using bi for deciding and planning the best usage for governmental lands

Using BI for deciding and planning the best usage for

governmental lands

Submitted ByBassant Nabil Mohamed Abass

AgendaA

gend

aMotivationObjectivesProblem StatementRelated WorksProposed Framework for land usages

BI Land Use Planning and Classification Model (BILUPC Model)

Case StudyConclusionFuture WorkPublication

-2-

3

عليكم ) ه الل نعمت اذكروا اس الن ها أي ياماء الس من يرزقكم ه الل غير خالق من هل

هو إال إله ال تؤفكون واألرض ى فأن )اآليه ) (3فاطر

1. Motivation

1. Quarrying2. Historical

Monuments3. Mining4. Oil Concession

areas5. Soil fertility6. Natural

protectorates

1. Raising the national development and the national income

2. Raising the number of investors in the country

3. Raising the number of human utilization to decrease unemployment -4-

2. Problem Statement• No existence for a model that can help decision maker

in planning and deciding the best usages in governmental lands, nothing exist to say how to utilize the governmental resources the best utilization.

-5-

3. Objective

Visualization Customization Security

Adapting GIS tools and

techniques in order to

present land usages

Dialogue system to support

Decision Making process

1.Privacy: Restrictions on data used like data on military lands or others

2.Security authentication:

to be authenticated by your password

Mak

ing

a U

I tha

t hel

ps in

Reporting

Reports Should be generated to help Decision Maker to take

the right decision at the right time in

the right format

-6-

• Providing a model that help decision makers to enhance usage of governmental lands by adapting Business Intelligence tools through the aid of the following 4 elements:

-7-

Background3 kinds of tools and techniques

are usedGIS tools

Data mining tools

Business Intelligent

tools

1. Layers2. Shape

files3. Attribut

es4. Maps

Shape FilesLayers

GIS tools

Data mining tools

Business Intelligent

tools

Background

Clustering Classification

Data warehouse from different repositories

(National land Database)

Integration between GIS tools + Data mining tools + Different data warehouses

-8-

4. Related Works

1 • Using GIS and outranking multi criteria analysis for land-use suitability assessment (England)

2 • An integrated GIS-based analysis system (IGAS) for supporting land-use management of lake areas in urban fringes ( China)

3 • Evaluation of soil erosion risk using Analytic Network Process and GIS: A case study from Spanish mountain olive plantations (Spain)

Related Works

Provides good

visualization for data

-9-

MCDA is applied to compare the set of identified

alternativesTo decide

and choose the best place for

building an airport

I = ∆ E +∆ L +∆ A+∆ R +∆ B+∆ H

1. Using GIS and outranking multi criteria analysis for land-use suitability assessment (England)

-10-

This model used to

select the best site

for certain purpose

only

I = D E + D L + D A + D R +DB +D HD E= Noise EmissionD L= Correction of distanceD R= Correction of reflectionD A= Correction of air absorptionD B= Correction for soil effectD H= Correction of obstacles

This model Used Multi-criteria decision analysis for choosing best alternatives through factors that helps in

building airports

1. Using GIS and outranking multi criteria analysis for land-use suitability assessment (England) cont…

-11-

2. An integrated GIS-based analysis system (IGAS) for supporting land-use management of lake areas in urban fringes ( China)

• The urban lake areas of China, are ideal locations for

recreational activities and the maintenance of ecological diversity.AHP: Analytical hierarchy process

Procedures of GIS-based land-use suitability assessment

• GDP = GDPI + GDPA + GDPSI1. Agricultural GDP:GDPA(t) = GDPA(t − dt) + GDPA GRA dt 2. Industrial GDP3. Service Industries

IGAS

MCDA

AHP

-12-

3. Evaluation of soil erosion risk using Analytic Network Process and GIS: A case study from Spanish mountain olive plantations

• The risk of soil erosion was evaluated in olive groves in Southern Spain,

• showing the potential of the ANP method (Analytic Network Process method) for modeling a complex physical process like

soil erosion.• showing usage of Multi-criteria

decision analysis.• Thus, soil erosion risk = 0.1986

+0.0428 + 0.0399 +0.027+0.0318 = 0.3401

shows the final weights of the soil erosion factors-13-

Proposed framework Vision (England Model)

I = D E + D L + D A + D R +DB +D H

-14-

Proposed Modelfor Land Use Planning and Classification Model

LUC= %F1∩%F2∩%F3∩%F4∩%Fn… = 100%LUC: stands for Land Use Classification

-15-

Models as:AgricultureIndustrialTouristic

National Land

Database

Dialogue Managemen

t (Agent

Negotiations)

DSS Support Decision

Maker for land use classification

Planning

To achieve the

proposed frame work,

dialogue should happen between

data warehouses

and models

-16-

5. Proposed Framework ( level 1)Data Capture:Data collected from different

Data ware houses of legal authorities ex: layer names,

areas, longitude, latitude

PreprocessingSelection Filtration elimination

To select proposed activities & area of interest

To use MCDA to select factors

To eliminate

unnecessary factors

Transformation:To transform text data into shape files &

layers

Data mining:Using 2 methods

cluster and classification to

analyze data & to detect intersection between factors

Interpretation evaluation:

To make user interface through

agent negotiations & Reports

Knowledge:For decision

maker to take decision

-17-

5. Proposed Framework ( level 2a) ( Filtration)Filtration (Preprocess phase)

Multi-criteria Decision analysis(MCDA)

SWOT Analysis

It comes out of Delphi studies

Benchmarking Method

identifying likely

competitive challenges

Delphi Method

is composed of integration between

Expert panels,Questionnaires,Send to experts,Send to experts with feedback.

Scanning

Analysis

Expert Survey

Results

-18-

5. Proposed Framework ( level 2b) ( Data mining)

Two methods used

Clustering

To integrate each similar factors in one cluster

Classification

To measure degree of importance to each factor in each cluster

-19-

5. Proposed Framework ( level 2b) ( Data mining) cont…

Clustering

Classification

Determine objectives

on the map +

determining

intersection between layers or degree of potentialiti

es

-20-

New vision for England model (old model)

Multi Criteria Decision Analysis is not the only phase that help in

Choosing alternative

s

Supporting Decision maker

in taking decisions

But it will be one of two phases implemented to support decision maker in planning

and deciding the best usage of the governmental lands

-21-

1. Implementing Multi criteria decision analysis in determining factors

Delphi Method

SWOT Analysis

Benchmarking

Method

1. Water2. Infrastructure3. Proximity to

Natural Protectorates

4. Soil5. Tourist Resorts or

monuments6. Island Marine7. Labor8. Hills and highlands9. Proximity to mines

and quarries10. Proximity to air

ports & ports11. Away from

contaminated areas

FactorsDELPHI RESULTED TO

DETERMININGwater

Infrastructure

Natural protectorates

Soil

Tourist resorts

Island Marine

-22-

2. Data Mining Phase (cluster + classification)

In order to determine the best utilization for governmental lands

Constituents of use(utilization)

Region Properties(Factors)

-23-

2. Data Mining Phase (CLUSTER + classification) cont…

Constituents of useRegion Properties

1. Water2. Infrastructure3. Proximity to

Natural Protectorates

4. Soil5. Tourist Resorts or

monuments6. Island Marine7. Labor8. Hills and highlands9. Proximity to mines

and quarries10. Proximity to air

ports & ports11. Away from

contaminated areas

If AgriculturalMake a cluster

that contains

• Through equation resulted from the new model 3 factors were achieved which are;

Water ∩ Soil ∩ Labor

LUC= %F1∩%F2∩%F3∩%F4∩%Fn… = 100%-24-

2. Data Mining Phase (CLUSTER + classification) cont…

Constituents of useRegion Properties

1.Water2.Infrastructure3.Proximity to Natural Protectorates

4.Soil5.Tourist Resorts or monuments

6.Island Marine7.Labor8.Hills and highlands9.Proximity to mines and quarries

10.Proximity to air ports & ports

11.Away from contaminated areas

Make a cluster

that contains

• Through equation resulted from the new model 4factors were achieved which are;

Tourist resort∩ Island marine ∩ proximity to air port ∩ Away from contaminations

LUC= %F1∩%F2∩%F3∩%F4∩%Fn… = 100%

Tourism

1ST STEP

-25-

2. Data Mining Phase (cluster + classification) cont…

Using Classification method in Data mining to classify each factor according to its degree of importance that leads to best utilization and best usage for governmental lands

2ND STEP

50%

1%2%

30%

1%1%

10%1%

2% 1% 1%

Water Infrastructure

Proximity to natural protectorates Soil

Tourist resort OR Monuments Islands Marine

Labor Hills and Highlands

Proximity to Mines and Quarries Proximity to air ports and ports

Away from Contaminated Areas

Agriculture

LUC= %F1∩%F2∩%F3∩%F4∩%Fn… = 100%-26-

Factors

Proximity to Mines & QuarriesProximity to Settlements-LaborTerrainIsland MarinesTourist Resorts or Moments

SoilsProximity to natural protectorates InfrastructureWater

Total Proximity to Transportation

Uses Agriculture(%)

5012

30

112

102

1

100


2. D

ata

Min

ing

Phas

e (c

lust

er +

cl

assi

ficat

ion)

con

t…

-27-


Using Classification method in Data mining to classify each factor according to its degree of importance that leads to best utilization and best usage for governmental lands

2ND STEPLUC= %F1∩%F2∩%F3∩%F4∩

%Fn… = 100%

The most important factors are:The greatest values

According to

equation

F2F1F3

Water 50%Soil 30%

Labor 10%Hills & High lands 1%

Island Marine 1%Proximity to mines 2%

Proximity to airports 1%Away from contaminated areas 1%

Infrastructure 1%Natural protectorates 2%

Monuments 1%

So, Small Percentages doesn’t affect

-28-

Case Study

Sinai

Working Environment:

1. Data Resources2. Data Maps

-30-

1. Topographic Data: Egyptian Survey Authority, Military Survey Authority

2. Climatic Data: Egyptian Climate Authority3. Soil Data: Research institute of ground water and

soil ( واالراضى الجوفيه المياة لبحوث القومى (المعهد4. Geologic and mining Data: Egyptian geological

survey5. Environmental data: Egyptian environmental affairs6. Agricultural Data: agricultural research institute7. Touristic Data: tourism development authority8. Industrial Data: industrial development authority

1. Data Resources:

-31-

2. Data Maps:Topographic Data: topographic maps drawn on a scale 1:250000 Military Survey

Soil Data: Meta-logic Map scale 1:1,000,000, issued by the Geological Survey of Egypt

Geologic and mining Data: hydrology map of scale 1:1000000-32-

Factors

Proximity to Mines & QuarriesProximity to Settlements-LaborTerrain (Hills and highlands)Island MarinesTourist Resorts or Moments



Uses Agriculture(%)

60--

30

---

10-

-

100

Data Classification

-33-

Factors




Uses Agriculture(%)

60--

30

---

10-

-

100

Industry(%)

-30--

---

1060

-

100

Data Classification

-34-

Factors




Uses Agriculture(%)

60--

30

---

10-

-

100

Industry(%)

-30--

---

1060

-

100

Tourism(%)

----

50101010--

20

100

Data Classification

-35-

Factors




Uses Agriculture(%)

60--

30

---

10-

-

100

Industry(%)

-30--

---

1060

-

100

Tourism(%)

----

50101010--

20

100

Housing(%)

-40-

10

--

1010-

30

100

Data Classification

-36-

Agricultural

capability map

Data about layers (name, areas, longitude, latitude) Agricultural Capability

-37-

Soil fertilit

y degre

es

Data about layers (name, areas, longitude, latitude) Agricultural Capability (cont…)

-38-

Mining Capabilitie

s

Quarries Capabilities

Building Material

Capabilities

Data about layers (name, areas, longitude, latitude) Extractive industries Capability

-39-

Data about layers (name, areas, longitude, latitude) Tourism Capability

TourismCapability

Map

-40-

Soil Fertility- Land productivity

From 1- 5

-41-

Quarries

-42-

Building Material

s

-43-

Mining

-44-

Mining Capabilitie

s

Quarries Capabilities

Building Material

Capabilities

Extractive Industries

-45-

In Order to use this UI control you have to be authorized

Authentication security:

-46-

Detecting Overlap

• Now we have to convert all matrix values into zeros and ones for normalization as follows:

-47-

Using analysis tools to detect intersections between factors and layers

using HistogramDetecting Overlap cont…

-48-

Using Line plot diagram

Detecting Overlap cont…

-49-

• Tourism Potentialities• Mining quarrying

Potentialities

• Tourism Potentialities• Mining Potentialities• Solar energy potentialities

Overlapping Map

-50-

Overlapping MapMINING QUARRYING POTENTIALITIES

Tourism Potentialities

-51-

Overlapping Map MINING QUARRYING POTENTIALITIES

HAMMAM FARAON TOURISM POTENTIALITIES

SOLAR ENERGY

-52-

Report To Help

Decision Maker In

Taking Decisions

-53-

Performance Evaluation :

Features England model New Vision Using geo-database 1 1 Using dynamic model 1 1 Using model builder 0 1 Using spatial analysis 1 1 Using Multi-criteria decision analysis 1 1 Using clustering method 0 1 Using classification method 0 1 Using evaluation matrix 1 1 Social, economical, environmental goals 1 1 Showing best utilization for overlapping 0 1 Dynamic reports 0 1

Zeroes and ones in this table represents feature existence in the model

England Model

-54-


Features China model New Vision Using geo-database 1 1 Using dynamic model 0 1 Using model builder 0 1 Using spatial analysis 0 1 Using Multi-criteria decision analysis 1 1 Using clustering method 0 1 Using classification method 0 1 Applying Analytical hierarchy process 1 1 Using evaluation matrix 0 1 Economical goals 1 1 Social, environmental goals 0 1 Showing best utilization for overlapping 0 1 Dynamic reports 0 1

China Model

-55-



Features Spain model New Vision Using geo-database 1 1 Using dynamic model 0 1 Using model builder 0 1 Using spatial analysis 0 1 Using Multi-criteria decision analysis 1 1 Using clustering method 0 1 Using classification method 0 1Analytical Network Process 1 0 Using evaluation matrix 0 1 Economical goals 1 1 Social, environmental goals 0 1Showing best utilization for overlapping 0 1Dynamic reports 0 1


Spain Model

-56-

Conclusion:

-57-

1. Utilize Governmental resources with the best utilization

2. Applying automatic Reports about used areas and unused areas to help

the decision maker to decide and plan for the best usage of the

governmental land

3. Applying password to prevent governmental data from misusages

4. By using the Business Intelligent Land use Classification Planning

model, any piece of land could be classified according to its utilization,

by understanding its factors to collect the dataset elements, and then put

this data in the geo-database to reach the optimal utilization result by

applying the mathematical equation that runs through the dot net

application behind the arc-GIS tool.

Future Work: Land usages Future Challenges

Make an online application that help people to know their country resources in a transparent way. -58-

Thank You

-59-

using bi for deciding and planning the best usage for governmental lands

Data & Analytics