Jingxia Wang

Environmental Scientist | Urban ecologist, now at The University of Sheffield, UK 🇬🇧

[Jingxia Wang @Jingxia_Wang]

📢 After some paperwork and the implementation meeting, I’m pleased to kick off my newly funded Royal Society project🚀: UAV-assisted thermal Digital Twins (Thermal DT) for microscale climate change adaptation 🌍📡#RoyalSociety #ClimateChange #GIS #UAV

[Jingxia Wang @Jingxia_Wang]

From the booming growth in Tibet (+21.5%) to the stark decline in Heilongjiang (-16.9%), this map dives deep into provincial population changes from 2010 to 2020. #China #Population #GIS #DataVisualization

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge, Day 16 (Choropleth): I feel it’s a great chance to reflect on how populations are shifting across China. Where does your region stand? 🧐 Explore the changing population dynamics of China! 📊🌏

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge, Day 15 (My Data): Exploring Lijiang’s vibrant vegetation! 🌱This NDVI map, created with Sentinel-2 data in Google Earth Engine (GEE), highlights the lush greenery from Yulong to Huaping. A ‘review’ of a green journey through the highlands! #NDVI #Mapping #GEE

[Jingxia Wang @Jingxia_Wang]

used @ArcGISPro

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge. Day 14 (A world map): Following the map from Day 8, here’s an updated global view with normalised data. This map shows flood magnitude and population displacement density 1985–2010, with an inset highlighting high-impact areas in South Asia. #Flood #mapping

[Jingxia Wang @Jingxia_Wang]

Alternatively, I explored GoogleEarthPro and found it offers faster 3D visualisation when data is available, as demonstrated with examples in Sheffield and HK. #Digitalisierung #3Dmap #visualisation

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge—Day 13 (A New Tool):
3D visualisation in QGIS. This is Rotterdam, where our @USPSheffield L2 UG students will have the privilege of site visits to explore various Nature-Based Solutions for flood mitigation.

[Jingxia Wang @Jingxia_Wang]

While 3D tiled images in QGIS produce effective results, performance can depend on internet speed and server responsiveness, with occasional frame drops. @qgis @earth @Google #Google #earth Pro

[Jingxia Wang @Jingxia_Wang]

used @ArcGISPro #3DVisualisation #digitalmap

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge Day 12 (Time and Space): A 30-second ‘drunk’ bird’s-eye fly-through of a 3D campus, capturing your familiar routine spaces and routes. Made in ArcPro— though some buildings aren’t fully visualised due to the intense computational demands.

[Jingxia Wang @Jingxia_Wang]

It gives a glimpse of the Time-Space experience—I wish my CPU/GPU could handle more! Not sure, but it seems ArcScene might handle this kind of visualisation with less demand on the PC performance.

[Jingxia Wang @Jingxia_Wang]

Fascinating to learn the Lena River Delta is the largest in the Arctic, and it’s also possible to map Indigenous peoples and their languages across this vast region! 🌐❄️ #GIS #ArcticMaps #Geography #Mapping #Indigenous people

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge, Day 11 (Arctic): Finally got a chance to 🌍 explore the Arctic through #ArcGISPro! 🧊✨ Maps showcasing Arctic DEM, slope, bathymetry, and administrative areas.

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge Day 10 (Pen & Paper): AI-enhanced map of a demolished place: 3 metres to the National Park, 1 min walking distance to the best school, 5min walking distance to the market and city centre, and 10 min walking distance to the UNESCO heritage site – Dayan Old Town

[Jingxia Wang @Jingxia_Wang]

The maps are visually appealing but geographically distorted; with more prompting, more errors are introduced, highlighting the importance of learning GIS.

[Jingxia Wang @Jingxia_Wang]

#30DayMapChallenge, Day 8 (AI only). Prompt: Create a forest and vegetation map of Germany as if made with QGIS, including urban greenspaces, river networks, and shaded relief.

[Jingxia Wang @Jingxia_Wang]

AI: creatively introduced more errors, with multiple Berlins in the south and several Munichs scattered across Germany.