Selected topics in Geoinformatics – the course, which presents a mix of methodology and application topics, rounding off PhD students’ experience and knowledge with perspectives from different researchers. So, here you can find my short impressions about each topic.
A framework for assessing geospatial quality of life in Big Cities
“In the 21st century, I think the heroes will be the people who will improve the quality of life, fight poverty and introduce more sustainability”
Quality of life – is very important aspect, which can help to identify the areas, where people are happy or not, so finding the general well-being of individuals and societies. Dr.Shahnawaz touched this topic in one of the lectures of “Selected topics” course. I agree 100% with him that special indicators as buildings, roads and green areas strongly influence on the quality of life.
Objective assessment using Geospatial data (remote sensing data) – I think the best approach to approve the assumption that “in places where higher green areas, higher QoL; higher density of buildings and roads show lower QoL” and so on. Of course, GIS and Remote sensing techniques can help not only to define the “poorest areas”, but also to help for example to plan new residential areas, to provide intelligently located schools, parks and roads and how connect these facilities. This can achieved also by using simple GIS techniques, such as network analysis, overlay or buffer.
According to the statistics, we can say which country has better or poor quality of life, even “Economist Intelligence Unit” defines Index of QoL as “Where to be born?” Each of us, of course, wants to live near the parks, near wide streets and beautiful houses. Hope that we can not only dream to improve the quality of life, but also to implement it in our life, using GIS.
Attitude toward urban green environment
“Knowing knowledge of Nature to be a treasure house; a key to It – observation”
This research is about interaction between environments and humans. According to Gyula Kothencz, the main concept is green areas and quality of life. A lot of work has been done to gather data, how people think about greenness of their areas, where they live or work. Also NDVI (Normalized Difference Vegetation Index) was used to find quantitative assessment of vegetation in Hungary, the most greenness areas.
But for me, comparing personal perception of greenness with measured data based on satellite image was something new for me. What is comparable with the machine?..
SALIAT – developing Geoinformation for Aviation.
“For safety is not a gudget but a state of mind “
SELIAT – stands for Safe Emergency Landing in Alpine Terrain. As I know it is a service in aviation that will provide geographic information about suitable landing sites. It supports off-field landings in alpine areas with difficult terrain. And this information will be available to pilots in their cockpit. I think it is very useful for pilots, who want to increase their safety and would like to improve their efficiency during flight operations.
This lecture with a hint of a small “advertising” again shows how applicable geographical information. The sites were identified in digital surface models and optical imagery provided by earth observation satellite services.
GIS in mobility and research planning
“Birds have wings; they’re free; they can fly where they want when they want. They have the kind of mobility many people envy”
The “Mobility” topic – one of my favorite lectures. We talked about transportation system as well as the challenges that we face. I want to say that we are, as always, sources of our problems: excess of mobility in Mumbai, when 1 million inhabitants live on 2.2 km2, or vice versa, in San Jose, when people lose more than 90 minutes and more than 50 miles to get to their work.
I can say our purposes and our desires are dangerous, because our behavior can have impact to the geography. Again social problem and social case behind this. Now we are losing roads as public spaces, we are dealing that mobility requires lot of our space. “Space is limited. So we need to decide how we use our space”- told Mr. Loidl. And of course GIS can help in such situations, to reduce necessity to travel, to ensure efficient and sustainable mobility, to minimize social impact of mobility.
But it is a pity that we are not free as birds. We have to limit our space, to avoid any bad consequences. So we have a little get greedy, even as we consider drivers who use a car for themselves, taking up “our” space on the roads!
Myths and realities about the recovery of L’aquila after the Earthquake
“Whenever an earthquake or tsunami takes thousands of innocent lives, a shocked world talks of little else”
After this lecture I can be sure how widely applicable GIS: land resources, transportation, utilities, business development, law enforcement and security, emergency management, demography, ecology, health and others..
Using GIS, a very large contribution was made in recovering of L’Aquila historical city in Italy after the strongest earthquake. Remote sensing data were used to identify, which buildings were reconstructed, to monitor the progress of recovery process after an earthquake and to answer the question whether the recovery process in L’Aquila stagnates or not. Analysis of spatial indicators of recovery as spatial connectivity, building condition, building use – also important approach for monitoring. Myths versus realities – that really does GIS can expose the authorities in their careless attitude to ordinary citizens in L’Aquila city.
“You can have data without information, but you cannot have information without data”
I very liked the definition by Euro Beinat – if it does not fit Excel it is a Big Data! How is big data generated? Where they are coming? Why Big Data (human sensors) for Geoinformatics? – these questions we considered in one of the lectures with Isabela Sitko.
As you know, what happens an incredible digitalization what we are doing every day. We produce a lot of data as a society so we can say now we are source of data. Data produce not only a massive amount but also with a very high speed! As an example: what happens in an internet minute? Every second, every minute we produce data very quick. My impression of the lecture is that now we have a large amounts of data, but most of them we will not use it in future. According to the Moor’s law – every year the data is doubled. Only quesion remains if wiil be useful to store such data?