WHAT ARE HOT TOPICS IN GISCIENCE, GEOINFORMATICS, GEOIT AND HOW TO PROVE THIS?

University of Salzburg Department of Applied Geoinformatics WHAT ARE HOT TOPICS IN GISCIENCE, GEOINFORMATICS, GEOIT AND HOW TO PROVE THIS? Student: Gabriel Schendl (ID: 1120878) Supervisor: Univ. Prof. Mag. Dr. Thomas Blaschke Seminar GIScience: Theories and Concepts Winter term 2016

Content List of tables... 2 List of figures... 3 1. Introduction... 4 2. Volunteered Geographic Information (VGI)... 6 3. Geographic Object Based Image Analysis (GEOBIA)... 13 4. Indoor Positioning Systems... 18 5. Conclusion and Outlook... 24 References... 25 2

List of tables Table 1: Citations in Google Scholar, Web of Knowledge and Scopus... 16 List of figures Figure 1: Own illustration of the four pillars of Volunteered Geographic Information... 7 Figure 2: The city of Kitzbühel in OpenStreetMap... 8 Figure 3: Neusiedl am See and surroundings in Wikimapia... 9 Figure 4: Publications on VGI (Scopus)... 11 Figure 5: Publications on VGI (Web of Science)... 12 Figure 6: Publications on GEOBIA (Scopus)... 17 Figure 7: Publications on GEOBIA (Web of Science)... 17 Figure 8: Own illustration of the main parts of Indoor Positioning Systems... 19 Figure 9: Own illustration of performance aspects of an Indoor Positioning System... 20 Figure 10: Publications on IPS (Scopus)... 23 Figure 11: Publications on IPS (Web of Science)... 23 3

1. Introduction The goal of this seminar paper is to discuss three selected hot topics in GIscience, Geoinformatics and GeoIT. After doing a literature search, the topics Volunteered Geographic Information (VGI), Geographic Object-Based Image Analysis (GEOBIA) and Indoor Positioning Systems (IPS) were taken due to the quality and quantity of the found references. This seminar paper gives an introduction into these three topics. For the topic Volunteered Geographic Information, the following aspects are discussed: Definitions Pillars of VGI Creation of such information Examples of VGI VGI as input to scientific research and mapping Quality of VGI For the topic Geographic Object-Based Image Analysis, the following aspects are discussed: Definitions How GEOBIA works Main targets of GEOBIA Coupling of GEOBIA with Agent-Based Image Analysis (ABIA) Strengths, Weaknesses, Opportunities and Threats 4

For the topic Indoor Positioning Systems, the following aspects are discussed: Definitions Properties of IPS Main parts of IPS and their basic functionality Performance aspects After giving an introduction, arguments are listed why these topics have to be considered as a hot topic. For all three topics, a literature analysis was conducted in Scopus and Web of Science to find out if the number of publications on these topics is ascending. A rising number of publications will be regarded as an indication that this topic must be considered as hot. For all topics, further arguments from other sources are discussed why they are important. In the conclusion and outlook, the three discussed topics will be categorized in the fields of GIscience, Geoinformatics and GeoIT based on the introduction of them. Open questions will be discussed. The motivation of this paper is to give the readers an overview and introduction of selected hot topics in GIscience, Geoinformatics and GeoIT. Further, the readers can discover how it can be proved that a certain topic needs to be considered as hot. 5

2. Volunteered Geographic Information (VGI) The last years were marked through a huge involvement of many private people without knowledge in Geoinformatics who created geographic information. Most of them do this voluntary and the results need not be accurate. Goodchild (2007) terms volunteered geographic information (VGI) as a dramatic innovation that will certainly have profound impacts on geographic information systems (GIS) and more generally on the discipline of geography and its relationship to the general public. VGI is marked through contents which are created by the users (Goodchild 2007). Elwood, Goodchild et al. (2012) define VGI as geographic information acquired and made available to others through the voluntary activity of individuals or groups, with the intent of providing information about the geographic world. Volunteered geographic information can be regarded as a localized due to the fact that 76 percent of the gathered data has a local extent (Elwood, Goodchild et al. 2012). After Loo Arancibia (2011), VGI refers to the creation of new geographically-based initiatives and abilities in Web sites by large numbers of untrained private citizens with or without formal geographic expertise or qualification. The created information refers to certain location due to a spatial reference; today, a lot of place-based information can be accessed. Projects like OpenStreetMap raised due to the increasing importance of volunteered geographic information. Everybody can use the Web unlocked GIS for sharing volunteered geographic information where local data from many places is gathered (Loo Arancibia 2011). Volunteered geographic information can be regarded as a new development of geographic information systems. Like GIS, VGI consists of four important elements (Loo Arancibia 2011): Hardware referring to computers handling a lot of information Software referring to the open source software for creating volunteered geographic information Data referring to the creation of geographic information; the available data increased severe People referring to the volunteers acquiring geographic data 6

Figure 1 shows the four pillars of Volunteered Geographic Information. Figure 1: Own illustration of the four pillars of Volunteered Geographic Information Geographic information is more and more produced by untrained persons who need not be interested in sciences like geography. This can be regarded as a trend toward increasing publicly volunteered geographic information, where various forms of geodata are provided voluntarily by individuals (Flanagin and Metzger 2008). Different views of volunteered geographic information are possible like an extension of public participation geographic information systems, collaborative GIS, participatory GIS, and Community Integrated GIS (Flanagin and Metzger 2008). Further, VGI serves as a version [ ] that is more inclusive by virtue of its emergence among endusers, based on their own particular interests and goals (Flanagin and Metzger 2008). The creation of geographic data created by volunteers affects geographic information. Huge amount of data from certain places, images or other important geographic information got available. Further, volunteers will often be the appropriate information source to acquire certain data from particular environments or local places. Volunteered geographic information is marked through advantages and disadvantages. For example, the reliability and quality of this information resource need not be perfect (Flanagin and Metzger 2008). 7

As an example of volunteered geographic information, Wikimapia can be mentioned. The users have the opportunity to add information on a part of the world. Features with different sizes from single buildings up to big cities can be edited. In OpenStreetMap, volunteered information is turned into an open map data source which need not be perfect. For some areas, the gathered data might be very detailed, for other areas not (Goodchild 2007). Figure 2 shows a detailed map of Kitzbühel in OpenStreetMap. Figure 2: The city of Kitzbühel in OpenStreetMap (https://www.openstreetmap.org/search?query=kitzb%c3%bchel#map=16/47.4462/12.3946 (accessed on January 3, 2017) To support the public creation of geodata, georeferencing of information is necessary. Tools which enable the identification of coordinates have to be provided because most users do not know the latitude and longitude of certain places. Many technologies now support georeferencing and facilitate the creation of volunteered geographic information. The Global Positioning System (GPS) is integrated in many products to determine the location e.g. cameras with GPS offer the possibility to label photographs with coordinates. GPS devices or mobile phones can be used to gather paths or routes. The resulting information can be integrated in digital maps like OpenStreetMap. Webpages like Wikimapia or OpenStreetMap are suitable to investigate remote places or to gather information for tours in certain areas. They are many times an inexpensive source of geodata. Further, such webpages can be the only data source; especially of areas where information is not provided by the authorities due to security reasons. GPS devices offer the possibility to determine the exact position of 8

objects on the earth with high accuracy. This technology led to the creation of volunteered geographic information like gathering dataset by cycling or walking (Goodchild 2007). Figure 3 shows Neusiedl am See and its surroundings. The area of this city has been edited very detailed while the surroundings are not edited in detail so far. Figure 3: Neusiedl am See and surroundings in Wikimapia (http://wikimapia.org/#lang=en&lat=47.949581&lon=16.833200&z=14&m=b&search=neusiedl%20am%20see accessed on January 3, 2017) These possibilities of creating and publishing descriptive geoinformation could influence science. If this happens, the properties of this information and methods to generate new information need to be developed. Volunteered geographic information could lead to adaptions of concepts in using geographic information. Research on VGI led to new research areas in disciplines like information science and computer science (Elwood, Goodchild et al. 2012). Volunteered Geographic Information can serve as input to scientific research. The existing field of citizen science got part of many disciplines. Meteorological data from volunteers had been integrated into networks of observation stations from professionals. Selected volunteers could be trained for contributing in the gathering of raw scientific data. One problem with integrating volunteered geographic information into research is that the acquired information need not fulfil the quality requirements which are necessary in scientific activities (Elwood, Goodchild et al. 2012). 9

Further, authorities do not want to take high costs of mapping. As consequence, they are looking for cheaper options like volunteers gathering mapping data. Some mapping applications can be done by remote sensing techniques, but satellites cannot acquire all information which needs to be displayed on maps like names of locations (Goodchild 2007). It is possible that mapping departments take advantage of volunteered geographic information for better change detection and updates of geospatial information (Coleman, Georgiadou et al. 2009). Not all governmental information can be gathered by volunteers. Data like orthoimagery or accurate information on elevation needs to be acquired by professionals. Also, information on static data like land tenure or administrative units are not suitable to be collected by voluntary geographers (Elwood, Goodchild et al. 2012). The quality of volunteered geographic information is not perfect. Possible problems here include measurement errors, loss of details, uncertainty of definitions or inaccuracy. Creating perfect representations of geographic phenomena will always be challenging; geospatial information will always have a lack of quality. Volunteers should think if a set of information is enough accurate for a certain purpose. Topics like regularly quality reviews of the data or considering information quality standards are not really taken into account when it comes to collecting volunteered geographic information. Data quality belongs to the negative aspects of using VGI data in scientific activities (Elwood, Goodchild et al. 2012). Volunteers could be trained for acquiring data with a higher accuracy and reliability. Citizens have the opportunity to work as a sensor and gather accurate data of their surrounding areas (Flanagin and Metzger 2008). Quality control is existing in volunteered geographic information, but not in the same manner as in the traditional production of geographic information (Elwood, Goodchild et al. 2012). One of the reasons for this is the fact that volunteered geographic information is acquired through crowdsourcing. Instead of experts working after established standards, the quality control is done by other volunteers in the crowdsourcing. Although models for involving VGI producers in the spatial data quality management process were proposed (Elwood, Goodchild et al. 2012), the quality of volunteered geographic information gets checked by the amount of people who verified or changed the content of the gathered data. Information about location with a high population or often studied phenomena probably is marked through a high quality, isolated structures on the other usually are marked through a low data quality (Elwood, Goodchild et al. 2012). 10

As mentioned before, volunteered geographic information has usually no metadata. Here, other possibilities for the documentation and description of the properties of geographic information are suggested. For example, metadata can be captured from comments of other users. It turned out that volunteered geographers are seldom motivated to provide metainformation about their acquired datasets, it could be possible in the future that volunteers will outline their experiences (Elwood, Goodchild et al. 2012). Besides these problems, volunteered geographic information can be essential despite the uncertain data quality. For example, in forest fires, volunteered data was gathered and sent to authorities. Official data acquisition from authorities would have taken more time. Often responding action like evacuations from authorities were done based on the volunteered geographic information which could have been inaccurate because no there was no data from authorities available (Elwood, Goodchild et al. 2012). Volunteered geographic information is representing an unprecedented shift in the content, characteristics, and modes of geographic information creation, sharing, dissemination, and use (Sui, Goodchild et al. 2012). Finally, arguments are discussed why volunteered geographic information is a hot topic. After Loo Arancibia (2011), the amount of literature on using volunteered geographic information and its implications ascended noticeably. This statement was proved via a literature analysis in Scopus where the term Volunteered Geographic Information was entered and article titles, abstracts and keywords were queried. Figure 4 shows the number of publications per year in Scopus from 2007 to 2016. 160 Number of publications on VGI in Scopus 140 120 100 80 60 Number of publications 40 20 0 2006 2008 2010 2012 2014 2016 2018 Figure 4: Publications on VGI (Scopus) 11

Web of Science was also used for a literature analysis; here Volunteered Geographic Information was entered and topics were queried after this term. Figure 5 shows the number of publications per year in Web of Science from 2005 to 2016. 160 140 120 100 80 60 40 20 Number of publications on VGI in Web of Science 0 2005 2007 2009 2011 2013 2015 2017 Number of publications Figure 5: Publications on VGI (Web of Science) Based on the literature analysis, the statement of Loo Arancibia (2011) can be verified. Based on the growing number of publications on Volunteered Geographic Information can be regarded as a hot topic. Because of devices for acquiring information, a huge amount of data is gathered. 90 percent of data got collected in the last 24 months; the mass of human data doubles every 24 months. Geospatial information needs to be considered as an essential part of big data in the future. Due to the storage and management of large raster data like satellite images or vector dataset, geographic information became an important aspect of big data (Sui, Goodchild et al. 2012). Elwood, Goodchild et al. (2012) give an overview of VGI activities before 2010. 73 percent of activities were done between 2005 and 2009, 14 percent between 2000 and 2004 respectively 6 percent of the activities before the turn of the millennium. For the other 7 percent, the time could not be investigated. This shows that the amount of VGI activities is rising which indicates that VGI is a hot topic. 12

3. Geographic Object Based Image Analysis (GEOBIA) The last years in remote sensing were marked through a paradigm shift. There was a move from a predominantly pixel-spectra based model to a dynamic multiscale object-based contextual model that attempts to emulate the way humans interpret images (Hay and Castilla 2008). Hay and Castilla (2008) term this new paradigm Geographic Object-Based Image Analysis (GEOBIA) which can be understood as object-based analysis of Earth remote sensing imagery (Hay and Castilla 2008). Hay and Castilla (2008) define GEOBIA as a subdiscipline of Geographic Information Science (GIScience) devoted to developing automated methods to partition remote sensing imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scales, so as to generate new geographic information in GIS-ready format. GEOBIA is an earth centric field because the used data comes from the surface of this planet. GEOBIA is object based, meaningful image-object delineation is a prerequisite of this approach, from with relevant intelligence can be generated (Hay and Castilla 2008). Further, its analytical methods are multi-source capable, [ ] methods are contextual, allowing for surrounding information and attributes (Blaschke, Hay et al. 2014). GEOBIA uses remote sensing data as input; as output, GIS data gets created. After Hay and Castilla (2008), GEOBIA represents a critical bridge between the (often disparate) raster domain of RS, and the (predominantly) vector domain of GIS. Remote Sensing and Geographic Information Systems are connected through the generation of polygons (i.e., classified image-objects) representing geographic objects (Hay and Castilla 2008). Important processes of GEOBIA are image segmentation, attribution or classification respectively the ability to query and link individual objects in space and time (Hay and Castilla 2008). The first step is a more or less arbitrary segmentation of the input data to generate a hierarchical net of image objects followed by an initial classification of the generated image objects (Hofmann, Lettmayer et al. 2015). The second step is an iterative process of selective segmentation improvements and re-classifications until a satisfactory result is achieved (Hofmann, Lettmayer et al. 2015). To generate and use geographic information, expertise and methods from many disciplines are required. GEOBIA differs from Object Based Image Analysis (OBIA) through its accentuation on Remote Sensing and geographic information (Hay and Castilla 2008). 13

Blaschke (2010) states that OBIA could be a too wide field. It is also used in disciplines like Material Sciences or Computer Vision. In GIS and environmental sciences, imagery from the earth surface gets used (Blaschke 2010). According to Hay and Castilla (2008), the main target of GEOBIA is not that of tool building, but rather the generation of geographic information (from remote sensing imagery), from which intelligence can be obtained. Possible applications for deriving intelligence are climate change, mapping or land use or land cover (Hay and Castilla 2008). The main target of GEOBIA as a discipline is to develop theory, methods and tools sufficient to replicate (and/or exceed experienced) human interpretation of RS images in automated/semi-automated ways (Hay and Castilla 2008). Benefits are higher accuracy of information and a higher degree of objectivity (Hay and Castilla 2008). The quality of geographic information derived from remote sensing imagery needs to fulfil the criteria of an acceptable classification result. Quality gets measured through quantifying the correctness and completeness of a classification result (Hofmann, Lettmayer et al. 2015). GEOBIA got more and more important due to the availability of remote sensing imagery with a high spatial resolution or through the high amount of earth observation data which gets recorded every day. Pixel-based image approaches were marked through limitations and the fact that object-based methods can make better use of neglected spatial information implicit within RS images, and provide greater integration with vector based (Hay and Castilla 2008). Further, computers turned out to be powerful tools for doing GEOBIA (Hay and Castilla 2008). GEOBIA gets mainly used to satellite data with a high spatial resolution; but it can also be applied to satellite imagery with low spatial resolution. The properties of image-objects can be evaluated based on the scale. Important aspects are conceptual modelling and representation of spatially referenced imagery (Blaschke, Hay et al. 2014). Further, GEOBIA can be applied in situations where spectral properties are not unique, but where shape or neighborhood relations are distinct (Blaschke, Hay et al. 2014). For example, spectral properties of meanders can vary depending if they are filled by water or by sediment. Spectral properties of image-objects get transferred into real-world features through the use of semantics based on descriptive assessment and knowledge (Blaschke, Hay et al. 2014). Compared to pixel-based approaches, higher classification accuracy is achieved with objectbased approaches. Acceptable results are only possible if rule sets are manually modified leading to a lower degree of automation. This is due to image contents with a high 14

complexity or the varying quality of objects in imagery. A solution can be integrating and extending object-based approaches with the agent-based paradigm for autonomous adaptation of rule sets and image objects (Hofmann, Lettmayer et al. 2014). Software agents in complex environments can respond if unpredictable changes appear. In Multi Agent Systems, software agents have the opportunity to work together to fulfil certain goals. Agent based systems can raise the accuracy of classifications. Different kinds of software agents can be distinguished in Agent-Based Image Analysis (ABIA) like Rule Set Adaption Agents, Image Object Agents or Control Agents (Hofmann, Lettmayer et al. 2014). Strengths of object-based approaches are (Hay and Castilla 2006): Separating images into objects resembles the way people structure landscapes Image-objects display features like shapes or texture which single pixels do not Integration of image-objects in vector GIS instead of raster maps classified pixel by pixel After Blaschke, Hay et al. (2014), image-objects represent meaningful entities or scene components that are distinguishable in an image. Image-objects vary depending on the scale. They serve as basic entities within imagery (Blaschke, Hay et al. 2014). Weaknesses of object-based approaches are (Hay and Castilla 2006): Many challenges in handling very large datasets; multiprocessing can be required No unique solution for the process of segmentation; small adaptions could result in different segmentation outputs No real understanding of scale and hierarchical relations among objects derived at different resolutions (Hay and Castilla 2006) Opportunities of object-based approaches are (Hay and Castilla 2006): Adaption of object-oriented methods and concepts to them possible New IT tools for object-based approaches Large datasets can be analyzed through multiprocessing, parallel processing or grid computing A possible threat of object-based approaches is that developing distinct object-based approaches could lead to isolation instead of consolidation (Hay and Castilla 2006). 15

In the next part of this seminar paper, arguments are listed why GEOBIA is a hot topic. The amount of scientific literature on GEOBIA ascended since the millennium. This growth is marked through a correlation of satellite data having with a high spatial resolution. Around the millennium, the software ecognition for doing object-based image analysis was released. Important research fields are segmentation, change detection, accuracy assessment or automation of image processing (Blaschke 2010). Based on a literature review, Blaschke (2010) states that GEOBIA can be regarded as an important trend in GIscience and also in remote sensing. According to Blaschke, Hay et al. (2014), GEOBIA can be regarded as a paradigm. Further, it was compared how many times important articles on the topic GEOBIA were cited in Google Scholar, Web of Knowledge and Scopus. Table 1 gives and overview of the sum of citations. The values for 2009 and 2013 were taken from Blaschke, Hay et al. (2014), the values for 2017 are based on an own literature review in Google Scholar, Web of Knowledge and Scopus which was conducted on February 7 th, 2017. Table 1: Citations in Google Scholar, Web of Knowledge and Scopus 2009 2013 2017 Google Scholar 799 5.255 9.842 Web of Knowledge 303 2.091 3.933 Scopus - 2.628 5.330 Overall 1.102 9.974 19.105 The values in Table 1 show that the number of citations ascended from 2009 to 2013 and also from 2013 to 2017; for the years 2009 to 2013, the increase was much higher than from 2013 to 2017. The growing number of citations indicates that GEOBIA is a hot topic. Another literature review was done in Scopus to prove if the amount of scientific literature on GEOBIA ascended since the year 2000 as stated in Blaschke (2010). The term Geographic Object-Based Image Analysis was entered and article titles, abstracts and keywords were queried. Figure 6 shows the number of publications per year since 2000. 16

60 Number of publications on GEOBIA in Scopus 50 40 30 20 Number of publications 10 0 2000 2005 2010 2015 Figure 6: Publications on GEOBIA (Scopus) A literature analysis in Web of Science where topics were queried after the term Geographic Object-Based Image Analysis showed a similar pattern. Figure 7 shows the number of publications per year since 2008. 40 35 30 25 20 15 10 5 Number of publications on GEOBIA in Web of Science 0 2008 2010 2012 2014 2016 Number of publications Figure 7: Publications on GEOBIA (Web of Science) Based on the literature analysis, the statement of Blaschke (2010) can be verified. Due to the growing number of publications and citations, GEOBIA can be regarded as a hot topic. 17

4. Indoor Positioning Systems The last years were marked through a growing importance of so called position based services (Feldmann, Kyamakya et al. 2003). For many purposes, the position of people in wireless networks needs to be calculated like for guided tours in museums using handheld devices (Feldmann, Kyamakya et al. 2003). Due to the growing use of wireless networks, methods for determining the position of persons were developed. Global Positioning Systems cannot be used for tracking persons inside buildings (Madigan, Elnahrawy et al. 2005) due to the fact that line-of-sight transmission between receivers and satellites is not possible in an indoor environment (Gu, Lo et al. 2009). Indoor positioning systems are marked through a higher complexity than outdoor positioning systems. Multi-path effects in indoor positioning are caused due to obstacles like walls or human beings which have an effect on the spread of electromagnetic waves (Gu, Lo et al. 2009). GPS systems take longitude, latitude and altitude to determine the position of an entity. Indoor positioning systems can use storey number, room number or other references to describe the position of objects (Kaemarungsi 2005). In Gu, Lo et al. (2009), the term Indoor Positioning System is defined as a system that continuously and in real-time can determine the position of something or someone in a physical space such as in a hospital, a gymnasium, a school, etc. Indoor Positioning Systems (IPS) have the aim to provide location estimates for wireless devices such as laptop computers, handheld devices, and electronic badges (Madigan, Elnahrawy et al. 2005). Due to the increase of wireless networks in facilities like airports or campuses, indoor positioning using Wi-Fi signals became more and more important (Madigan, Elnahrawy et al. 2005). Other applications for indoor positioning systems include location based service in indoor environments, police and firefighters, intelligent transportation, industry, logistics and optimization, surveying and geodesy, construction sites or underground construction (Mautz 2012). For providing exact position inside buildings, various aspects need to be covered. In contrast to outdoor positioning with GPS devices, indoor positioning is marked through smaller coverage areas with rooms and buildings. It is more difficult to receive signals from satellite inside building which led to the creation of Assisted Global Navigation Satellite System receivers. Further, the accuracy in indoor environments needs to be better than in outdoor environments. Precise indoor positioning is complex due to obstacles like walls (Mautz 2009). Wireless positioning systems are composed of three basic components: location sensing devices, positioning algorithm and display systems. The positioning process starts with the 18

location sensing devices detecting the signals transmitted by or received at known reference points using sensing technologies such as [ ] infrared, or ultrasound (Kaemarungsi 2005). In the next step, the sensing technique converts the sensed signal into location metrics that are time of arrival, angle of arrival, carrier signal phase of arrival, or received-signal-strength (Kaemarungsi 2005). Different sensing techniques use strength, time or direction of the signals to determine the position. Using known reference points, the position gets calculated based on the distance or direction of the location metrics. It is also possible that locations can be determined through certain signal characteristics. In the next step, the positioning algorithm processes the location metrics and estimates the location information using approaches such as signal processing, distance based approach, neutral network, or probabilistic approach (Kaemarungsi 2005). In the last step, the display system shows the users the position information in an appropriate format (Kaemarungsi 2005). Figure 8 shows the main parts and processes of Indoor Positioning Systems. Figure 8: Own illustration of the main parts of Indoor Positioning Systems The performance of indoor positioning technologies can be determined by different aspects like accuracy, complexity, scalability, cost or precision (Liu, Darabi et al. 2007). Figure 9 gives an overview of performance aspects which will be discussed in more detail in the following section. 19

Figure 9: Own illustration of performance aspects of an Indoor Positioning System Accuracy can be regarded as the most important requisition of indoor positioning systems. The term distance error refers to the average Euclidean distance between the estimated location and the true location (Liu, Darabi et al. 2007). Accuracy is a systematic offset of an IPS. Good indoor positioning systems are marked through a high accuracy, but also through other characteristics (Liu, Darabi et al. 2007). Precision describes how consistently the system works (Liu, Darabi et al. 2007). Precision is a measure of the robustness of the positioning technique as it reveals the variation in its performance over many trials (Liu, Darabi et al. 2007). In most cases, cumulative probability functions are taken to describe the precision of an IPS (Liu, Darabi et al. 2007). Complexity of IPS refers to the needed software and hardware. Indoor positioning systems with a low complexity are preferred. Determining the complexity of an IPS can be regarded as complex; as an indicator for complexity, location rate can be mentioned (Liu, Darabi et al. 2007). Robustness means that indoor positioning systems also work if parts of the system are out of order e.g. some signals cannot be used. The position gets determined with the signal from other measuring devices (Liu, Darabi et al. 2007). Scalability of an indoor positioning system ensures the normal positioning function when the positioning scope gets large (Liu, Darabi et al. 2007). With a higher distance between 20

receiver and transmitter, the performance of the system gets lower. For an IPS, two scales are important: geography and density. Geography refers to the area for which the indoor positioning system should work. Density refers to the number of units located per unit geographic area/space per time period (Liu, Darabi et al. 2007). Another factor for the performance of an indoor positioning system are the costs. Here, aspects like energy, weight, space, money and time play an important role. Time refers to installing and maintaining an IPS. Mobile units can be marked through weight and space issues. A substantial cost aspect is energy; some mobile devices can be energy passive resulting in an unlimited working time while others need to be recharged every several hours (Liu, Darabi et al. 2007). Mautz (2012) discuses more requirements for indoor positioning systems. One of them is called coverage which describes the spatial extension where system performance must be guaranteed by a positioning system (Mautz 2012). Three types of coverage can be distinguished: local coverage referring to small areas like single buildings scalable coverage referring to systems with the ability to increase the area by adding hardware (Mautz 2012) and global coverage where the system works worldwide like Global Navigation Satellite Systems or celestial navigation (Mautz 2012). Integrity is another requirement for indoor positioning systems referring to the reliability of it. Mautz (2012) defines integrity risks as the probability that a malfunction in the system leads to an estimated position that differs from the required position by more than an acceptable amount [ ] and that the user is not informed within the specified period of time. Availability refers to the percentage of time during which the positioning service is available for use with the required accuracy and integrity (Mautz 2012). Reduced availability can be caused through failures of the system or maintenance work. Low availability means that in less than 95 percent of the time the IPS works, regular availability that the system works in more than 99 percent of the time. Indoor positioning systems with a high availability work in more than 99.9 percent of the time. For a good availability, aspects like integrity, continuity and accuracy need to be ensured (Mautz 2012). 21

Continuity refers to the property of continuous operation of the system over a connected period of time to perform a specific function (Mautz 2012). This aspect does not differ much from availability of an indoor positioning system (Mautz 2012). Update rate refers to the frequency with which the positions are calculated on the device or at an external processing facility (Mautz 2012). Three types can be distinguished after (Mautz 2012): periodic: regular updates taking place within a certain interval on request driven by the user of the indoor positioning system on event: the position gets calculated if a particular event happens like a sensor measures a critical threshold System latency refers to the delay with which the requested information is available to the user (Mautz 2012). Different types of latency can be distinguished after (Mautz 2012): Real time latency where the delay is not really recognizable. This type gets used for navigation purposes and can be found in nearly all indoor positioning systems. Sooner the better according to the best effort of an IPS. An upper limit can be set for the maximum time of delay. Post processing without a specific time of delay Further requirements for indoor positioning systems include security and safety. Security refers to the extent of protection against some unwanted occurrence such as the invasion of privacy, theft, and the corruption of information or physical damage (Mautz 2012). Safety means that the risk of unwanted happenings should be reduced under a certain level. Additionally, aspects like privacy of the users or approvals by authorities need to be considered (Mautz 2012). Indoor positioning is a hot topic due to the growing importance of so called position based services (Feldmann, Kyamakya et al. 2003). Position based services are a vibrant and rapidly evolving application area with many active research groups, many products, and many interesting applications (Schiller and Voisard 2004). Due to the ascending number of wireless Internet devices, indoor positioning became an important topic (Madigan, Elnahrawy et al. 2005). Today, indoor positioning has many applications areas as discussed in Mautz (2012). Indoor positioning is an important topic due to the fact that people spent about 90 percent of their time in indoor environments (Coutalides 2015). 22

Further, a literature analysis was conducted. In Scopus, article titles, abstracts and keywords were queried on the term Indoor Positioning Systems. Figure 10 shows the number of publications per year since 1994. Number of publications on IPS in Scopus 1600 1400 1200 1000 800 Number of publications 600 400 200 0 1994 1999 2004 2009 2014 Figure 10: Publications on IPS (Scopus) Like before, Web of Science was also used for a literature analysis. Topics were queried on the term Indoor Positioning Systems. Figure 11 shows the number of publications per year since 1991. Number of publications on IPS in Web of Science 800 700 600 500 400 Number of publications 300 200 100 0 1991 1996 2001 2006 2011 2016 Figure 11: Publications on IPS (Web of Science) Due to the growing number of publications, Indoor Positioning Systems can be regarded as a hot topic. 23

5.Conclusion and Outlook Through a literature analysis which showed that the number of publications is ascending and other arguments like rising number of citations in case of GEOBIA or rising number of activities in case of VGI, it could be proved that the discussed topics Volunteered Geographic Information, Geographic-Object Based Image Analysis and Indoor Positioning Systems need to be considered as hot topics. GIscience, Geoinformatics respectively GeoIT are dissimilar fields with different hot topics. Based on the introductions into the topics, the author of this seminar paper would classify Volunteered Geographic Information as a hot topic in GeoIT due to the use of technological devices like GPS and in Geoinformatics due to the created information which can be used for scientific research respectively mapping. Geographic Object-Based Image Analysis can be seen as a hot topic in GIscience due to the definition of a sub discipline and in Geoinformatics due to the creation of vector data. Indoor Positioning Systems are a hot topic in GeoIT due to the performance requirements and technical equipment like Location Sensing Device, Positioning Algorithm or Display System. In this seminar paper, only three hot topics were discussed. Presumably, there are other hot topics in these domains which were not covered in here. In the next years, topics which are now considered as secondary could become hot topics in GIscience, Geoinformatics and GeoIT. On the other side, topics like the ones discussed in this seminar paper which are regarded as hot now could loose on meaning in the next years and could become minor topics in the future. 24

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