::::{grid} auto :::{grid-item-card} :class-card: sd-text-center sd-rounded-circle :link: https://giscience.github.io/gis-training-resource-center/english/content/en/intro.html {octicon}`home-fill;1.5em;sd-text-danger` ::: :::: # Exercise 4: Security Peshawar :::{card} __Aim of the exercise:__ ^^^ In this exercise, we will create a security map for a humanitarian team. In response to a recent cholera outbreak in Khyber Pakhtunkhwa (KP), the Pakistan Red Crescent Society (PRCS) and other Partner National Societies (PNS) have mobilized a team to this region. The primary objective of the team is to conduct a comprehensive assessment on the ground and facilitate coordination for any necessary future responses. In order to ensure the safety of our team, we will create a map displaying security related information such as bomb threats. Additionally, we will take a look at data on armed conflicts and create an overview map showing the conflicts per thesil (subdistrict/ADM3). In order to achieve this, we will use more advanced geoprocessing tools such as buffers and intersection tools and join two datasets using attribute table joins. The exercise is split into __three tasks__: - In the first task, we will digitise security-related information as point data and create buffer zones to indicate high-risk zones. - In the second task, we will load a csv-file with data on conflict events that happened in Pakistan. We will use this dataset to find out the number of conflict events per thesil (subdistrict). - In the third task, we will load another csv-file with data on the multi-poverty-index (MPI) per district (ADM2). The Multidimensional Poverty Index (MPI) measures poverty by looking at more than just income. It includes things like education, health, and living standards to understand how people are poor in different ways. The csv-file does not contain explicit spatial information. We will add spatial information by joining it to a polygon layer containing the district boundaries. ::: ::::{grid} 2 :::{grid-item-card} __Larkana Flood Response Exercise Track:__ ^^^ This exercise is part of the [Larkana Flood Response Exercise Track](https://giscience.github.io/gis-training-resource-center/english/content/en/Exercise_tracks/en_larkana_flood_response.html). However, the exercise __can__ be done without completing the previous exercises. ::: :::{grid-item-card} __These skills are relevant for__ ^^^ - QGIS Essentials - Working with multiple layers - Conduct spatial queries - Creation of geodata ::: :::: ::::{grid} 2 :::{grid-item-card} __Estimated time demand for the exercise:__ ^^^ - The exercise takes around 3 hours to complete, depending on the number of participants and their familiarity with computer systems. ::: :::{grid-item-card} __Relevant wiki articles:__ ^^^ * [Geodata Import in QGIS](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_import_geodata_wiki.html) * [Layer Concept](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_layer_concept_wiki.html) * [Geodata Classification- Categorized](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_categorized_wiki.html) * [Digitisation- Point data](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_digitisation_wiki.html#add-geometries-to-a-layer) ::: :::: ## Instructions for the trainers :::{dropdown} __Trainers Corner__ ### Prepare the training - Take the time to familiarise yourself with the exercise and the provided material. - Prepare a white-board. It can be either a physical whiteboard, a flip-chart, or a digital whiteboard (e.g. Miro board) where the participants can add their findings and questions. - Before starting the exercise, make sure everybody has installed QGIS and has downloaded __and unzipped__ the data folder. - Check out [How to do trainings?](https://giscience.github.io/gis-training-resource-center/english/content/en/Trainers_corner/en_how_to_training.html#how-to-do-trainings) for some general tips on facilitating trainings. ### Conduct the training __Introduction:__ - Introduce the idea and aim of the exercise. - Provide the download link and make sure everybody has unzipped the folder before beginning the tasks. __Follow-along:__ - Show and explain each step yourself at least twice and slow enough so everybody can see what you are doing, and follow along in their own QGIS-project. - Make sure that everybody is following along and doing the steps themselves by periodically asking if anybody needs help or if everybody is still following. - Be open and patient to every question or problem that might come up. Your participants are essentially multitasking by paying attention to your instructions and orienting themselves in their own QGIS-project. __Wrap up:__ - Leave time for any issues or questions concerning the tasks at the end of the exercise. - Leave some time for open questions. ::: ### Available Data :::{card} :link: https://nexus.heigit.org/repository/gis-training-resource-center/Modul_5/Exercise_4_Security_Peshawar/Modul_5_Exercise_4_Security_Peshawar.zip __Download all datasets [here](https://nexus.heigit.org/repository/gis-training-resource-center/Modul_5/Exercise_4_Security_Peshawar/Modul_5_Exercise_4_Security_Peshawar.zip) and save the folder on your computer and unzip the file.__ ::: | Dataset name| Original title|Publisher|Download from| | :-------------------- | :----------------- |:----------------- |:----------------- | | 2024-01-01-2024-09-23-Pakistan.xlsx |Conflict data for Pakistan 01/2024-09/2024 |ACLED| HDX | | PAK_KP_admin_3.gpkg |Administrative Boundaries level 3 of KP |UN OCHA | HDX | | Pak_adm2_Khyber Pakhtunkhwa.gpkg |Administrative Boundaries level 1 of KP |UN OCHA | HDX | |20240605_PAK_MPI.xlsx|Pakistan Multi Poverty Index (MPI)|Pakistan Bureau of Statistics|Pakistan Bureau of Statistics| |AOI_Peshawar.gpkg|Area of Interest (AOI) around Peshawar||| ## Preparation - Create a new QGIS project and save it into the exercise folder. ## Task 1: Geolocate security-related information of the last days :::{card} Context: In response to a recent cholera outbreak in Khyber Pakhtunkhwa (KP), the Pakistan Red Crescent Society (PRCS) and other Partner National Societies (PNS) have mobilized a team to this region. The primary objective of the team is to conduct a comprehensive assessment on the ground and facilitate coordination for any necessary future responses. The team will be staying at the "Roomy Crossroad Hotel Peshawar" during their mission in the affected area. We have received security-related information from various sources. Our task is to digitise this information into spatial data that can be represented on a map. ::: For this exercise, we will use the QuickMapServices plugin to locate places precisely. Follow the steps below to install it. 1. In the menu bar, click on `Plugins` → `Manage and Install Plugins…` 2. Under `All`, search for “QuickMapServices” and click `Install Plugin`. For general plugin management in QGIS, see the [QGIS plugin installation guide](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_plugins_wiki.html#installation-of-plugins). After installing the plugin, we can add basemaps: 3. In the menu bar, navigate to `Web` → `QuickMapServices` → `ESRI` → `ESRI Satellite`. This adds a satellite imagery basemap to the Layers Panel. 4. Add a roads layer to improve orientation: `Web` → `QuickMapServices` → `Google` → `Google Road`. 5. In the [Layers Panel](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_layer_concept_wiki.html), make sure the `Google Road` layer is above the satellite imagery. 6. For easier navigation, make the satellite imagery transparent: - Open the layer’s [Symbology tab](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_4/en_qgis_styling_vector_data.html#styling-panel). - Adjust the global opacity. 7. 7. We will need the "Lat Lon Tools" plugin to locate the coordinates we receive from the field. Install the plugin by following the same steps as for QuickMapServices, but search for "Lat Lon Tools" instead. Great, now we have set up our QGIS-project and can start to digitise the known incidents and related areas of interest (as polygons). Below, you will find a table with the information and locations. Read through the table once and think about what kind of geometry type can represent the information. | Number| Description | | :-------------------- | :----------------- | |1 | A __bomb threat__ including Improvised Explosive Devices (IED) on the road N45 right between __Seri-Bahlol__ and Tableeghi Markaz Mardina near Jandy has been reported by local radio stations. Please mark the area between the communities along the road as no-go areas. | |2 | In a conversation with a PRCS driver, a local teacher shared that the vicinity of the __Government Girls Primary School in Takkar__ is notorious for being a minefield. Specifically, the fields between the school and the __Noormuhmmad hospital__ are known to be heavily mined. Due to this danger, local farmers are extremely reluctant to work on this particular piece of land.| |3 |Following the recent events, it has been decided that the vicinity surrounding the __Arbab Niaz Cricket Stadium Peshawar__ is now designated as a no-go area for all staff members. This area encompasses the region bordered by the __N5__ road to the south, the __Afghan Colony road__ to the north and east, and the __Charsadda Road__ to the west.| |4 | GPS Coordinates: __(33.99519949518549, 71.66217873936723)__. A humanitarian worker was transporting medical supplies through the region when they were approached by a local farmer. The farmer mentioned that a nearby abandoned well, located at these coordinates, has become a gathering point for __unexploded ordnance__. Due to its proximity to a residential area, this site is now flagged as high-risk and needs immediate attention from demining teams.| |5 | GPS Coordinagtes: __(34.02878398623702, 71.43081737211224)__. A health unit here has recently been vacated after a bomb threat was called in. Police and bomb squads searched the premises but didn’t find any explosives. However, local business owners reported unusual activity around the building in the weeks leading up to the threat. This location is now under surveillance. The area between the health unit and the river, as well as the parks and playground behind it, need to be marked as a temporary no-go zone. | |6 | __Qissa Khwani Bazaar, Peshawar__: A popular historical market at this address has become a focal point for local community gatherings, but recent intelligence reports suggest that the site could be at risk for political protests that have turned violent in the past. Authorities are now considering setting up temporary barriers to manage the flow of people, and it's crucial that this location is marked as a high-risk area for potential crowd control measures.| In order to digitise the information, we will need two new layers: A point layer and a polygon layer. In case the information states an exact area, create a new polygon layer and map it exactly. 8. [Create a new point and a new polygon layer](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_3/en_qgis_digitissation.html#creating-new-datasets) to digitise point and polygon information. :::{tip} When creating the point and polygon layer use the CRS UTM 42 N __EPSG: 32642__. This Coordinate reference system is ideal for Pakistan and the __units of measurement are in meters__. ::: 9. Find the locations from the table and create new features using the [digitisation toolbar](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_3/en_qgis_digitisation.html#digitisation-toolbars). Capture the information in the table and Use Google or another search engine in your browser, the base map, and the Lat Lon Tools plugin to locate the exact position. 10. Once you are done, make sure to save the edits to your layers by clicking on the ![](../../../fig/3.44_digitisation_save_edits.png) `Save Layer Edits`-button. Great, we have successfully digitised the important security information. However, to make the information useful, we need to process the geodata. The current SOP states that the sides of recent violent incidents are to be avoided in a 1 km radius. To reflect this on the map, we will use the ![](../../../fig/mAlgorithmBuffer.png) buffer tool. ::::{margin} {admonition} Reminder :class: seealso If you see a ![](../../../fig/3.44_caution_symbol.png)-symbol next to the `Distance` parameter, this means that the layer you want to buffer is in a geographic coordinate reference system. This means the units of measurements are in __degrees, and not in meters__. If that is the case, you need to [reproject your layer](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_projections_wiki.html#changing-the-projection-of-a-vector-layer) into a metric coordinate reference system. Tip: EPSG:32642 is an ideal CRS for Pakistan. :::: 6. [Create a buffer around the points](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_geoprocessing_wiki.html) of violent incidents with a distance of __2.000 meters__. 7. We now have two polygon layers (the point buffers and the digitised areas). In order to visualise the No-Go Area in Peshwar, we can merge the polygons from both layers into a single layer with the tool `Merge vector layer` and select both layers we want to merge as inputs. 8. Load the layer `AOI_Peshawar.gpkg` into your project. ::::{margin} :::{tip} There are several clipping algorithms to be found in the toolbox. Make sure to choose the ![](3.44_clip_vector.png) clipping tool grouped under "Vector overlay". ::: :::: 9. We want to create a polygon indicating the safe area where the team is allowed to travel. To do this, we need to "invert" the polygon. We can achieve this by using the tool `Symmetrical difference`: - In the processing toolbox, search for the tool "Symmetrical difference" and open it. - As `Input Layer`, select your layer containing the security information. - As `Overlay Layer`, select the layer `AOI_Peshawar.gpkg`. - Under `Symmetrical Difference`, click on `...` and navigate to the `data/results/`-folder for this exercise and save the layer. - Click `Run`. 10. Let's do a quick symbolisation of the resulting layer so we can understand the information more easily: - Open the [styling panel](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_4/en_qgis_styling_vector_data.html#styling-panel) for the safe area. - [Adjust the symbology](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_4/en_qgis_styling_vector_data.html#only-display-the-outlines-of-polygons) for the layer so that the polygons are __semi-transparent and green__. Congratulations! We now have a map to help the team on the ground stay safe. ## Task 2: Load Excel File containing conflict data into QGIS :::{card} __Context:__ ^^^ In this step, we want to get a broader overview of conflict events in Pakistan. The [Armed Conflict Location and Event Data project (ACLED)](https://acleddata.com/) distributes datasets on conflict events per country as excel spreadsheets. The datasets include dates, actors, __locations__, fatalities, and types of reported conflict events. The dataset also includes geographic coordinates for each reported event. In QGIS, we can transform the datatable into a spatial dataset, such as a GeoPackage. Once we have the conflict events as point data, we can aggregate the data on subdistrict level and create a map showing the subdistricts (ADM3) most affected by conflict. ::: 1. Drag and Drop the ACLED conflict excel table `2024-01-01-2024-09-23-Pakistan.xlsx` into your QGIS project. 2. In the processing toolbox, find the tool `Create points layer from table` and open it. - As `X Field` select the column "longitude", and as `Y Field` select "latitude". - Under `Points from Table` , click on the three dots, choose 'Save to GeoPackage' and navigate to you temp folder. Save the layer with the name "Pak_Conflict_points_2024". - Click `Run`. :::{figure} ../../fig/Create_ponts_from_table.PNG --- width: 700px name: SVG Marker --- Create points from table. ::: Great! We now have a point layer showing the conflict events in Pakistan. Now, we could investigate this dataset further by taking a look at the [attribute table](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_2/en_qgis_attribute_table.html) and see what kind of information is represented. However, we want to know the number of conflict incidents per thesil (subdistrict/ADM3). To calculate this: 3. Import the layer `PAK_KP_admin_3.gpkg` from your `data/input`-folder into your QGIS-project. 4. We want to count the number of conflict events per thesil (subdistrict/ADM3). To do this: - In the processing toolbox, find the tool `Count points in polygon` and open it. - As `Polygons`, select `PAK_KP_admin_3`-layer. - As `Point Input`, select the `PAK_Conflict_points_2024`. - Under `Count`, click on the `...` and save the layer in your `data/results/`-folder as "PAK_num_events_adm3". :::{figure} ../../fig/count_point_polygon.PNG --- width: 700px name: SVG Marker --- Count points in polygon ::: 5. Open the attribute of your 'Pak_num_events_adm3' layer and scroll to the right. You will find a column with the name "NUMPOINTS". Here you find the number of events per thesil. - Right-click on the layer and navigate to `Properties` → `Symbology`. A new window will open. At the top, change Single Symbol to "Graduated". - In the `Value` field choose "NUMPOINTS". - Then below click on `Classify`. - You can adjust the Mode and the number of classes if wanted. Also you can choose your preferred color ramp. You can try out different coloring options. - Once you are satisfied with the look, click `Apply` and then `OK`. Your result could look similar to this: :::{figure} ../../fig/Number_events_graduated.PNG --- width: 700px name: SVG Marker --- Number of conflict events per thesil. ::: ## Task 3: MPI data :::{card} __Context:__ ^^^ In this step, we want to visualise the Multi-Poverty index on district level (ADM2). This can help us understand the economic and social vulnerability geographically. The data with the MPI index is available in an excel spreadsheet. However, it does not contain columns with the specific geographic data (such as coordinates). We have to find a way to join the data table with a spatial dataset. ::: 1. First, let us open the spreadsheet with Microsoft Excel or a similar program (such as LibreOffice Calc): - Here, you can already investigate the information that is stored in the different columns. Can you identify columns that contain spatial information? - For now, let us export the file as a CSV UTF-8. * Click on `File` → `Save As.` * Chosse an output folder, where it will be saved (the `data` → `temp` folder is recommended here) and give the file a meaningful name, for instance __20240605_PAK_MPI__. * Choose the option __CSV UTF-8 (Comma delimited) (*.csv)__ and `Save`. :::{figure} ../../../fig/PAK_Excel_to_CSV.png --- width: 400px name: Convert Excel to CSV align: center --- Convert Excel to CSV ::: 2. Open QGIS and create a new project. Save the project in your project folder. 3. [Import the layers](https://giscience.github.io/gis-training-resource-center/english/content/en/Wiki/en_qgis_import_geodata_wiki.html) `__20240605_PAK_MPI.csv__` and `Pak_adm2_Khyber Pakhtunkhwa.gpkg` to QGIS: * Drag and drop the ADM2-layer into your QGIS-window. * To import the CSV-file, click on the `Layer` tab → `Add Layer` → `Add Delimited Text`. * Browse for your __20240605_PAK_MPI.csv__ file. * Choose the correct `File Fromat`: `Custom delimters` → `Semicolon`. * Go to the tab `Geometry Definition` and choose `No geometry`. We don't have a column with coordinates or geoemtry information, but only the admin2 name and P-Code. * Add layer and close the window. :::{figure} ../../../fig/PAK_Load_CSVfile.PNG --- width: 400px name: Load CSV file to QGIS align: center --- Load CSV file to QGIS ::: Now, we need to join the data to existing to the existing district boundaries (ADM2). This process is called a [non-spatial join](https://giscience.github.io/gis-training-resource-center/english/content/en/Module_5/en_qgis_spatial_tools.html#spatial-joins) and it allows us enrich datasets using attribute data. In our case, the MPI dataset contains a column with the district names (admin2) and the P-Codes. P-Codes are international codes for administrative boundaries and are generally the best way to identify an administrative units, as names can have several spellings. Our polygon-layer also has columns with the p-codes. So, we need to perform a non-spatial join using the P-Codes Columns as identifiers. To do this, 4. Open the attribute table of the attribute table and detect the column which contains the information you want to use to join the data with the location. E.g. City name, district name, or best the P-Code. In our case it is `ADM2_PCODE`. * __Hint__: Each administrative level and area contains a worldwide unique code number. This helps to determine the exact administrative boundary without misspelling the name of the area. 5. Now, we need the polygon layer with the administrative boundaries. * Open the attribute table for the layer __Pak_adm2_Khyber Pakhtunkhwa.gpkg__ and locate the column containing the P-Code. How is it called? 6. To link the two layers, open the Toolbox and search for the tool __Join attributes by field value__. Open it. * `Input layer`: __Pak_adm2_Khyber Pakhtunkhwa.gpkg__ * `Table field`: __admin2Pcode__ * `Input layer 2`: __20240605_PAK_MPI.csv__ * `Table field 2`: __ADM2_PCOCDE__ * Choose a location to save the file as GeoPackage and give it a meaningful name, for instance __MPI_Admin2_joined.gpkg__ * `Run` and close. :::{figure} ../../../fig/PAK_joined_MPI_csv_admin2.PNG --- width: 400px name: Join the districts with the MPI data align: center --- Join the districts with the MPI data ::: __Info__: You can see that not all areas are visible. Since we don't have data for all districts, only the districts were linked with the csv on which we have MPI data. :::{figure} ../../../fig/PAK_joined_MPI_csv_admin2_info.PNG --- width: 400px name: Information of not joined and linked data align: center --- Information of not joined and linked data ::: 5. Visualize __MPI_Admin2_joined.gpkg__ file: We have a new file, showing the district boundaries, but having the MPI information in the attribute table. The MPI value per district we now want to visualize. * Open the `Symbology` window of the file __MPI_Admin2_joined.gpkg__. * Decide which column you want to visualize. For instance the values of the year 2014 in the column __A_2014_15__. * Choose `Graduate` visualization. * Choose `Value` __A_2014_15__. * Click `Classify`. * Choose Mode `Pretty Breaks`. * Click `OK` and close the window. 6. Visualize __Pak_adm2_Khyber Pakhtunkhwa.gpkg__ layer for districts we don't have MPI data on. * Open the `Symbology` window of the file __Pak_adm2_Khyber Pakhtunkhwa.gpkg__. * Change the color, maybe to dark grey, so we can differentiate between the districts we have and don't have MPI data for. 7. Add OpenStreetMap as a baselayer for better orientation. :::{figure} ../../../fig/PAK_visualized_MPI.PNG --- width: 400px name: Visualized MPI data on district level align: center --- Visualized MPI data on district level. :::