Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

SEP Separates --- BR staff personalia --- BR institute --- GIS (geographic information system) --- plant populations --- mapping --- individual mapping

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data protection --- Environmental law --- Freedom of information --- Geographic information system --- Geospatial data --- Law and legislation --- Government policy

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Finland --- macrophytes --- aquatic ecosystems --- ecological survey --- climatic changes --- species richness --- distribution --- land use --- GIS (geographic information system) --- spatial analysis --- thesis --- full text online

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Sea level rise (SLR) due to climate change is a serious global threat. The scientific evidence is now overwhelming. Continued growth of greenhouse gas emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, the authors have assessed the consequences of continued SLR for 84 developing countries. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated global data on critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5m SLR. The results reveal that hundreds of millions of people in the developing world are likely to be displaced by SLR within this century, and accompanying economic and ecological damage will be severe for many. At the country level, results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (such as Vietnam, A. R. of Egypt, and The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (such as China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and the Middle East and North Africa exhibit the greatest relative impacts. To date, there is little evidence that the international community has seriously considered the implications of SLR for population location and infrastructure planning in developing countries. The authors hope that the information provided in this paper will encourage immediate planning for adaptation.

Agriculture --- Analysis --- Biodiversity --- Climate --- Climate Change --- Data Sources --- Environment --- Floods --- Geographic Information --- Geographic Information System --- Indicators --- Islands --- Land --- Land Use --- Observations --- Sea Level Rise --- Storms --- Studies --- Water Resources --- Waves --- Wetlands --- Wetlands Loss

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed into a resource for urban planning and development. The hypothesis is tested by creating road traffic crash location data, which are scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over age five and young adults. The research project scraped 874,588 traffic-related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. The project geolocated 32,991 crash reports in Twitter for 2012-20 and clustered them into 22,872 unique crashes to produce one of the first crash maps for Nairobi. A motorcycle delivery service was dispatched in real-time to verify a subset of crashes, showing 92 percent accuracy. Using a spatial clustering algorithm, portions of the road network (less than 1 percent) were identified where 50 percent of the geolocated crashes occurred. Even with limitations in the representativeness of the data, the results can provide urban planners useful information to target road safety improvements where resources are limited.

Big Data --- Geographic Information System --- Geospatial Analysis --- Machine Learning --- National Urban Development Policies and Strategies --- Road Safety --- Roads and Highways --- SDGS --- Social Media --- Spatial Clustering --- Transport --- Transport Economics Policy and Planning --- Urban Development --- Urban Economic Development --- Urban Planning