Saeed Makaremi’s Post

Day 30 - My Favorite... Exploring Amsterdam's Bicycle Rental Accessibility For the final day of the #30daymapchallenge, I'm excited to share one of my favorite maps! Isochrons, depicting accessibility based on time and network type, offer profound insights into urban accessibility analysis. For today's map, I decided to showcase the bike-friendly city of Amsterdam by visualizing Bicycle Rental Points. Utilizing isochrones based on walking time in 5, 10, and 15-minute intervals from each rental point, the map clearly illustrates the accessibility of these points across the city. As this marks the end of this fantastic month and my first year participating in this challenge, I want to express my gratitude to everyone who followed my journey. The learning experience has been invaluable, and I'm open to any further discussions about my maps. Here's to more mapping adventures ahead! 🗺️🚴♂️ #30daymapchallenge #urbanplanning #networkanalysis #transportation #mobility #walkability #accessibility #15minutecity #FavoriteMap #python #osm #osmnx #openstreetmap #Isochrons #Amsterdam #bicycle #bikesharing #dataviz #DataVisualization

Day 29 - Population: New Zealand's 2023 Population Density For Day 29, I present a map illustrating the Population Density of New Zealand in 2023. Leveraging the Kontur Population dataset, which provides population counts at a 400m resolution in H3 hexagons, I calculated and rendered the population density in QGIS and Blender, resulting in this lovely 3D map. Population density is a crucial metric for understanding the distribution of people across geographic areas. This map provides insights into the concentration of population in different regions of New Zealand. As we near the end of the #30daymapchallenge, each map has been a unique exploration, showcasing the diverse ways data visualization enhances our understanding of the world. #PopulationDensity #NewZealand #DataVisualization #Blender #QGIS #3D

Day 28 - Is this a chart or a map? Australian Cities Street Network Orientation For Day 28, I ventured into the study of Australian cities' street network orientation. Each city is represented by a polar histogram showcasing the compass bearings of its streets. The length of each bar reflects the relative frequency of streets in that direction. Analyzing the cities, we observe a predominant north-south-east-west layout, slightly angled to the right. Adelaide and Darwin stand out with precise north-south orientations. However, cities like Canberra and Hobart break the mold, featuring more evenly distributed street orientations, and challenging navigation for unfamiliar visitors. The Australian cities' data is sourced from OpenStreetMap, with the processing and diagram generation conducted through osmnx. Additionally, the basemap is crafted using DEM data of Australia and rendered using Blender. This map is inspired by Geoff Boeing's work on network orientation. I recommend delving into the associated article provided via the link for further insights: https://lnkd.in/dGd2EcMh #30daymapchallenge #OpenStreetMap #OSMnx #DEMData #Blender #UrbanAnalysis #NetworkAnalysis #Australia

Day 27 - Dot: Berkeley’s Street Network - Nodes Betweenness, Weighted by Travel Time For Day 27's theme of 'Dot,' I continued our exploration of Berkeley's street network, this time exploring the Betweenness Centrality of Nodes. Unlike yesterday's map, I integrated Speed Limit data into the analysis, calculating the travel time for each Edge. This map not only considers the position of Nodes but also weighs their significance based on the minimized travel_time attribute value. It provides a nuanced perspective on the importance of intersections in Berkeley's Street Network, factoring in the time it takes to traverse each Node. Leveraging the functionalities of the networkx and osmnx packages, and making use of OpenStreetMap data, this analysis provides a more holistic perspective on Network Centrality Analysis. #30daymapchallenge #Berkeley #StreetNetwork #NetworkAnalysis #Nodes #TravelTime #BetweennessCentrality

Day 26 - Minimal: Berkeley’s Street Network - Betweenness Analysis For Day 26, exploring the theme of Minimal, I decided to conduct another type of Network Centrality Analysis. If you recall my map on day 2, I performed a Centrality Analysis of Manhattan based on its Network's Closeness. Today's map specifically focuses on the Network's Betweenness Analysis. Both analyses highlight the nuanced importance of distinct edges through centrality analysis. Betweenness centrality specifically quantifies the proportion of all shortest paths in a network that traverse each edge. Closeness centrality gauges the average distance between an edge and all other edges in the network. Powered by the capabilities of networkx and osmnx packages and leveraging OpenStreetMap data of Berkeley's street network, this analysis paints a minimal yet profound picture of the structural significance of Berkeley's street network. #30daymapchallenge #NetworkAnalysis #CentralityMetrics #UrbanPlanning #Transportation

Day 25 - Antarctica: Antarctic Surface Lithology For today's theme, given the absence of any urban settlements in Antarctica, I opted to highlight the Surface Lithology of the continent. As part of my exploration in gaining proficiency with Blender, I incorporated the DEM as well. The current map features two layers of data: the digital elevation model sourced from the Polar Geospatial Center and the lithology data obtained from the GeoMAP dataset. GeoMAP is a collection of datasets comprising geological information for Antarctica, including both rock and surficial deposit information. They have been compiled by the GeoMAP team led by GNS Science. This work utilized co-funding from GNS Science's Nationally Significant Databases and Collections Programme (NSDC) and international collaboration through the SCAR GeoMAP project. Data Source: GeoMAP: https://lnkd.in/eSiuC_HK DEM: https://lnkd.in/eY-bQFqR #30daymapchallenge #Antarctica #SurfaceLithology #QGIS #3D

Day 24 - Back & White: Urban Form of Barcelona: 1.5 Square-km from Sagrada Família For Day 24 of #30daymapchallenge, I ventured into the captivating urban form of Barcelona, focusing on a 1.5 square-kilometer area around the iconic Sagrada Família. Using the OSMnx package to process building footprint data from OpenStreetMap, this black-and-white visualization offers a unique perspective on the city's architecture. Figure-ground diagrams like these are powerful tools in urban planning and design, revealing the intricate relationship between built and open spaces. Barcelona's urban fabric, elegantly showcased in monochrome, tells a story of architectural harmony. #UrbanForm #BuildingFootprint #blackandwhite #Barcelona

Day 23 - 3D: New Zealand's DEM Data On Day 23 of #30daymapchallenge, I explored 3D mapping with New Zealand's Digital Elevation Model (DEM) data. While navigating the learning curve of Blender for the first time, I was satisfied with the outcome. Yet, there's always room to enhance skills and explore limitless possibilities. 3D maps provide a visually stunning perspective, bringing geographical data to life in ways that traditional maps can't. Excited to explore more of these renderings in future projects. #3DMapping #NewZealand #DEM #Blender #QGIS

Day 22 - North is not always up: NASA's Black Marble Today's challenging prompt got me thinking that sometimes, the only way North isn't up is when you're sitting on the space station, witnessing the world below illuminated by city lights at night! NASA's Black Marble is a mesmerizing dataset, in which artificial lighting becomes the canvas. This unique perspective, derived from satellite images, unveils the human footprint. Remotely sensed lights at night have become a valuable data source, enhancing our understanding of the intricate interactions between human systems and the environment. This dataset is actively contributing to research on crucial topics like light pollution, illegal fishing, fires, disaster impacts and recovery, as well as human settlements and associated energy infrastructures. #30daymapchallenge #BlackMarble #NASA #NightLight

Day 21 - Raster: Urban Green Raster: Munich On Day 21, exploring the theme of Raster, I delved into a fascinating research dataset to visualize the Urban Green Raster of Munich. This detailed land cover classification, with a spatial resolution of 10 meters, focuses on urban vegetation areas. The dataset, derived from automated classification of Sentinel-2 satellite data during the full 2018 vegetation period, utilizes reference data from the European LUCAS dataset. This map identifies eight land cover classes, including Built-up, Coniferous wood, Water, and more, based on reference data from the European LUCAS land use and land cover point dataset. It's a testament to the power of remote sensing and data analytics in understanding and visualizing the urban environment. Data Source: https://lnkd.in/eVdMGcKG #30daymapchallenge #Raster #Urban #Greenery #Munich