RA
R.M. Aalders
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Exploring a 'coordinating' smart city data governance model
A case study of The Green Village on the TU Delft Campus
Digital technology and data are becoming more interwoven into daily life in cities. Much of this data is geographic data with a spatial component. Geomatics concerns the chain of geoinformation from acquisition to visualisation. It would be preferable if this geoinformation were used fairly and for the benefit of the people living in the city. The primary motivation of this research is therefore, how to do good with (geo-spatial) data? Sennett [2018] developed a framework of two possible directions with regard to integrating digital technology into city life. The first direction is a prescriptive smart city. In this city the agency of citizens is limited and technology is used to prescribe citizens how to live their life as efficient, safe, and frictionless as possible. The second is a coordinating smart city. Here, citizens decide what, and how, technology and data are used in city life. However, Sennett’s framework is still very abstract. It only describes the two idealised types at the ends of a possible spectrum. In addition, it contains ideas and ideals for dealing with data but these have not been translated into practice, nor tested. The aim of this study is to use exploratory research to test and refine Sennett’s framework into a workable data governance model for a coordinating smart city. The Green Village (TGV) is used to test Sennett’s framework. TGV is a living lab on the campus of the TU Delft. Here, people live and work, and data is collected in public and private spaces. For the purpose of this study it functions therefore as a small smart city. The main question of this research is: ‘To what extent does the framework of the ’coordinating’ smart city of Sennett apply to the TGV case, and how can TGV help refine Sennett’s framework?’ To answer this question a combination of literature research and a case study enriched by interviews with actors in TGV are used. The conclusion of this study is that the coordinating model is a usable lens through which to examine data governance. Furthermore, TGV is not exactly coordinating or prescriptive. The participation of residents in decision-making in particular is limited. On the other hand, TGV does inspire a great deal of trust from residents because they are transparent towards hem and listen to criticism. Based on residents of TGV the level of involvement required in a coordinating smart city might be unrealistic. In addition, the role of the data manager of the data platform seems to fulfil Sennett’s concept of the expert and the authority and accountability of open data and the data commons. However, the data manager’s main accountability is not to the residents of TGV Finally, the involvement of SURF, the IT cooperative of Dutch education and research institutions, in digital infrastructure design is a way of maintaining control at TGV instead of becoming dependent on a large tech company.
...
Digital technology and data are becoming more interwoven into daily life in cities. Much of this data is geographic data with a spatial component. Geomatics concerns the chain of geoinformation from acquisition to visualisation. It would be preferable if this geoinformation were used fairly and for the benefit of the people living in the city. The primary motivation of this research is therefore, how to do good with (geo-spatial) data? Sennett [2018] developed a framework of two possible directions with regard to integrating digital technology into city life. The first direction is a prescriptive smart city. In this city the agency of citizens is limited and technology is used to prescribe citizens how to live their life as efficient, safe, and frictionless as possible. The second is a coordinating smart city. Here, citizens decide what, and how, technology and data are used in city life. However, Sennett’s framework is still very abstract. It only describes the two idealised types at the ends of a possible spectrum. In addition, it contains ideas and ideals for dealing with data but these have not been translated into practice, nor tested. The aim of this study is to use exploratory research to test and refine Sennett’s framework into a workable data governance model for a coordinating smart city. The Green Village (TGV) is used to test Sennett’s framework. TGV is a living lab on the campus of the TU Delft. Here, people live and work, and data is collected in public and private spaces. For the purpose of this study it functions therefore as a small smart city. The main question of this research is: ‘To what extent does the framework of the ’coordinating’ smart city of Sennett apply to the TGV case, and how can TGV help refine Sennett’s framework?’ To answer this question a combination of literature research and a case study enriched by interviews with actors in TGV are used. The conclusion of this study is that the coordinating model is a usable lens through which to examine data governance. Furthermore, TGV is not exactly coordinating or prescriptive. The participation of residents in decision-making in particular is limited. On the other hand, TGV does inspire a great deal of trust from residents because they are transparent towards hem and listen to criticism. Based on residents of TGV the level of involvement required in a coordinating smart city might be unrealistic. In addition, the role of the data manager of the data platform seems to fulfil Sennett’s concept of the expert and the authority and accountability of open data and the data commons. However, the data manager’s main accountability is not to the residents of TGV Finally, the involvement of SURF, the IT cooperative of Dutch education and research institutions, in digital infrastructure design is a way of maintaining control at TGV instead of becoming dependent on a large tech company.
Student report
(2023)
-
E.C. Tsipa, G. Iliopoulos, O.J. Post, R.M. Aalders, E. Verbree, J.A. Schoonman, B.M. Meijers
The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the Dutch National Archive. This was based on the corner coordinates of the map sheets. The first issue that had to be tackled was the reprojection of the original coordinates which were in Bonne projection
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence. ...
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence. ...
The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the Dutch National Archive. This was based on the corner coordinates of the map sheets. The first issue that had to be tackled was the reprojection of the original coordinates which were in Bonne projection
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence.
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence.