Urban SDG Measuring System using the Open Geospatial Data of the International Organizations
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Choi, Junyoug
Hyunsoo Kim
Jaeseong Ahn
As a post Millennium Development Goals(MDG), 17 Sustainable Development Goals(SDG) and 169 indicators were adopted at 2015 in the UN general assembly. Especially as the developing countries are predicted to face rapid urbanization until the 2030, Cities attract more and more attentions for achieving SDGs because most activities occur in the cities. But the activities are closely related to spatial phenomena. It is essential to consistently construct the spatial database for developing and developed countries and uniformly measure and monitor the indicators of urban SDGs which is operationally defined in this presentation as a SDG 9 and 11. In this regards, open geospatial data present both possibilities and limitations for urban SDG measurement. In this presentation, we analyze open database from UN agencies and Multilateral Development Banks(MDB) and measure and predict the urban SDG until the 2030 using that database. To that end, we analyze the related open data structure of those international organizations and develop a UN SDG monitoring system based on the FOSS4G solutions such as PostGIS and GeoServer. Choi, Junyoug (Spatial Information office, Korea Land and Housing corp., Republic of Korea) Hyunsoo Kim (CITUS. CO) Jaeseong Ahn
 Okay, we are continuing with our third and last speaker for this morning. I would like to introduce Mr. Choi from the Spatial Information Office from the Korea Land and Housing Corporation. He is going to tell us something about using the open-duty spatial data of the international organization in Korea. So if you are ready, the floor is yours. Yeah, nice to meet you. My name is Choi Jun-young. I am from Korea Land and Housing Information. It is a government company. So we usually deal with providing affordable housing in Korea. And recently we provide affordable housing master plan for developing countries, listed developing countries. My presentation is consist of five chapters and the main content is about the open data plan systems of international organizations. And we analyzed a very small part that developed a monitoring scheme for the urban SDGs. It is a part of UN Sustainable Development Call that was approved last year by the UN General Assembly. And in this presentation, urban SDGs means urban SDGs 11. And we focused on urban SDGs 11. And the last chapter, we focused on the new urban agenda, but it is a brief overview because the new urban agenda was not finalized because UN Habitat III components will be held this October. So this new urban agenda was not finalized. But before the main components, there were several pre-components and there was Gerald Left and the Palace Unit. So we derived the implications and direction for the new urban agenda in the perspective of open data. Our talk was started from the urbanization and urban SDGs. The urbanization in developing countries has many people have focused more on the cities and UN SDGs, especially urban SDGs, focuses on the city. That means the city is the center of the, of achieving urban SDGs, not only urban SDGs, including all the UN SDGs. Initially, global goals started from the 2000, there was MDG, Millium Development Goal, but it's changed to SDGs, Sustainable Development Goal, last year. And the next step for the, that goal is new urban agenda that is more focused on the urban area, urban domain. And UNHMT3 components will set up the new urban agenda in accordance with the SDGs 11. But that the new urban agenda is very superficial and is not finalized. So we now focused on the SDGs 11 and related indicators. Urban SDGs, UN SDGs, all 17 goals as 169 indicators. And SDGs 11 has a very small portion of this, but these goals are very closely related to the special phenomena. So our interest in the, what is the role of GIS to achieve this goal, especially urban goals. And also very recently, agenda, related agenda for new urban agenda. And the second one is the, the supports can bring the gap of monitoring and implementation among list developed developing and developed countries. Because our goal is the global goal. So most countries are, some countries are developed and some countries are just developed. So in case of list development, developing countries can help from these post-poly. So in this presentation, we survey the, what can do using the post-poly and open data. About this topic, we dealt with this topic in the post-poly Seoul 2015. And as a result, some of the outcomes of outcomes were derived. That was UN Open GIS, that was between the collaboration between UN and post-poly community. And in this talk, we think the, we analyze the open data from UN agencies and some multi-development banks such as ADB and IADB and to measure and monitor the goal and agenda. Especially in case of SDG, we think it is possible to develop a monitoring system based on the open data and post-poly solution such as GeoNode. There are many available open source GIS data. That is from list-based, vector-based and some site to provide the list and vector data. But the list data was very, very, very much. But vector source is very, very little. And in some cases, it is not just data itself, it provides a style, the time map for their own uses such as the environment and agriculture, etc. And also some data cover specific continent such as Europe or Asia or Latin America. And some international organizations provide open data sources. In case of UN habitat, they provide 220 countries and 700 cities and over 100 indicators. And it is provided by CSV and JSON and OECD provide data for member states and selected non-member states. But OECD data is focused not on the open data but more on the economic perspective. So it provides the country overview and other economy and education and employment data. But OECD has provided very detailed data for member states. And World Bank is very, very best amount of data through the satellite imagery. And it also covers all nations. And it provides data in a very various way. In case of MDV such as the Asia Development Bank, African Development Bank and Inter-American Development Bank. This organization provides data for their specific region, continent. But their focus is on the investment. So they provide investment and projection information data. So there are very limitations to use this data in monitoring and implementing the SDG and new urbanization. When we compare the information systems, habitat user, some collection, data collection scheme such as GUO and OECD heavily rely on each country's statistical agency, OPEC. And World Bank has its own institution for collecting data. And in case of temporal monitoring, habitat provides a very long range of time span. But there is very little data that provides a full range of data. So based on this existing international organizations, open data and multilateral development banks data, we rely with a very important point to monitor the urban goals and agenda is to the management of metadata and to visualize the data to compare the each country and region. So we chose the appropriate architecture from the GeoNode. And it is the example of GeoNode from the Korean company Mango System. And we focused on the high tier layer that is the visualization layer. And we added the visualizing techniques such as D3JS to visualize the data. That means to compare the each country and region using the D3JS. Based on this scheme, that is we are adding the D3JS on top of the GeoNode architecture. But currently this conceptual diagram, we separately work to implement it. That means we only implemented the D3JS visualization. When we monitor the global level, the specialty is very important. In many cases, the focus is on the country level. In some cases, the city level, that means the metropolitan level. So in case of Ecuador or Quito, more data will be concentrated on the city or metropolitan level. Some comparison purpose, we can compare the each country. That means we compare the Ecuador, Colombia, Venezuela, the cross country level. So in this special we need the city level or regional level and the wider regional level over the country boundary. So in this case, we need the appropriate base map from the collected raw data from the international organizations. So our scheme is to adding two functionality to GeoNode, that is the metadata management. That is metadata management is already existing in the original GeoNode architecture. And the second one is the geo-imprographic. That is the geo-visual analytics function for decision support to relate to country or city level monitoring. And to implement this second functionality, we use the open layers and this is JS. And by adding this technique, we can compare the each country's population, employment and other status of each country's urban phenomena using the chart and graph on the map. There is architecture on the geosystems that is adding the geo-imprographic on the geosystems in GeoNode architecture. And there is more needed to more functionality to managing the metadata to when we collect the data from the various sources of the from other organizations data, open data. When we compare the each country's status for the purpose of monitoring and monitoring the global urban goal, we think that there is some component that can be possible. There is a user interaction and we think the narrative structure is more powerful in explaining the current status of each country, macro-level structure. So we introduce the narrative design template and narrative structure and also we use the special time visualization. There is a simple component of the geo-imprographic environment that is color-plus map and bar chart and circling animation heat map, isotype. Using this component, we some cases study for the Korean housing policy. This is a visualizing case for Korea. The question is what is the quality of life for housing in Korea? So we use the color-plus map and chart and this is that we use the information explained template to design this geo-imprographic diagram. In case of Korea to buy our own home, we save all the salary without spending for five and two months. But in case of the metropolitan area, we save more time than the national average. This geo-imprographic explains that environment. In case of urban SDG, there are indicators for implementing the urban goal. But in case of new urban agenda, the implementation scheme is not settled. So we suggest some implications from the existing police unit that is consisting of the new urban agenda. New urban agenda is collaborating each component of the global agenda and some agreement that is in case of SDG, SDG 11 is urban agenda and the last year's climate change agreement is also influenced on the new urban agenda. And other important agreement was influenced on the new urban agenda. But we achieve this agenda with more important thing in terms of data. We need to collect and analyze confidence and transparent urban data. So there are 10 police units for Habitat 3 and we deliver the implication for data and in case of the city and city's portal, we need to measure the formal and informal sector with information. So provide the decision support information for the citizen participation and social cultural urban framework. In this case, we need to open up and construct a social cultural P.O.I. inclusively and national urban policy. In the case of using NSD, national special data infrastructure, that is from the government agency, traditional scheme, but also from the private sector including open street map. Urban governance capacity and institutional development, consolidated data between urban and rural area and also consolidated data for international comparison. For municipal finance and local fiscal system, improving the transparency and accountability by opening and standardization of municipal finance data and special importance of municipal finance data. Policy 6, urban special strategy, in case of this policy unit, special information can detect urban sprawl or expansion of metro burden area. For 6, it's very important because we can do more about this policy unit. And also number 10, also it's very important because in this case, if we provide affordable housing, we have to detect informal settlement and such as informal settlement detection and slur mapping is very important. So, in this case, policy 6 and 10 will be very important to the geospatial information. This analysis is very broad overview, but there will be some implication from this overview. And recently, many international organizations are trying to adopt monitoring approach using post-projects such as World Bank and NASA, Lincoln Land Institute. And to be successful, more project and community members have to be collaborated. And second, more OSGU project have to be included to achieve global urban goal legend. Such as, for example, GeoNode can be a good example to be used as a monitoring platform, but to develop further, more project have to be included. UNHB3 components at Kito Iquador will be held 17 of October, so there are still room for participation in better space. Thank you. Thank you.
 Okay, we are continuing with our third and last speaker for this morning.  I would like to introduce Mr. Choi from the Spatial Information Office from the Korea  Land and Housing Corporation.  He is going to tell us something about using the open-duty spatial data of the international  organization in Korea.  So if you are ready, the floor is yours.  Yeah, nice to meet you.  My name is Choi Jun-young.  I am from Korea Land and Housing Information.  It is a government company.  So we usually deal with providing affordable housing in Korea.  And recently we provide affordable housing master plan for developing countries, listed  developing countries.  My presentation is consist of five chapters and the main content is about the open data  plan systems of international organizations.  And we analyzed a very small part that developed a monitoring scheme for the urban SDGs.  It is a part of UN Sustainable Development Call that was approved last year by the UN  General Assembly.  And in this presentation, urban SDGs means urban SDGs 11.  And we focused on urban SDGs 11.  And the last chapter, we focused on the new urban agenda, but it is a brief overview because  the new urban agenda was not finalized because UN Habitat III components will be held this  October.  So this new urban agenda was not finalized.  But before the main components, there were several pre-components and there was Gerald  Left and the Palace Unit.  So we derived the implications and direction for the new urban agenda in the perspective  of open data.  Our talk was started from the urbanization and urban SDGs.  The urbanization in developing countries has many people have focused more on the cities  and UN SDGs, especially urban SDGs, focuses on the city.  That means the city is the center of the, of achieving urban SDGs, not only urban SDGs,  including all the UN SDGs.  Initially, global goals started from the 2000, there was MDG, Millium Development Goal, but  it's changed to SDGs, Sustainable Development Goal, last year.  And the next step for the, that goal is new urban agenda that is more focused on the urban  area, urban domain.  And UNHMT3 components will set up the new urban agenda in accordance with the SDGs 11.  But that the new urban agenda is very superficial and is not finalized.  So we now focused on the SDGs 11 and related indicators.  Urban SDGs, UN SDGs, all 17 goals as 169 indicators.  And SDGs 11 has a very small portion of this, but these goals are very closely related to  the special phenomena.  So our interest in the, what is the role of GIS to achieve this goal, especially urban  goals.  And also very recently, agenda, related agenda for new urban agenda.  And the second one is the, the supports can bring the gap of monitoring and implementation  among list developed developing and developed countries.  Because our goal is the global goal.  So most countries are, some countries are developed and some countries are just developed.  So in case of list development, developing countries can help from these post-poly.  So in this presentation, we survey the, what can do using the post-poly and open data.  About this topic, we dealt with this topic in the post-poly Seoul 2015.  And as a result, some of the outcomes of outcomes were derived.  That was UN Open GIS, that was between the collaboration between UN and post-poly community.  And in this talk, we think the, we analyze the open data from UN agencies and some multi-development  banks such as ADB and IADB and to measure and monitor the goal and agenda.  Especially in case of SDG, we think it is possible to develop a monitoring system based  on the open data and post-poly solution such as GeoNode.  There are many available open source GIS data.  That is from list-based, vector-based and some site to provide the list and vector data.  But the list data was very, very, very much.  But vector source is very, very little.  And in some cases, it is not just data itself, it provides a style, the time map for their  own uses such as the environment and agriculture, etc.  And also some data cover specific continent such as Europe or Asia or Latin America.  And some international organizations provide open data sources.  In case of UN habitat, they provide 220 countries and 700 cities and over 100 indicators.  And it is provided by CSV and JSON and OECD provide data for member states and selected  non-member states.  But OECD data is focused not on the open data but more on the economic perspective.  So it provides the country overview and other economy and education and employment data.  But OECD has provided very detailed data for member states.  And World Bank is very, very best amount of data through the satellite imagery.  And it also covers all nations.  And it provides data in a very various way.  In case of MDV such as the Asia Development Bank, African Development Bank and Inter-American  Development Bank.  This organization provides data for their specific region, continent.  But their focus is on the investment.  So they provide investment and projection information data.  So there are very limitations to use this data in monitoring and implementing the SDG  and new urbanization.  When we compare the information systems, habitat user, some collection, data collection scheme  such as GUO and OECD heavily rely on each country's statistical agency, OPEC.  And World Bank has its own institution for collecting data.  And in case of temporal monitoring, habitat provides a very long range of time span.  But there is very little data that provides a full range of data.  So based on this existing international organizations, open data and multilateral development banks  data, we rely with a very important point to monitor the urban goals and agenda is to  the management of metadata and to visualize the data to compare the each country and region.  So we chose the appropriate architecture from the GeoNode.  And it is the example of GeoNode from the Korean company Mango System.  And we focused on the high tier layer that is the visualization layer.  And we added the visualizing techniques such as D3JS to visualize the data.  That means to compare the each country and region using the D3JS.  Based on this scheme, that is we are adding the D3JS on top of the GeoNode architecture.  But currently this conceptual diagram, we separately work to implement it.  That means we only implemented the D3JS visualization.  When we monitor the global level, the specialty is very important.  In many cases, the focus is on the country level.  In some cases, the city level, that means the metropolitan level.  So in case of Ecuador or Quito, more data will be concentrated on the city or metropolitan  level.  Some comparison purpose, we can compare the each country.  That means we compare the Ecuador, Colombia, Venezuela, the cross country level.  So in this special we need the city level or regional level and the wider regional level  over the country boundary.  So in this case, we need the appropriate base map from the collected raw data from the  international organizations.  So our scheme is to adding two functionality to GeoNode, that is the metadata management.  That is metadata management is already existing in the original GeoNode architecture.  And the second one is the geo-imprographic.  That is the geo-visual analytics function for decision support to relate to country or  city level monitoring.  And to implement this second functionality, we use the open layers and this is JS.  And by adding this technique, we can compare the each country's population, employment  and other status of each country's urban phenomena using the chart and graph on the map.  There is architecture on the geosystems that is adding the geo-imprographic on the geosystems  in GeoNode architecture.  And there is more needed to more functionality to managing the metadata to when we collect  the data from the various sources of the from other organizations data, open data.  When we compare the each country's status for the purpose of monitoring and monitoring  the global urban goal, we think that there is some component that can be possible.  There is a user interaction and we think the narrative structure is more powerful in explaining  the current status of each country, macro-level structure.  So we introduce the narrative design template and narrative structure and also we use the  special time visualization.  There is a simple component of the geo-imprographic environment that is color-plus map and bar  chart and circling animation heat map, isotype.  Using this component, we some cases study for the Korean housing policy.  This is a visualizing case for Korea.  The question is what is the quality of life for housing in Korea?  So we use the color-plus map and chart and this is that we use the information explained  template to design this geo-imprographic diagram.  In case of Korea to buy our own home, we save all the salary without spending for five and  two months.  But in case of the metropolitan area, we save more time than the national average.  This geo-imprographic explains that environment.  In case of urban SDG, there are indicators for implementing the urban goal.  But in case of new urban agenda, the implementation scheme is not settled.  So we suggest some implications from the existing police unit that is consisting of  the new urban agenda.  New urban agenda is collaborating each component of the global agenda and some agreement that  is in case of SDG, SDG 11 is urban agenda and the last year's climate change agreement  is also influenced on the new urban agenda.  And other important agreement was influenced on the new urban agenda.  But we achieve this agenda with more important thing in terms of data.  We need to collect and analyze confidence and transparent urban data.  So there are 10 police units for Habitat 3 and we deliver the implication for data and  in case of the city and city's portal, we need to measure the formal and informal sector  with information.  So provide the decision support information for the citizen participation and social cultural  urban framework.  In this case, we need to open up and construct a social cultural P.O.I. inclusively and national  urban policy.  In the case of using NSD, national special data infrastructure, that is from the government  agency, traditional scheme, but also from the private sector including open street map.  Urban governance capacity and institutional development, consolidated data between urban  and rural area and also consolidated data for international comparison.  For municipal finance and local fiscal system, improving the transparency and accountability  by opening and standardization of municipal finance data and special importance of municipal  finance data.  Policy 6, urban special strategy, in case of this policy unit, special information can  detect urban sprawl or expansion of metro burden area.  For 6, it's very important because we can do more about this policy unit.  And also number 10, also it's very important because in this case, if we provide affordable  housing, we have to detect informal settlement and such as informal settlement detection  and slur mapping is very important.  So, in this case, policy 6 and 10 will be very important to the geospatial information.  This analysis is very broad overview, but there will be some implication from this overview.  And recently, many international organizations are trying to adopt monitoring approach using  post-projects such as World Bank and NASA, Lincoln Land Institute.  And to be successful, more project and community members have to be collaborated.  And second, more OSGU project have to be included to achieve global urban goal legend.  Such as, for example, GeoNode can be a good example to be used as a monitoring platform,  but to develop further, more project have to be included.  UNHB3 components at Kito Iquador will be held 17 of October, so there are still room  for participation in better space.  Thank you.  Thank you.
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