Although data is increasingly shared online and accessible for re-use, we still witness heterogeneous coverage of thematic areas and geographic regions. This especially becomes an issue when data is needed for large territories and including different nations, as, for example, required to support macro-regional development policies. Once identified, data gaps might be closed using different approaches. Existing—but so far non accessible—data might be made available; new public sector information could be gathered; or data might be acquired from the private sector. Our work explores a fourth option: closing data gaps with direct contributions from citizen (Citizen Science). This work summarizes a particular case study that was conducted in 2016 in the Danube Region. We provide a gap analysis over an existing macro-regional data infrastructure, and examine potential Citizen Science approaches that might help to close these gaps. We highlight already existing Citizen Science projects that could address a large part of the identified gaps, and suggest one particular new application in order to indicate how a—so far uncovered—gap might be approached. This new application addresses bioenergy as a particular field of the circular economy. On this basis we discuss the emerging opportunities and challenges for this particular way of public participation in regional development policy. We close by highlighting areas for future research.

The paper presents a conceptual model for the disposition of state agricultural land. The model is made as an extension of the Croatian Land Administration Domain Model (LADM) country profile. The LADM 19152:2012 is an International Organization for Standardization (ISO) standard which provides a formal language to describe the basic information-related components of land administration. The aim of this research is to assess the possibility of using the LADM extension to efficiently manage state-owned agricultural land. Since more than half of state-owned agricultural land in Croatia is not activated, the priority is to increase usage and activate uncultivated agricultural land by users through disposition process. The disposition process is highly regulated and complex in procedures, and it poses difficulties for organizations in the implementation of disposition, so a model of successful management is necessary. The disposition process and the necessary steps are shown—mainly defined by legal regulations—and divided into two phases: the first phase is the development of the Program of Disposition of State-Owned Agricultural Land where the multi-criteria decision analysis (MCDA) for land potential analysis is crucial; the second phase is the realization of the disposition. In line with the disposition process, a Unified Modeling Language (UML) model for this LADM extension on the conceptual level was developed and is presented herein. Finally, the improvement of the agricultural land management system and the related processes are reviewed.

The article addresses the spatial data manager domain in local self-government units. The scope of the article comprehends typical tasks and required competences for conducting the usual spatial data manager job tasks in local self-government. Furthermore, in the research described in the article, the tasks are systematized and mapped with required competences. Based on the research on tasks and required competences, two types of profiles of spatial data managers in local self-government with its specializations are proposed and described, which are recommended to be considered when developing both, occupation standard and qualification standard of spatial data managers.

The paper presents the results of research on the possibility of calculating the investment potential of a particular area based on its spatial characteristics. The level of spatial unit in this research is local administrative unit (cities or municipalities), while the geographic coverage is entire area of Republic of Croatia. Regarding the method, the results could be applied internationally and are not limited to national borders. Furthermore, when deciding on investing, it is important to know the risk. This risk in the pre-investment cycle is generally estimated on the basis of well-known wellestablished economic methods - without applying multiple criteria in the potential assessment and, among others, criteria of spatial characteristics as one of the most influential ones. Therefore, there was a need to model the investment potential as a precondition for risk calculations based on spatial criteria, which was carried out through this research using multi-criteria GIS analysis. The research in this paper is focused on testing the correlation of spatial features of certain local unit with its development index. The source data used are existing spatial data in the National Spatial Data Infrastructure (NSDI) platform, open data, and the development index as a composite index. The paper shows the results of OLS method and conclusions about influence from certain spatial characteristics on development index, and accordingly the location investment potential based on the results can be modelled.