ARCGIS-BASED lab geospatial analysis in gis gis.pdf

BAKHAT ALI Bakhtali21uaar@gmail.com 1 BAKHAT ALI Institute of Geoinformatics and Earth Observation, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi , Punjab, Pakistan bakhtali21uaar@gmail.com

BAKHAT ALI Bakhtali21uaar@gmail.com 2 LAB# 1: Create map by ARCGIS GIS mapping is a process that helps users manage, organize, and analyze location-based data. Combining traditional mapping with location-based data, it was created in an effort to transcend the limits of two- dimensional paper maps. Parts of a Map Title Scale Legend Compass Latitude and Longitude Title: The title indicates the theme of the map, explaining what is represented in the image you see. Map Scale: The presence of a map scale allows for a map to be physically and visually distinct from other maps. Map Key (Map Legend) A map key will contain a list of different symbols and/or colors next to a brief explanation of what each symbol means. Compass Rose A small, but important part of each map is the directional reference. Latitude and Longitude The last feature critical to all maps falls into the ability to label a specific location on the planet.

BAKHAT ALI Bakhtali21uaar@gmail.com 3 STEPS : Open ArcGIS. Click on plus sign to add data and select the file. Then right click on file and click on label feature option. Again right click on file and open properties. Now click on labels and write file name in label field then click on apply and then ok. If you want to change color click on small box under file and select color as you want then click on ok. Click on layout view. After that click on select features Click on insert option and select title option and give name to your map. Again click on insert and click neat line option and change background color of your map if you want. Click on insert and select legend option. Click on next option until it ends on finish. Click on insert and select north arrow and select arrow shape whatever you want. Again click on insert and select scale bar option. Select scale according to your own choice. Now give coordinates to your map. To give coordinates to map, right click on layers and select properties option. Then select new grid option.

BAKHAT ALI Bakhtali21uaar@gmail.com 4 LAB# 2: CREATE SHAPE FILE The shape file format is a geospatial vector data format for geographic information system (GIS) software. The shape file format can spatially describe vector features: Point POLYLINE P0LYGON

BAKHAT ALI Bakhtali21uaar@gmail.com 5 STEP#1 created point shapefile Image1 Image2 FROM image 1and2 • OPEN ARCGIS • USED CATALOG TOOL • Select any folder connection • CLICK right to connection folder • Select new shape file • Select point shape file • Select COORDINATE WGS 1984

BAKHAT ALI Bakhtali21uaar@gmail.com 6 STEP#2 created polyline shapefile Image3 Image4 FROM image 3 and 4 • OPEN ARCGIS • USED CATALOG TOOL • Select any folder connection • CLICK right to connection folder • Select new shape file • Select POLYLINE shape file • Select COORDINATE WGS 1984

BAKHAT ALI Bakhtali21uaar@gmail.com 7 STEP#3 created polygon shapefile Image5 Image6 FROM image 5 and 6 • OPEN ARCGIS • USED CATALOG TOOL • Select any folder connection • CLICK right to connection folder • Select new shape file • Select P0LYGON shape file • Select COORDINATE WGS 1984

BAKHAT ALI Bakhtali21uaar@gmail.com 8 STEP#4 editing point ,line and polygon Image7 Image8 FROM image 7 AND 8 • START EDITING AND DRAW POINT, LINE AND POLYGON • RIGH CLICK SHAP FILE TO SELECT PROPERTIESAND GAVING COORDINATE • WGS 1984 UTM ZONE 43 N

BAKHAT ALI Bakhtali21uaar@gmail.com 9 LAB# 3: Adding point(GPS POINT) data from excel sheet in ArcGIS Spatial Data : Data that define a location (reference) of a geographical features. STEP#1 calculator point data Google Earth GPS point ’s and to add Excel sheet Image1 FROM Image1 • ADD GPS data Google Earth pro • Copy latitude and longitude to add Excel sheet • Title Excel sheet of tree point, x-coordinate is longitude and y- coordinate is latitude

BAKHAT ALI Bakhtali21uaar@gmail.com 10 STEP#2 Add GPS point data in ArcGIS Image2 Image3 Image4 FROM image2,3,4 • Adding x and y coordinate to Excel sheet date • Select COORDINATE WGS 1984

BAKHAT ALI Bakhtali21uaar@gmail.com 11 STEP#3 Add spatial point data (city and other GPS data point ) in study area map Image5, Image6

BAKHAT ALI Bakhtali21uaar@gmail.com 12 Image7 FROM image5,6,7 Special data add to vector • Select COORDINATE WGS 1984 UTM ZONE 43 N • Adding vector data to show special data point LAB#4 Georeferencing In addition to latitude and longitude, georeferencing often makes use of methods for projecting the Earth's curved surface onto a plane and associated planar coordinate systems .

BAKHAT ALI Bakhtali21uaar@gmail.com 13 STEP#1 Set coordinates and start geo-referencing in image Image1 Image2 FROM image 1and2 • Connect internet and AREGIS online • Select COORDINATE WGS 1984 UTM ZONE 43 N • Select georeferencing tool • Start georeferencing add point 1 in Muzaffargarh mage C

BAKHAT ALI Bakhtali21uaar@gmail.com 14 STEP#2 add base map georeferencing image with base map Image3 Image4 Image5

BAKHAT ALI Bakhtali21uaar@gmail.com 15 Image6 Image7 FROM image 3,4,5,6,7 • Add base map in ARCGIS to imagery with labels • Add same point in base map • Add 3 point in Muzaffargarh and add 3point in base map • Show Muzaffargarh mage base map in to his location

BAKHAT ALI Bakhtali21uaar@gmail.com 16 LAB#5 : Digitization Digitization is the process of converting geographic data into digital form. During this process, spatial data on maps or images are traced as points, polylines or polygons. STEP#1 Created shapefiles Image1 Image2

BAKHAT ALI Bakhtali21uaar@gmail.com 17 Image3 FROM image1,2,3 • OPEN ARCGIS • USED CATALOG TOOL • Select any folder connection • CLICK right to connection folder • Select Muzaffargarh shape file • Select point, polyline, polygon and shape file Select COORDINATE WGS 1984

BAKHAT ALI Bakhtali21uaar@gmail.com 18 STEP#2 start editing features Image4 Image5 FROM image4 and 5 • START EDITING AND DRAW POINT, LINE AND POLYGON • RIGH CLICK SHAP FILE Muzaffargarh TO SELECT PROPERTIESAND GAVING COORDINATE WGS 1984 UTM ZONE 43 N Mage3

BAKHAT ALI Bakhtali21uaar@gmail.com 19 LAB#6: Conversion data Raster Data: Raster data is made of pixels. It is an array of grid cells with columns and row. Each and every geographical feature is represented only through pixels in raster data. Vector Data: Vector data represents any geographical feature through points, line or combination of these. ASCII: ASCLL in full American Standard Code for Information Interchange, a standard data-encoding format for electronic communication between computers. STEP#1: Conversion vector to raster data Image1

BAKHAT ALI Bakhtali21uaar@gmail.com 20 Image2 Image3

BAKHAT ALI Bakhtali21uaar@gmail.com 21 Image4 FROM image 1,2,3,4 • Add vector data • Select conversion tool • Select to raster tool • Select raster polygon • Convert raster

BAKHAT ALI Bakhtali21uaar@gmail.com 22 STEP#2: Conversion raster data to vector Image1 Image2

BAKHAT ALI Bakhtali21uaar@gmail.com 23 Image3 FROM image 1,2,3, • Add raster data • Select conversion tool • Select from raster to vector tool • Select vector polygon • Convert vector

BAKHAT ALI Bakhtali21uaar@gmail.com 24 STEP#3: Conversion ASCLL to raster data Image1 Image2

BAKHAT ALI Bakhtali21uaar@gmail.com 25 Image3 Image4 FROM image 1,2,3,4 • Add ASCLL data • Create .ASC file • Select conversion tool • Select ASCLL to raster tool • Convert raster

BAKHAT ALI Bakhtali21uaar@gmail.com 26 STEP#4: Conversion raster to ASCLL data Image1 Image2

BAKHAT ALI Bakhtali21uaar@gmail.com 27 Image3 FROM image 1,2,3, • Add raster data • Select conversion tool • Select from raster to ASCLL tool • Convert ASCLL

BAKHAT ALI Bakhtali21uaar@gmail.com 28 LAB#7KERNEL DENSITY IDW: Spatial interpolation is a method that uses the known values at given locations to estimate a continuous surface. There are several types of spatial interpolation, including inverse distance weighting (IDW), spline, and Kriging. Inverse distance weighted (IDW) interpolation determines cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance. The surface being interpolated should be that of a locationally dependent variable. IDW neighborhood for selected point . STEP#1 Add data and calculations IDW Image1

BAKHAT ALI Bakhtali21uaar@gmail.com 29 Image2 FROM image 1and 2 • Adding data and select special analyst tool • Select interpolation tool • Select IDW • Select data and z-value average Image3

BAKHAT ALI Bakhtali21uaar@gmail.com 30 Image4 FROM image 3and4 • Processing extent tool to select same as layer Punjab • Raster analysis to make Punjab LAB#8: kriging Kriging is a powerful type of spatial interpolation that uses complex mathematical formulas to estimate values at unknown points based on the values at known points. I will focus on performing Kriging using ArcMap’s Geostatistical Analyst toolbox. Kriging can also be performed using other software, such as R statistical software, GeoDa but the Geostatistical Wizard tool in the ArcMap toolbox has an easy-to-use interface.

BAKHAT ALI Bakhtali21uaar@gmail.com 31 STEP#1 Calculate Kriging Image1 Image2 FROM image 1and 2 • Adding data and select special analyst tool • Select interpolation tool • Select Kriging • Select data and z-value average

BAKHAT ALI Bakhtali21uaar@gmail.com 32 Image3 Image4 FROM image 3and4 • Processing extent tool to select same as layer Punjab • Raster analysis to make Punjab

BAKHAT ALI Bakhtali21uaar@gmail.com 33 LAB # 9: MAKING OF USGS ACCOUN STEP#1 Image1 FROM image1 • OPEN USGS EARTH EXPLORER • CLICK ON LONIN STEP#2

BAKHAT ALI Bakhtali21uaar@gmail.com 34 Image2 • FROM image2 • ENTER USERNAME • ENTER PASSWORD • ENTER CONFIM NEW PASSWORD • SAVE CONTACT INFROMATION STEP#3

BAKHAT ALI Bakhtali21uaar@gmail.com 35 Image3 FROM image3 • AFTER SAVING CONTACT INFROMATION • AFTER SAVING SUBMITTING REGISTRATION LAB#10 : How to download Landsat data STEP#1 Image1

BAKHAT ALI Bakhtali21uaar@gmail.com 36 Image2 FROM image1and2 • SING in USGS • Click on address place and then click on show • Click on LANDSAT and then click on LANDSAT callection2 level2 • Click on LANDSAT8,9

BAKHAT ALI Bakhtali21uaar@gmail.com 37 STEP#2 Image3 Image4 FROM image3and4 • CLICK mage LANDSAT8,9 download and save to pc.

BAKHAT ALI Bakhtali21uaar@gmail.com 38 LAB#11: How to add download Landsat data in ARCGIS. STEPS Image1 Image2

BAKHAT ALI Bakhtali21uaar@gmail.com 39 Image3 FROM image1,2and3 • Open ARCGIS • SET coordinate WGS-1984 • Add save data in ARCGIS • Given the mage to color

BAKHAT ALI Bakhtali21uaar@gmail.com 40 LAB#12: NDVI The Normalized Difference Vegetation Index (NDVI) is a popular remote sensing tool used to assess the density and health of vegetation STEP#1 Calculate NDVI Image1

BAKHAT ALI Bakhtali21uaar@gmail.com 41 Image2 • From image 1and 2 • Add B4 and B5 • NDVI=B5-B4/B5+B4 STEP#2 RECLASSIFIED NDVI and clip area of interest Image3

BAKHAT ALI Bakhtali21uaar@gmail.com 42 Image4 Image5

BAKHAT ALI Bakhtali21uaar@gmail.com 43 Image6 From image 3,4,5and 6 • Add Muzaffargarh map • SHOW Muzaffargarh IDVI Image7

BAKHAT ALI Bakhtali21uaar@gmail.com 44 Image8 From image 7and8 • Given the 4 classification • Show the mage MUZAFFARGARH classification LAB#13: NDWI The Normalized Difference Water Index (NDWI) is a remote sensing tool that helps identify water bodies and evaluate the water content in plants. STEP#1 Image1

BAKHAT ALI Bakhtali21uaar@gmail.com 45 Image2 • From image 1and 2 • Add B3 and B5 • NDWI=B3-B5/B3+B5 • STEP#2 Calculate NDWI and clip area of interest

BAKHAT ALI Bakhtali21uaar@gmail.com 46 Image3 Image4 Image5

BAKHAT ALI Bakhtali21uaar@gmail.com 47 Image6 From image 3,4,5and 6 • Add Muzaffargarh map • SHOW Muzaffargarh IDVI STEP#3 reclassified Image7

BAKHAT ALI Bakhtali21uaar@gmail.com 48 Image8 From image 7and8 • Given the 4 classification • Show the mage MUZAFFARGARH classification LAB#14 : LST LST( Land Surface Temperature), is the temperature of the Earth's surface measured using remote sensing technologies like satellites and drones. It serves as a crucial indicator of surface energy balance, ecosystem health, and climate conditions. Grasping the concept of LST is vital across multiple disciplines, including meteorology, agriculture, urban planning, and environmental monitoring. STEP#1: Calculation of TOA

BAKHAT ALI Bakhtali21uaar@gmail.com 49 Image1 • FROM IMAGE 1 • Add Land sat 8 • Add Band10 Image2 From image 2and3 • From mage 2 • T0A=ML* Qcal +Al-Q • From mage 3 • Q=0.295 • Calculation of top or radiance Stop#2: TOA to brightness temperature(BT)conversion

BAKHAT ALI Bakhtali21uaar@gmail.com 50 Image4 From image 4 • BT=k2/ln(k1/radiance) +1] -273.15 STEP#3: NDVI Image5

BAKHAT ALI Bakhtali21uaar@gmail.com 51 Image6 From image 5and 6 • Add B4 and B5 • NDVI=B5-b4/B5+B4 STOP#4: LSE OR PV Image7and8 • From 7and8 • PV=(NDVI-NDVImin) / (NDVImax –NDVImin ))2 STOP#5: Land Surface temperature or LST

BAKHAT ALI Bakhtali21uaar@gmail.com 52 Image9 Image10 • From image 9and 10 • LST= BT/(1+(radiance *BT/c2)*in(E)) • C2=1.4388 and E=0.004*pv+0.986 STOP#6: LST of Muzaffargarh

BAKHAT ALI Bakhtali21uaar@gmail.com 53 Image11 Image12 • From image 11and 12 • Add Muzaffargarh map • SHOW Muzaffargarh LST

BAKHAT ALI Bakhtali21uaar@gmail.com 54 LAB#15: Geodatabase University Outline: 1) Create Geodatabase 2) Create relationship 3) Create topology 4) Create network short route PRACTICAL#1 TITLE: Create Geodatabase STEPS: Open ArcGIS. Gotocatalog Select folder connection rightclick onfolder andselect file geodatabase and create give name “Arid Geodatabase “ Figure 1.1: File geodatabase and create give name “Arid Geodatabase “

BAKHAT ALI Bakhtali21uaar@gmail.com 55 Select file geodatabase andrightclickonselect file geodatabase and select new toselect feature dataset given name uni create it. Figure 1.2: Feature dataset given name uni create it. Select feature dataset andrightclickonselect feature datasetand select new to selectfeature class pint(tree),line(road)and polygon(boundary) given name uni create it.

BAKHAT ALI Bakhtali21uaar@gmail.com 56 Figure 1.3: Feature class pint (tree) Figure 1.4: Feature class line (road) Figure 1.5: Feature class polygon (boundary)

BAKHAT ALI Bakhtali21uaar@gmail.com 57 GO window oneditor START EDITING ANDDRAWPOINT, LINE AND POLYGON Stop editor andsave Figure 1.5: START EDITING ANDDRAWPOINT, LINE AND Polygon Figure 1.6: Stop editor and save

BAKHAT ALI Bakhtali21uaar@gmail.com 58 PRACTICAL#2 TITLE: Create relationship STEPS: Select file geodatabase andrightclickonselect file geodatabase and select new toselect table given name building and department two table create it. Building table A attribute building_ name and department_ name. Start editing add record Departmenttable B attribute ,department _name, student name, semester andCouse Start editing add record Figure 2.1: Table created Figure 2.2: Table A attribute

BAKHAT ALI Bakhtali21uaar@gmail.com 59 Figure 2.3: Table B attribute Figure 2.4: Table start editing Figure 2.5: Table A adding record

BAKHAT ALI Bakhtali21uaar@gmail.com 60 Figure 2.6: Table B adding record Create relationship building and department Building _name used primary key department _name used Foreign key Figure 2.7: Relate table Figure 2.8: Building _name used primary key department _name used Foreign key

BAKHAT ALI Bakhtali21uaar@gmail.com 61 Figure 2.9: relationship building and department Create relationship department and building Department _name used primary key Foreign key Building _name Figure 2.10: Department _name used primary key Foreign key Building _name Figure 2.11: Create relationship department and building

BAKHAT ALI Bakhtali21uaar@gmail.com 62 PRACTICAL#3 TITLE: Create topology STEPS: Selectfeature dataset andrightclickonselectfeature datasetand select new to selecttopology add feature class road and adding all role given name uni topology create it. Goto window ontopology tool andstarting editing Figure 3.1: Topology created Figure 3.2: Add rule topology Figure 3.3: Topology tool on

BAKHAT ALI Bakhtali21uaar@gmail.com 63 Goto topology tool select errors inspector and remove error adding role show error Figure 3.4: Add role show errors Figure 3.5: Remove errors split Figure 3.6: Add role show errors Figure 3.7: remove errors mark as exception

BAKHAT ALI Bakhtali21uaar@gmail.com 64 Figure 3.8: Add role show errors Figure 3.9: remove merge to largest Figure 3.10: Add role show errors Figure 3.11: remove split

BAKHAT ALI Bakhtali21uaar@gmail.com 65 PRACTICAL#4 TITLE: Create network short route STEPS: Selectfeature dataset andrightclickonselect feature datasetand select new to selectnetwork dataset add uni topology create it. Goto window onnetwork analysttool and Goto network analyst tool Select new route Select create network locationtool addlocation to select solve tool create short route, route 1, route2, route3, route 4 androute 5. Figure 4.1: Create network dataset Figure 4.2: network analyst tool on

BAKHAT ALI Bakhtali21uaar@gmail.com 66 Figure 4.3:Network analyst tool select new route

BAKHAT ALI Bakhtali21uaar@gmail.com 67 Figure 4.4 : Create short rout, route 1, route2, route3, route 4 and route 5. Geodatabase University Map

BAKHAT ALI Bakhtali21uaar@gmail.com 68 Lab#16 : University Ground Survey University Ground Survey : The purpose of this report is to present the findings of a survey conducted on the university ground. The survey aimed to gather data on various coordinates and aspects of the ground to assess its current state and potential areas for improvement. Mobile GPS: Findings: Figure 1.1 Mobile devices GPS survey Usage Patterns: Various activities such as sports practices, recreational gatherings, and events contribute to the ground's popularity. Recommendations: Based on the survey findings, the following recommendations are proposed for enhancing the university ground Accuracy: 2.96 m

BAKHAT ALI Bakhtali21uaar@gmail.com 69 GPS device: Findings: Figure 2.1 GPS devices survey Usage Patterns: Various activities such as sports practices, recreational gatherings, and events contribute to the ground's popularity. Recommendations: Based on the survey findings, the following recommendations are proposed for enhancing the university ground give more best results. Accuracy:1.1 m

BAKHAT ALI Bakhtali21uaar@gmail.com 70 DGPS: Findings: Figure 3.1 DGPS survey Usage Patterns: Various activities such as sports practices, recreational gatherings, and events contribute to the ground's popularity. Recommendations: Based on the survey findings, the following recommendationsare proposed for enhancing the university ground .DGPS accuracy better than GPS devices and mobile GPS Accuracy: 25 cm Conclusion: The survey of our university ground revealed its central role in hosting various activities, fostering a vibrant community. However, location data accuracy varies across devices, with mobile GPS at 2.96 meters, standard GPS at 1.1 meters, and Differential GPS (DGPS) at 25 centimeters. We recommend adopting DGPS for precise tracking and mapping, enhancing data accuracy and enabling better activity planning. DGPS also opens avenues for advanced features like augmented reality navigation, enriching user experiences. Investing in DGPS technology will optimize ground use, providing students and faculty with superior recreational and academic environ

BAKHAT ALI Bakhtali21uaar@gmail.com 71 Lab#16 : Project Project A temporary endeavor aimed at creating a unique product or service. Key Characteristics of a Project: Temporary: Projects have a defined beginning and end. Unique: Each project produces a unique result or service. Specific Objectives: Projects aim to achieve particular goals or solve specific problems. Resource Constraints: Projects operate within limitations of time, budget, and resources. Project Proposal A document that outlines the plan for a proposed project to gain approval and support.. Key Components: Title: A clear title that summarizes the project concept. Executive Summary: A brief overview of the project, its importance, and the proposed approach. Objectives: Specific goals the project intends to achieve. Scope: The boundaries of the project, including what will and won’t be included. Methodology: The approach, techniques, and procedures that will be used to carry out the project. Budget: An estimate of the costs involved in the project. Timeline: A schedule outlining key phases and milestones. Conclusion/Call to Action: A compelling conclusion that encourages stakeholders to approve the proposal.

BAKHAT ALI Bakhtali21uaar@gmail.com 72 Project Report A retrospective document detailing what occurred during a project, its outcomes, and lessons learned. Key Components : Title Page: The title of the project, participants, and date. Executive Summary: A concise summary of the project and its outcomes. Introduction: Background information and the context of the project. Objectives: The goals that were set at the beginning. Methodology/Approach: A description of how the project was executed. Results and Findings: Data and information about the outcomes of the project. Analysis: An evaluation of the project’s success, including what worked well and what didn’t. Lessons Learned: Insights gained during the project that can inform future projects. Recommendations: Suggestions for future actions or improvements based on the project experience. Conclusion: A final summary of the project’s significance and outcomes.

BAKHAT ALI Bakhtali21uaar@gmail.com 73 Network Analysis using GIS Techniques BAKHAT ALI Institute of Geoinformatics and Earth Observation, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi , Punjab, Pakistan bakhtali21uaar@gmail.com Abstract: This study harnesses ArcGIS-based network analysis to evaluate the efficiency of essential services, including hospitals, schools, and fire stations, in Muzaffargarh, Pakistan. By leveraging high-resolution Google Earth imagery and advanced geo-referencing techniques, we meticulously digitized the city's intricate road network and pinpointed service locations. The network analysis tool was then employed to quantify service efficiency, focusing on critical metrics such as travel time and distance. This detailed analysis revealed significant spatial discrepancies in service allocation, providing actionable insights and strategic recommendations for optimizing the distribution of these vital services to enhance accessibility and efficiency for the city's residents. Furthermore, the study identifies areas with inadequate service coverage and suggests targeted interventions to address these gaps. By implementing these recommendations, local authorities can improve emergency response times, ensure equitable access to education and healthcare, and ultimately foster a more resilient and well-served community. This innovative approach serves as a model that can be adapted and applied to other cities facing similar challenges worldwide, setting a benchmark for urban service efficiency. Keywords: Efficiency, GIS, Network Analysis, Optimization, Services, Accessibility.

BAKHAT ALI Bakhtali21uaar@gmail.com 74 Contents Abstract:.....................................................................................................................................73 1 Introduction: ........................................................................................................................74 1.1 Aim:...................................................................................................................................74 1.2 Objective:...........................................................................................................................74 2 Methodology...........................................................................................................................75 2.1 FLOWCHAR:................................................................................................................75 2.2 Study Area:........................................................................................................................76 2 .3 Datasets:...........................................................................................................................77 3 Material and Methods............................................................................................................77 3.1 Geo-data base Creation...................................................................................................77 3.2 Data processing..................................................................................................................78 3.3Network Analysis:...............................................................................................................79 4 Results :....................................................................................................................................81 5 Discussion: ...............................................................................................................................82 6 Conclusion:...............................................................................................................................82 References:.....................................................................................................................................82 1 Introduction: Geographic Information System (GIS) integrates spatial and non-spatial data for comprehensive analysis, supporting urban planning and transportation management. The ArcGIS Network Analyst extension provides advanced network-based spatial analysis, facilitating efficient travel routing, service area definition, and resource allocation. 1.1 Aim: To conduct a comprehensive network analysis of Muzaffargarh city utilizing advanced GIS techniques to enhance urban planning and service delivery. 1.2 Objective: • To identify the most efficient travel routes within Muzaffargarh.

BAKHAT ALI Bakhtali21uaar@gmail.com 75 • To define optimal service areas for different facilities such as hospitals, schools, etc. 2 Methodology 2.1 FLOWCHAR: Figure 1: Enhanced Network Analysis Process Flow Diagram

BAKHAT ALI Bakhtali21uaar@gmail.com 76 2.2 Study Area: Muzaffargarh, located in the southwestern region of Punjab, Pakistan .Muzaffargarh, Punjab, Pakistan is located at Pakistan country in the Cities place category with the GPS coordinates of 30° 4' 27.7572'' N and 71° 11' 4.7544'' E. Figure 2: Muzaffargrh study area map

BAKHAT ALI Bakhtali21uaar@gmail.com 77 2 .3 Datasets: Data acquisition for this study involved the collection of satellite imagery, road network data, and point data for facilities such as hospitals, education data , and popular place Muzaffargarh. High-resolution satellite imagery from sources like Google Earth was obtained and geo-referenced to ensure accuracy in spatial analysis. Road network data, including information on road types, traffic flow, and connectivity, were sourced from local government agencies and digital maps. 3 Material and Methods 3.1 Geo-data base Creation For generating Geo-database following data has been used.

BAKHAT ALI Bakhtali21uaar@gmail.com 78 Figure 3: Network analysis database • Chandigarh city base map using Google imagery • City road network Shape file • Shape file of public services such as hospital, schools, colleges and fire station. 3.2 Data processing For the data processing following steps were taken:

BAKHAT ALI Bakhtali21uaar@gmail.com 79 Figure 4:Network GEO-DATASET • Geo-referencing of MUZAFFARGRH BASEMAP (World Street Map). • Generation of Shape file of hospitals, education and ,populated _place. • Digitization of road network • Generate topology • Generating Network Geo-dataset 3.3Network Analysis:

BAKHAT ALI Bakhtali21uaar@gmail.com 80 ❖ Set time and distance: Figure 5:Sat time and distance • Add Fields distance ,time and speed • Calculate distance in meters • Give Speed =40000 m (car speed) • TIME= distance /speed/60 minutes ❖ Network data sat: Figure 6:Network DATASET • Created NETWORKS DATASAT • ADD FERATH CLASS Road ‘hospitals, education and ,populated _place.

BAKHAT ALI Bakhtali21uaar@gmail.com 81 4 Results : Figure 7: Muzaffargrh _network Analysis

BAKHAT ALI Bakhtali21uaar@gmail.com 82 5 Discussion: There is therefore the possibility of using a network analysis based on GIS to calculate the time and cost of travel between various places within Muzaffargarh District, taking into consideration the shortest distances between two points. Several other heads have also been described which are deemed crucial crossing over the ChenabRiver namely the Head Sulaiman Bridge, Rangpur Head, Jhang Head, Kotla Head, Ghazipur Head, Khurrianwala Head, Bait Mirza Head, Sultanpur Head, Ali Pur Head and last but not lease Basit Pur Head. The network analysis starts by mapping out all possible routes between Muzaffargarh City and the neighboring towns: Namely; Multan, Alipur, Kot Addu, Jatoi South, Sitapur, Dera Ghazi Khan, Pay-Jamal, and Munda Road. Each proposed route is rated according to the distances that a car would need to travel, average traffic conditions, and possible tolls or crossing fees. For example, the travelling time between Muzaffargarh and Multan might be lesser through a route covering the Head Sulaiman Bridge owing to its less number of curves and proper construction. On the other hand, reaching Alipur may take lesser time through Ali Pur if it is further away and the traffic signal problems are less. It also helps in exploring specific routes that might be less time consuming and cheaper when it comes to travelling and fortifying a network in Muzaffargarh District. In order to identify whether present facility in Muzaffargarh District provides adequate and satisfactory infrastructural and utilities services a survey was conducted on the present state of Hospitals, Schools and Colleges and other built up places and known localities was done on the basis of time and distance. This entailed geographically overlaying the various service delivery areas for schools, colleges, and universities to ascertain the accessibility of service delivery in relation to time buffers to enhance the delivery coverage. Hospitals were also placed under scrutiny, whereby service areas were determined to reveal areas that require faster response to emergency cases or health care facility availability. In order to evaluate the canopy and community engagement of ubiquitous locations, certain everyday places which are congested with people were selected including parks, markets and cultural hubs 6 Conclusion: Defined the network analysis using GIS techniques has provided valuable insights into the efficiency of services and transportation (short routing )infrastructure in Muzaffargarh. By identifying areas located in for improvement and optimization, this study can inform urban planning decisions aimed at enhancing service delivery and improving the overall quality of life for residents. References: [1] http://chandigarh.gov.in/knowchd_general.htm [2] Facility, Closest, and Service Area Analysis. “ArcGISNetwork Analyst.” [3] Fang, Kun, Polygon Based Model, and Xu Yiqin. “Gis Network Analysis in Rescue of Coal Mine.” (2001) [4] http://help.arcgis.com/en/arcgisdesktop/10.0/pdf/network-analyst-tutorial.pdf [5] Smith, Richard C, David L Harkey, and Bobby Harris. “Implementation of GIS-Based Highway SafetyAnalyses : Bridging the Gap.” January (2001) [6] http://webhelp.esri.com/arcgisdesktop/9.2/pdf/Network_Analyst_Tutorial.pdf

BAKHAT ALI Bakhtali21uaar@gmail.com 83 Flood Extent and Disaster Management in DG Khan Division, Pakistan: A GIS-Based Approach BAKHAT ALI Institute of Geoinformatics and Earth Observation, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi , Punjab, Pakistan bakhtali21uaar@gmail.com Abstract: This paper shall be able to show that while work flood have measures to avoid them, flooding is one of the most severe natural disasters in the world and is a reoccurring disaster that highly impacts Pakistan depending on its geographical location and weather conditions. The most threatened province is the Punjab particularly the Southeast of the study area, which covers the Sulaiman Mountain Range of DG Khan Division that receives the Indus River system flood zone; as a result, this research employed GIS techniques to systematically study and evaluate the flood behavior within the entire DG Khan Division. objectives: . The following can be generated as factors that could be useful in achieving the management goals of attaining effective and accurate strategies for managing the impacts of flood Disaster warnings that are also important when it comes to occurrence of disasters Structural patterns of disasters which are important since they will help Structure Disaster which relates with the mitigation process in disaster. Therefore, despite the fact that the study has chosen the notion of the recommendation of measures of flood management as its major conclusion, it is in fact designed to draw people’s attention to the understanding of the fact that it is important to think about the possibility of the fact that

BAKHAT ALI Bakhtali21uaar@gmail.com 84 the dynamics of such a flood type may be useful in terms of the planning of an adaptive strategy for a future event and real flood control, which should be practiced in other such areas Keywords: GIS techniques are flood risk analysis; Flood susceptibility mapping; and Remote sensing and GIS based flood zonation. Contents Flood Extent and Disaster Management in DG Khan Division, Pakistan: A GIS-Based Approach.83 Abstract:.....................................................................................................................................83 1. Introduction............................................................................................................................85 1.1 Aim....................................................................................................................................85 1.2 Objectives ..........................................................................................................................85 2. Methodology............................................................................................................................86 2.1 Process Flow Diagram........................................................................................................86 2.2 Study Area.........................................................................................................................87 2.3 Datasets..............................................................................................................................88 3. Material and Methods.............................................................................................................89 3.1 Geo-database Creation.......................................................................................................89 3.2 Data Processing..................................................................................................................90 4. Results.....................................................................................................................................90 4.1 Land Use Land Cover (LULC) Analysis ............................................................................90 4.2 Slope Analysis....................................................................................................................91 4.3 Watershed Characteristics.................................................................................................92 4.4 Digital Elevation Model (DEM) Analysis..............................................................................93 4.5 Flood Extent Mapping .......................................................................................................94 5. Discussion................................................................................................................................95 6. Conclusion...............................................................................................................................96 References...................................................................................................................................96

BAKHAT ALI Bakhtali21uaar@gmail.com 85 1. Introduction Flooding is a recurrent natural disaster that poses significant threats to regions globally, with Pakistan being notably vulnerable due to its diverse and complex terrain. The Dera Ghazi Khan (DG Khan) Division in Punjab, Pakistan, is particularly prone to severe floods due to its unique geographical and climatic conditions. The frequent flooding in this region results in profound socio-economic impacts, underscoring the need for effective disaster management strategies. Geographic Information Systems (GIS) provide powerful tools for assessing, mapping, and visualizing flood extents, which are essential for the development of robust flood management plans. This study aims to leverage GIS technology to analyze flood risks and propose strategic interventions to mitigate the impacts of flooding and enhance preparedness and response mechanisms within the DG Khan Division. 1.1 Aim The primary aim of this project is to analyze the flood extent and develop disaster management strategies for the DG Khan Division using GIS-based methodologies. 1.2 Objectives The specific objectives of this study are to: ▪ Map Flood Extents: Utilize historical flood data and satellite imagery to create detailed flood extent maps. ▪ Identify Flood-Prone Areas: Analyze key factors contributing to flooding, including land use, topography, and watershed characteristics. ▪ Propose Disaster Management Strategies: Recommend effective measures to mitigate flood impacts and enhance resilience. ▪ Enhance Preparedness and Response: Improve mechanisms for flood preparedness and emergency response to future flood events.

BAKHAT ALI Bakhtali21uaar@gmail.com 86 2. Methodology 2.1 Process Flow Diagram The methodological approach for this study involves a systematic process encompassing data collection, geodatabase creation, data processing, flood extent mapping, and the formulation of disaster management strategies. This process is visualized in a flow diagram that outlines the sequential steps undertaken in the analysis: Data processing LULC Slope Watershed DEM Generate Maps FLOOD EXTENT end DATASAT Flood historical data ASTER DEM (Grid) 30 m LULCdata (Grid) 10 m Landsat8 Start FLOOD EXTENT LULC Slope Watershed DEM

BAKHAT ALI Bakhtali21uaar@gmail.com 87 Figure 1: Disaster Management Process Flow Diagram 2.2 Study Area The DG Khan Division is located in the southwestern region of Punjab, Pakistan, spanning from 28°30' to 30°30' North latitude and 69°30' to 71°15' East longitude. The area features a varied landscape, including river systems, agricultural lands, and urban areas. Its topography and climate significantly influence its vulnerability to flooding, making it a critical region for flood risk assessment and management.

BAKHAT ALI Bakhtali21uaar@gmail.com 88 Figure 2: DGKHAN_DIVISION study area map 2.3 Datasets The study utilized a diverse range of datasets from various authoritative sources to conduct a comprehensive flood analysis. These datasets include: S. No Type of data Source of extracted data Extracted data I Flood historical data NDMA, PDMA, and Irrigation Department Past flood events II ASTER DEM (Grid) 30 m × 30 m resolution NASA’s official website https://search.earthdata. nasa.gov Hillshade, Slope, Elevation, Curvature, Drainage Density, and TWI III LULC data (Grid) 10 m × 10 m resolution ESRI 2020 data, https://livingatlas.arcgis.com/landcover/ Land use/land cover map IV Landsat8 Imagery (band5, band4) USGS official website https://earthexplorer.usgs.gov NDVI map

BAKHAT ALI Bakhtali21uaar@gmail.com 89 V Precipitation (TRMM data) NASA’s official website https://giovanni.gsfc.nasa.gov/ giovanni/ Rainfall map Table1 : Datasets 3. Material and Methods 3.1 Geo-database Creation A geo-database was established to manage and store the spatial and attribute data relevant to flood extent analysis. This database serves as a central repository for organizing the diverse datasets required for detailed GIS analysis and flood mapping. Figure 3: Disaster management database

BAKHAT ALI Bakhtali21uaar@gmail.com 90 3.2 Data Processing The raw datasets were processed into formats suitable for GIS analysis. Key processing steps included: Raw datasets were converted into usable formats and rasterized, including elevation, slope, drainage density, LULC, NDVI, and rainfall data. . 4. Results The flood extent map revealed: Significant areas in the southern parts of the DG Khan Division are frequently flooded due to land use land cover ,slope, watershed, lower elevations and flatter terrain.. 4.1 Land Use Land Cover (LULC) Analysis LULC analysis reveals that different land cover types significantly influence flood risk. Water bodies, such as rivers and streams, are primary sources of floodwaters. Urban and built-up areas, characterized by impermeable surfaces, are particularly vulnerable due to reduced infiltration and increased runoff. Vegetation and barren lands also play significant roles in modulating runoff and flood dynamics

BAKHAT ALI Bakhtali21uaar@gmail.com 91 Figure 4: land use land cover map 4.2 Slope Analysis Slope analysis indicates that terrain steepness significantly affects water runoff and flood risk. Areas with steeper slopes experience faster and more concentrated runoff, increasing the risk of flooding in downstream regions. Conversely, flat or gently sloping areas tend to accumulate water, leading to prolonged flooding and waterlogging issues.

BAKHAT ALI Bakhtali21uaar@gmail.com 92 Figure 5: Slope map 4.3 Watershed Characteristics The characteristics of watersheds, including their size, shape, and elevation, are crucial in determining flood behavior. Large and complex drainage basins often have a higher capacity to collect and convey floodwaters, thereby influencing the severity and extent of flooding. Understanding these dynamics helps in predicting flood patterns and planning effective mitigation measures.

BAKHAT ALI Bakhtali21uaar@gmail.com 93 Figure 6: watershed map 4.4 Digital Elevation Model (DEM) Analysis The DEM provides a detailed three-dimensional representation of the terrain, essential for flood risk assessment. Low-lying areas identified through the DEM are particularly susceptible to flooding due to water accumulation.

BAKHAT ALI Bakhtali21uaar@gmail.com 94 The DEM also aids in understanding the flow paths of floodwaters, crucial for developing flood mitigation and management strategies. Figure 7: 3D DEM 4.5 Flood Extent Mapping The flood extent map developed in this study highlights areas within the DG Khan Division that are highly prone to flooding. Analysis indicates that the southern parts of the division are most frequently affected due to their lower elevation and relatively flat terrain. These regions, often encompassing agricultural lands and urban centers, require targeted disaster management strategies to reduce flood impacts.

BAKHAT ALI Bakhtali21uaar@gmail.com 95 Figure 8: FLOOD EXTENT MAP 5. Discussion The flood extent analysis provides crucial insights into the areas most at risk of flooding in the DG Khan Division. Implementing effective disaster management strategies is essential to mitigate the impacts of flooding, protect lives, and reduce economic losses

BAKHAT ALI Bakhtali21uaar@gmail.com 96 6. Conclusion This study successfully mapped the flood extents in the DG Khan Division and proposed comprehensive disaster management strategies to mitigate flood impacts. By integrating GIS technology, the study provides a robust framework for understanding flood dynamics and enhancing preparedness and response mechanisms. The findings offer valuable insights for policymakers and planners to improve flood management practices. Future research should focus on incorporating real-time data and advanced modeling techniques to further refine flood prediction and management capabilities. References [1] Abbas, A., Bhatti, A. S., Ullah, S., Ullah, W., Waseem, M., Zhao, C., et al. (2023). Projection of precipitation extremes over South Asia from CMIP6 GCMs. Journal of Arid Land, 15, 274–296. doi:10.1007/s40333-023-0050-3 [2] Abbas, A., Ullah, S., Ullah, W., Waseem, M., Dou, X., Zhao, C., et al. (2022). Evaluation and projection of precipitation in Pakistan using the coupled model intercomparison project phase 6 model simulations. International Journal of Climatology, 42, 6665–6684. doi:10.1002/joc.7602 [3] Ahmad, I., Tang, D., Wang, T., Wang, M., & Wagan, B. (2015). Precipitation trends over time using Mann-Kendall and Spearman’s rho tests in Swat River Basin, Pakistan. Advances in Meteorology, 2015, 1–15. doi:10.1155/2015/431860 [4] Ahmad, I., Zhang, F., Liu, J., Anjum, M. N., Zaman, M., Tayyab, M., et al. (2018). A linear bi-level multi-objective program for optimal allocation of water resources. PLOS ONE, 13, e0192294. doi:10.1371/journal.pone.0192294 [5] Ali, K., Bajracharya, R. M., & Koirala, H. (2016). A review of flood risk assessment. International Journal of Environment, Agriculture and Biotechnology, 1, 1065–1077. doi:10.22161/ijeab/1.4.62

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