diff --git a/docs/travel/my_maps.md b/docs/travel/my_maps.md
index 7032b19..7918a0d 100644
--- a/docs/travel/my_maps.md
+++ b/docs/travel/my_maps.md
@@ -1,3 +1,73 @@
+Understood. Let's focus on a workflow that revolves around managing points of interest (POIs) using Linux, Vim, and other technologies you're comfortable with, while leveraging your data analysis skills to enhance your travel experiences. Here's a tailored guide that incorporates your preferences:
+
+## Step 1: Data Collection
+
+1. As you come across interesting locations during your travel planning, create a simple text file using Vim to store the POI data. Open a terminal and run:
+
+   ```
+   vim travel_pois.txt
+   ```
+
+2. For each POI, enter the data in the following format:
+   ```
+   <name>;<latitude>;<longitude>;<description>;<category>
+   ```
+
+   For example:
+   ```
+   Eiffel Tower;48.8584;2.2945;Iconic landmark in Paris;Attraction
+   ```
+
+3. Save the file and exit Vim using `:wq`.
+
+## Step 2: Data Processing with Linux Commands
+
+1. Use Linux commands to process and analyze your POI data. For example, to count the number of POIs in each category, run:
+
+   ```
+   cut -d';' -f5 travel_pois.txt | sort | uniq -c
+   ```
+
+   This command extracts the category field, sorts the categories, and counts the occurrences of each unique category.
+
+2. To filter POIs by a specific category, use:
+
+   ```
+   grep "Attraction" travel_pois.txt
+   ```
+
+   Replace "Attraction" with the desired category.
+
+## Step 3: Geocoding and Visualization
+
+1. If you have POIs without latitude and longitude, use a geocoding API (e.g., Google Maps Geocoding API or OpenStreetMap Nominatim) to obtain the coordinates based on the address or place name. You can write a simple Python script to automate this process.
+
+2. Convert your processed POI data into a format suitable for visualization, such as CSV or GeoJSON. Use Vim to create and edit the file, and Linux commands or Python scripts to automate the conversion process.
+
+3. Import the converted file into QGIS for visualization and further analysis.
+
+## Step 4: Advanced Analysis
+
+1. Use QGIS to perform spatial analysis on your POI data, such as finding the nearest POIs to a specific location or calculating the density of POIs in different areas.
+
+2. Utilize the QGIS Processing Toolbox to run more complex analysis, like clustering POIs based on their attributes or creating heatmaps to visualize the concentration of POIs.
+
+3. Create custom QGIS plugins or scripts using Python to automate repetitive tasks or perform specialized analysis tailored to your travel needs.
+
+## Step 5: Integration and Collaboration
+
+1. Store your POI data, scripts, and QGIS project files in a version control system like Git to keep track of changes and facilitate collaboration with other travel enthusiasts.
+
+2. Use Linux tools like `cron` to schedule regular data updates or backups, ensuring your travel data remains current and protected.
+
+3. Collaborate with others by sharing your Git repository, allowing them to contribute their own POIs, scripts, or analysis techniques.
+
+By adopting this workflow, you can leverage your familiarity with Linux, Vim, and data analysis to create a powerful and personalized travel planning system. This approach allows you to collect, process, analyze, and visualize your POI data using tools you're comfortable with while still benefiting from the geospatial capabilities of QGIS.
+
+As you continue to refine your workflow, you can explore additional Linux tools, Python libraries, and QGIS plugins that further streamline your process and provide new insights into your travel data.
+
+---
+
 Understood. Given your preference for using Google My Maps as the primary source for your data and your goal of being well-prepared for your next travel, let's focus on a streamlined workflow that revolves around using Vim to manage your data and QGIS for visualization. Here's a step-by-step guide tailored to your needs:
 
 ## Step 1: Data Collection with Google My Maps