The development of a Regional Spatial Data Infrastructure (Regional SDI) is much more challenging than the development of a National SDI initiative within a nation. This is mainly because of the voluntary nature of cooperation at a multi-national level and participation in a Regional SDI initiative. As a result, despite considerable interest and activities, the development of an effective and comprehensive Asia-Pacific Regional Spatial Data Infrastructure (APSDI) is hampered by a lack of support from member nations which results in this initiative remaining only an innovative concept. Based on this situation, the aim of this research is to design an improved conceptual model for Regional SDI and an implementation strategy. It is proposed that this problem can be partly addressed by increasing the level of awareness about the nature and value of SDIs; improving the SDI conceptual model to better meet the needs of nations; and by identifying key factors that facilitate development by better understanding the complexity of the interaction between social, economic and political issues.

Recently it is obvious that climate change has became a critical topic for human society. As climate change becomes more severe, natural disasters caused by climate change have increasingly impacted humans. Most recently, Hurricane Ida killed 43 people across four states. Hurricane Ida's damage could top $95 billion, and many meteorologists predict that climate change is making storms wetter and wider. Thus, there is an urgent need to predict how much damage the flood will cause and prepare for possible destruction. Most current flood damage estimation system did not apply social media data. The theme of this thesis was to evaluate the feasibility of using machine learning models to predict hurricane damage and the input data are social media and satellite imagery. This work involves developing Data Mining approach and a couple of different Machine Learning models that further extract the feature from the data. Satellite imagery is used to identify changes in building structures as well as landscapes, and Twitter data is used to identify damaged locations and the severity of the damage. The features of Twitter posts and satellite imagery were extracted through pre-trained GloVe, ResNet, and VGG models separately. The embedding features were then fed to MLP models for damage level estimation. The models were trained and evaluated on the data. Finally, a case study was performed on the test dataset for hints on improving the models. / Master of Science / Natural disasters affect Millions of people's lives each year and it is becoming even more severe because of global warming. To make rescue more efficient when the roads and bridges are cut, social media and satellite imagery are effective data sources to help estimating flood damage. With the growth of social media, it is obvious that the post and information from people on the Internet are powerful. Also, with image processing technology improves, the information extracted from satellite images is crucial. In this work we have developed a data mining approach along with different combinations of pre-trained models using neural networks, satellite imagery and archived data from Twitter to estimate flood damage. The data mining approach leverages keywords to identify the event in the history posts in the Twitter, more specifically, we attain the geo-location, time, language information from Twitter, also using pre-event and post-event images which satellite took to generate vectors and thus effectively acquire very useful embedding features. With vectored information from Twitter and satellite imagery, we use pre-trained models and generate damage level prediction. The final results suggest that the proposed approach has potential to create more accurate prediction by using multiple data as input. Furthermore, the estimate result by using only satellite images even outperformed the result using Twitter information, which is an unexpected result comparing to previous studies.

Comparing to traditional cartography, big data geographic information processing is not a simple task at all, it requires special methods and methods. When existing geovisualization systems face millions of data, the zoom function and the dynamical data adding function usually cannot be satisfied at the same time. This research classify the existing methods of geovisualization, then analyze its functions and bottlenecks, analyze its applicability in the big data environment, and proposes a method that combines spatial data structure and iterative calculation on demand. It also proves that this method can effectively balance the performance of scaling and new data, and it is significantly better than the existing library in the time consumption of new data and scaling<br>