unexpected” relative to some statistical model, usually of a random process

  4. Spatial modeling or prediction

• – Constructing models (of processes) to predict spatial outcomes (patterns)

  Briggs Henan University 2010

  72

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.

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.

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

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

  2. Model and compute (Assemble a logical sequence of GIS operations to derive new information) 3. explore and interpret results (map, summarize, graph, compare)

  4. Decide (Make decisions, document your results, determine next steps)

  5. Share your results (communicate as useful maps and information products, tell your story)

GIS Modeling

• Representation Modeling • Human‐Environment Modeling

   Land Suitability Modeling

• Exploratory Data Analysis

   Land ‐use/land‐cover change

• Environmental Modeling _o

  Economic models (Walker)

   Environmental Risk Assessment _o

  Agent ‐based models

   Atmospheric Modeling  Archaeological Modeling  Soil Erosion Modeling  Decision ‐Support Systems  Hydrological _{o o}

  Land allocation Topographic Modeling _{o o} Agroforestry (Ellis) Watershed Analysis _o

• Business/Economic Modeling – Thrall

  Dynamic Modeling

• Emergency Manage>Land‐water interactions
• Habitat Modeling

Example Areas Where Challenging to Make Effective Decisions

• Computer systems operation
• Corporate strategy (e.g. project launching)
• Corporate operations (e.g. ordering policy,based on inventory & past orders)
• Municipal planning
• Managing an industrial or power plant
• Road network planning
• Project management

  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.

KLASIFIKASI MODEL

  Tipe Klasifikasi Tipe Model

  Fungsi Deskriptif (kondisi nyata ) Prediktif (meramalkan) Normatif ( seharusnya )

  Struktur / Morfologi Ikon Analog Simbolik

  Waktu Statik Dinamik

  Aspek Informasi Deterministik (pasti/tentu) Probabilistik (tidak pasti/tidak tentu)

  Tingkat Generalisasi Umum Khusus

  Interaksi dgn lingkungan Terbuka Tertutup

  Tingkat Kuantifikasi Kualitatif Kuantitatif

  Dimensi 2 dimensi

  Bahan Ajar Kuliah Simulasi dan Permodelan

  85

  Maybe we understand more if we look at

  some taxonomies

  of operators for spatial analysis such as …

Does it help?

• • As you can see there are (as expected) many different

  approaches to build a taxonomy

• • You have seen three of them: one based on category of

  

technique, one based on the conceptual model and

one based on the data model used

• What most of them have in common is: They include

  most of the (functional) operations in a GIS, somehow

• Let‘s look at some examples

So maybe it’s rather: How to use SA?

• The three examples gave you an impression in which contexts SA can be used (and this comes along with

  Openshaw and Goodchild & Longley in Longley et al. 2005

• Explore / describe spatial patterns and relationships

  (exploratory/descriptive approach)

• Testing hypotheses regarding spatial patterns and relationships (explanatory/confirmatory approach)
• Simple analysis for criteria evaluation

What is a Spatial Model

• Spatial models (at some places GIS models) might describe basic properties and processes for a set of spatial features
• The aim is to study spatial objects or phenomena in the real world
• As you can imagine we also find dozens of definitions and many different classification schemes - we will look at three of them

Spatial and GIS Models II

  (Goodchild 2003)

• Data models: Entities and fields as conceptual models
• Static modeling: taking inputs to transform them into outputs using sets of tools and functions
• Dynamic modeling: iterative, sets of initial conditions, apply transformations to obtain a series of predictions at time intervals

  (DeMers 2005)

Spatial and GIS Models III

• Based on purpose

  descriptive - passive, description of the study area prescriptive - active, imposing best solution

• Based on methodology

  stochastic - based on statistical probabilities deterministic - based on known functional linkages and interactions

• Based on logic

  inductive - general models based on ind. data deductive - from general to specific using known factors and relationships

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)

  process

• Modeling can (or should) be considered as a …

  

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

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!!

Karakteristik dari model adalah: 1. mampu menggambarkan sistem nyata secara rinci,

2. mampu menerangkan bentuk-bentuk interaksi elemen

dengan jelas, dan 3. mampu meramalkan kondisi-kondisi di masa datang secara realitas.

  Modeling

• Models are used in many different ways, from

  simulations of how the world works, to evaluations of planning scenarios, to the creation of indicators of suitability or vulnerability

• Model is a simplification of reality in be viewed as a model.

  Modeling

• In the field of GIS, modelling provide

  understanding of the way the world works with sufficient precision and accuracy to allow prediction and confident decision-making.

• Modeling concern the way in which analyses are carried out using standard functionality

  Modeling

• One of many uses of GIS analysis tools is to build models.
• A model is a simplified representation of a phenomena or a system.
• A model helps us better understand a

  phenomenon or a system by retaining the significant features and relationships of reality.

  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/m} _{LFG pressure (kPa)} ^{3 )} _{u g} where:

  

  Washington Mudslide: How it Really Happened

3/28/2014 12:12PM A mixture of heavy rain and soft soil created a recipe for disaster in Oso, Wash. WSJ's Jason Bellini

reports. Photo: Next Media Animation.

  Flow diagram of P-index calculations using ArcView model builder

  , penggunaan kata system bisa di deskripsikan sebagai ide atau Sistem adalah proses 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.

  Sistem adalah kumpulan obyek yang saling berinteraksi dan bekerja sama untuk mencapai tujuan logis dalam suatu lingkungan yang kompleks.

  Cara mempelajari sistem

  Peran Vegetasi dalam sistem tanah longsor (landslide)?

• Step 1. Stating the problem.
• – What is the goal?
• Step 2. Breaking the problem down.
• – What are the objectives.

  process

• – What are the objects and their interactions ( model).

  data model and presentation model)

• – What datasets ( 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

  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

  

  

Air Pollution Index (API) reading

Bukhari , Z., Rodzi A. M., Noordin A. Spatial and Numerical Modeling Laboratory, Institute of Advanced Technology,

This problem is a good example of multi-criteria decision problem. Criteria include:

• – Religion – Beauty – Wealth – Family status
• – Family relationship
• – Education

  Agama (Religion) Harta/kekayaan Keturunan Kecantikan (Beauty) (Wealth) (Family relationship)

  )

  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)

  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.

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.

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)

  Wind Prospecting model scheme

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.

  The final lab of this course will be to create a mine suitability model for Trempealeau County, Wisconsin. As has been previously stated in the labs, Trempealeau County, located in western Wisconsin located in a region very popular for frac sand mining. We will be looking specifically at the lower half of the county.

  This is primarily done so the geoprocessing processing takes a shorter amount of time. Two models will be used: one to show where sand mining is suitable and the other to show where sand mining is risky. This index determines where it would be best to develop frac sand mines in Trempealeau County (figure 1). The goal is to create a final map which depicts the best locations for sand mining with minimal impacts to the environment and humans.

  Objectives:

  Suitability for mining Generate a spatial data layer to meet the criteria for:

  1. Geology

  2. Land Cover (suitable and non-suitable)

  3. Proximity to Rail Terminal

  4. Slope

  5. Water-table depth

  6. Then combine the criteria to develop a suitability index model Risk for mining Generate a spatial data layer to measure impacts to:

  1. Streams

  2. Prime farmland

  3. Residential and populated areas

  4. Schools

  5. Wildlife areas

  6. Then combine the factors into a risk model

  7. Examine the results in proximity to prime recreational areas

  Methods and Results: The work flow seen below (figure 2) developed the first suitability model. Some of the input feature classes that

were originally vector needed to be changed from a feature to raster, this was done using the 'Feature to Raster' tool. The 'Euclidean Distance' tool created a distance to closest source raster. This tool basically serves as a

'Buffer' tool within raster. The 'Reclassify' tool was then run to rank the distance of the nominal variable as being

suitable, or not suitable for frac sand mine location. The raster calculator was used to add the reclassified feature classes, which resulted in a suitability index. As stated above, reclassification is vital in order to make the suitability model accurate. Below illustrates the classification that was manually inputted (figure 3) to create the most appropriate suitability model. The higher the ranking, the more suitable the area is for a mine site. The ranks labeled '0' are not at all suitable for a mine site.

  The individual maps that were created from the reclassification are seen below (figure 4). The green areas on the maps were manually ranked the highest in the reclassification tool. The center top map (figure 4) was calculated using the 'Raster Calculator' tool to 'add up' the rasters.

  A very similar workflow was used on the impact model. The feature classes in the model include streams, prime farmland, residential areas, schools, and wildlife areas. The workflow can be seen below (figure 5).

  The specific reclassification ranks that were assigned to the features (figure 6). Unlike the suitability model, a high rank was given to features that are the most impactful to the environment and people. Below illustrates the classification that was manually inputted (figure 6) to create the most appropriate impact model. The distances are all ranked similarly because the closer the mine is to the feature (ie streams and prime farmland), the more impacted the feature is.

  

An additional analysis piece that was used during the lab was a weighted impact model. This was done through Py

Scripter to determine a weighted index model using features of the previous impact model, with specific weight on the residential area, thus making that specific feature more 'important'. A weight of '1.5' was used to emphasize

residential areas. The script for the lab can be seen . A comparative map was created to show the difference between the weighted and regular impact models (figure 8). Another added component that was included for this lab was using the viewshed tool. This tool takes an observation point and calculates the estimated area that can be seen from the observation point using a (DEM).

  The final set in completing the overall suitability model was using the raster calculator tool to 'add up' the calculated impact and calculated suitability features. The workflow can be seen below (figure 11). Its related map is also below (figure 12)

  Conclusion: As frac sand mining will likely become more popular as demand for the sand increases, it is important to

develop accurate and relevant suitability models. Without the proper use of this GIS technology, there could be

many implications that could leave the environment, people, and mining resources in jeopardy. Not only that,

but the specific mining company that would hypothetically use our suitability model could lose millions of dollars

from developing a mine on unsuitable land.

  Discussion:

One of the most challenging components of creating the suitability model was creating a logical ranking for the

reclassification. It is very easy to develop a random numbering system, but to make the project more accurate,

it is important to consider logical breaks when reclassifying rasters. This takes someone who has a relatively expansive knowledge of the subject and how it should be applied in the real world.

  

Through trial and error throughout this lab, it is apparent that it is very easy to 'sway' the data depending on how

the rasters are reclassified, however, it is very important to take time and consider the implications of a miscalculated impact or suitability model.

I personally really enjoyed this lab because it is extremely relevant to the real world and used a large number of

the tools that I have gained over my two formal GIS courses. It is labs like this that get me excited to be able to

use GIS as a took to make important environmental decisions in the future.

  Source:

  Bureau of Transportation Statistics. (n.d.). Rail terminals feature class. Retrieved December 6, 2015, from