Using Volunteered Geographic Information to Measure Name Changes of Artificial Geographical Features as a Result of Political Changes: A Libya Case Study

Abstract

Over the past few years, political systems have changed in several countries of the Middle East as a result of citizen revolutions on the ruling regimes. These geopolitical changes have had effects on the names of artificial geographical features, such as roads and schools. Many of the names, especially those that were associated with previous regimes, were changed to become associated with the revolutions, their dates, their leaders, or their martyrs. The recent change in the paradigm of Web use towards data sharing and crowd-sourcing in the Web 2.0 provides new opportunities to get insight into a local community’s perception of political events. Crowd-sourced spatial data, often referred to as Volunteered Geographic Information (VGI), can be contributed and accessed through various websites and data repositories. These data can supplement traditional data sources, such as road maps hosted by governmental offices. Libya’s governmental maps of urban infrastructure are scarce and incomplete. This provides an incentive for citizens and grassroots groups to collect and generate spatial data on their own and to express changed realities of feature names by the means of crowd-sourced mapping. Using two districts in Libya this study evaluates for five Web 2.0 platforms (OpenStreetMap, Wikimapia, Google Map Maker, Panoramio, and Flickr) to which extent VGI reflects name changes of geographical features as a result of the revolution in 2011. Other data sources, such as school directories posted by teachers on Facebook, serve as additional information for feature name change detection. Results show that the extent to which VGI reflects name changes based on the 2011 revolution in Libya varies strongly between VGI data sources. VGI provides a useful supplement to limited governmental resources to better understand how names of artificial geographical features are affected by changes in political systems.

Keywords: Volunteered Geographic Information, Web 2.0, name change, crowd-sourcing, political change, Libya

1. Introduction

Libya, a country in North Africa, suffered 42 years of a dictatorial regime which seized power in a coup in 1969. In order to perpetuate its takeover, its ideals, and the names of its leaders, this regime changed many names of artificial geographical features, such as roads, schools, and plazas. These new names were used to represent the ideas of this regime and to blur the identity of the former regime in the country. Therefore, most of these new names were associated with the names of the coup, its date, its leaders, and its ideals. The regime fell in 2011 during the Libyan revolution, which officially lasted from February 15 to October 23, 2011 and was part of the Arab spring revolutionary wave. During and after the revolution, yet another change of names of artificial geographical features occurred in Libya, now reflecting names associated with the 2011 revolution, and undoing some name changes enacted in the previous regime. This new phase of name changes was also noticed by the geospatial Web community which in its own way disseminated the word about the revolution. It did so by adding updated feature names in a variety of Web-based geo-portals that allow the sharing of crowd-sourced geospatial information. This paper analyzes, using the Libyan revolution as a showcase, the usability of crowd-sourced information for the identification of name changes of artificial geographical features as a consequence of changes in a political system. The case study is based on two districts in Libya (Tripoli and Benghazi). It compares name changes between five crowd-sourced datasets and uses alternative data sources, such as local knowledge of residents in the analyzed areas, as a reference dataset for comparison when available.

1.1. Place names

Place names are a prominent research topic among geographers. Some of the many important characteristics about place names are that they reflect the identity of people, embody ideologies, and provide a common means to toponymic commemoration by creating a “geography of memory” (Alderman, 1996, p. 51). The latter means that the history of the past can be transferred to the present through commemorative place naming, such as commemorative street names that define the historical memory of a nation (Rose-Redwood, 2008). In cultural geography, place names have played a significant role in establishing new identities of nations. New identities could be achieved by renaming geographical places to deface the ideas of the former regimes (Alderman & Inwood, 2013). Place names are claimed to be essential for a better understanding of the political landscape (Zelinsky, 1983) since place naming is considered an essential part of the political process (Cohen & Kliot, 1992). Starting with the French Revolution, renaming streets commonly occurred after political changes when a new group comes to power (Azaryahu, 1997). This renaming is an act of diminishing the impact of the previous regime and establishing the authority of the new ruling group. Studies that analyze name changes of places due to political changes rely on a variety of sources to do so. Examples of sources include: newspaper articles, maps and reports generated by naming commissions for analyzing street name changes in East Berlin in the context of German reunification (Azaryahu, 1997); Websites of dedicated geographic name councils for analyzing name changes of municipalities in South Africa in the context of apartheid (Guyot & Seethal, 2007); or city government (Bucharest Primărie) documents for analyzing changes of street names in Bucharest in the context of post-socialism (Light, 2004). In geographic regions (e.g. countries,

cities) where these types of official data sources (newspaper articles, governmental databases or maps) about feature names and/or their changes are not available crowd-sourced spatial information, collected and shared on Web 2.0 platforms, could provide a viable alternative source of information for the detection of name changes. Analyzing to which extent such crowd-sourced geodata reflect names changes of artificial features is, therefore, the overarching goal of this study.

Names of artificial geographic features, including parks, schools, and streets, are typically assigned by governments at various different hierarchical administrative levels (Alderman & Inwood, 2013). However, in cases of weak public governance (e.g. due to limited resources), changed place names, may not (yet) have been officially declared but be formed by the local community within a toponymic process. The focus of this study is to analyze the change of official feature names (i.e. relating to the Gaddafi regime) to community driven names, through the Libyan revolution in 2011. Examples for non-governmental, community-based place names, can be found on several Facebook websites that were posted by local teachers in the study area. These websites contain directories of local schools with their names before and after the revolution, whereas official governmental resources for new school names do not exist. These Facebook websites allow parents to identify new names for schools of their children. The schools listed in these directories were also used during local election periods as election centers. These facts mean that those new school names, though not contained in governmental gazetteers or maps, were generally accepted as valid by the community. These community-based names are somewhat related to vernacular place names, some of which are also community driven (e.g. vernacular regions in cities). However, vernacular place names typically refer to larger areas than individual artificial features. They are vague descriptions of locations for which no official boundary exists due to their fuzzy object boundaries, or for which their commonly perceived boundaries differ from official ones (Jones, Purves, Clough, & Joho, 2008). Names of vernacular places are not the subject of this presented study.

1.2. Volunteered Geographic Information

This study revolves around crowd-sourced spatial information as a potential data source to document changes in place names where governmental reports, historic maps, or other official documents are not available. The introduction of the Web 2.0 and the integration of GPS in various mobile devices, such as smartphones and cameras, facilitated the emergence of crowd-sourcing which generates spatial information through the Web community. The Web 2.0 significantly changed Web user behavior, from primarily one-directional data consumption to a bi-directional collaboration in which users are able to interact with and provide information to central sites. Crowd-sourced spatial information is oftentimes referred to as Volunteered Geographic Information, short VGI (Goodchild, 2007a) or neogeography (Turner, 2006). Its technologies are used in emergency response, spatial decision making, participatory planning, and citizen science (Haklay, 2013). VGI comes in many forms, ranging from informal, emotional, and unstructured annotations of places, like in Wikimapia, to geometrically accurate representations of physical features, like in OpenStreetMap (OSM) (Bittner, 2016).

(Glasze & Perkins, 2015) state that crowd-sourcing offers a radical alternative to conventional ways of map making, challenging the hegemony of official and commercial cartographies. They

point out that crowd-sourcing offers a forum for different voices, elaborating on the fact that maps, and geoinformation in general, can never be neutral or objective, but instead are always embedded in specific social contexts of production and use. (Fraser Taylor & Caquard, 2006) suggest that cartography’s notion of a ‘map user’ no longer implies a discrete singular consumer of cartographic communication. Instead, online applications, developed by the open-source community, enable a user to integrate individual data to create maps in collaboration with others. The so-called democratization of GIS is facilitated by tools and applications that incorporate increased levels of interactivity and data manipulation made available to the Web community (Couclelis, 2003), and by integrating local geographic knowledge through the involvement of new communities (Dunn, 2007). A review of archived messages on the Humanitarian OpenStreetMap Team (HOT), Crisis Mappers, and Crisis Commons listserves shows that individuals and grassroots groups regularly discuss how to represent and share their knowledge and how to integrate new technologies in disaster management (Burns, 2014). (Elwood, 2008, 2010) points out that citizens and grassroots groups begin to generate spatial data that is popular among government officials, altering the roles of petitioner and provider. (Johnson & Sieber, 2013) find that governments have long been providing online services to citizens, and that nowadays governments (primarily Western-style) foster citizen engagement through Web 2.0. This approach facilitates transparency and effectiveness of government services (Brewer, Neubauer, & Geiselhart, 2006).

1.3. Paper structure

The remainder of this paper is structured as follows: The next section reviews the literature on place names and describes the characteristics of user communities that contribute crowd-sourced information, such as place names, to VGI platforms. This is followed by a description of the methodology including the study site, VGI data sources and data collection methods, and analysis. The next section discusses analysis results for each data source including the detection of name changes. This is followed by conclusions and directions for future work.

2. Background and Literature Review

Place names have become a subject of interest for numerous studies in Geography due to their important role in building the identity of nations and because they are frequently rewritten in the event of political and ideological changes (Alderman, 2008). These studies (Azaryahu, 1997; Azaryahu & Golan, 2001; Azaryahu & Kook, 2002; Cohen & Kliot, 1992) broadly illustrate how place names, especially commemorative street names, are being manipulated by the ruling elite in countries like Russia, Germany, Israel, Romania, and former Yugoslavia to reform the national and the historical identity of the nation. Scholars have also explored the role of place names for ethnicities finding and expressing their identities. As a case study for the southern United States, (Alderman, 1996; Alderman & Inwood, 2013) elaborate on how the renaming of streets in honor of Martin Luther King, Jr created a new geography of memory, commemorating the historical experiences, struggles, and achievements of African Americans in a region where its landscape was controlled by white people. Renaming of streets may indicate as a positive symbol of southern black communities given that name changes occur more often when black people control the

government or mobilize to petition their local governments. A book by (Monmonier, 2006) discusses, among others, the politics of map names in conflict zones like Cyprus and Israel, and international disputes about name features of Antarctica, the ocean floor, and the moon. It specifically elaborates on two types of applied toponyms, i.e., names of geographical features and names of settled places, omitting street names and names of constructed features. The notion that maps can never be neutral or objective but are embedded in a social context (Glasze & Perkins, 2015) becomes evident in VGI platforms, where volunteers provide their local knowledge by contributing through the GeoWeb (Stephens, 2013). One prominent example of this phenomenon is an OSM “tagging war” with disagreement over the use of Greek or Turkish names in the Turkish-controlled area of Cyprus (Perkins, 2014). Another example is continued disagreement over name forms of contested territories such as Jerusalem or in Kosovo. (Mooney & Corcoran, 2012a, 2012b) analyzed in detail the phenomenon of “tag flip-flopping”. They found that especially the values of the “name” and “highway” attributes, which are attached to mapped features in OSM, such as roads, are subject to frequent change. As for the name attribute, the authors suspect that objects with two name value assignments could be a result of place name spelling errors, incorrect naming, or splitting of a way or a polygon feature into two or more new objects, leading to objects with different names. The OSM highway attribute more closely specifies the type of mapped road, street or path, such as primary or residential. Flip-flopping of this attribute can be attributed to uncertainty among contributors regarding the designation for a given highway object. This is demonstrated for a road feature whose highway value has been flipped 88 times between “Trunk” and “Construction” by different users (Mooney & Corcoran, 2012b).

VGI can facilitate the collection of spatial information for community mapping programs and provides a promising way to include formerly excluded people in geodata creation (Tulloch, 2008). However, VGI reflects community-based biases for different VGI platforms, such as OSM and Wikimapia (Bittner, 2016). Analysis of such biases provides a more refined view of VGI usage than the often stated digital divide (Goodchild, 2007a; Heipke, 2010), which suggests different levels of access to information and technology among population groups with varying levels of socioeconomic and demographic composition (Chow, 2012). (Tulloch, 2008) describes a case study where the New Jersey State Department of Environmental Protection collected and mapped data on vernal pools across the state based on the participation of volunteers. Similarly, a U.S. Geological Survey (USGS) project encouraged volunteers to contribute data to The National Map using online editing tools (Poore, Wolf, Korris, Walter, & Matthews, 2012). Freely available governmental data has been commonly imported into various VGI data collections, such as OSM, providing a comprehensive database for the community to add on their own collected data (Zielstra, Hochmair, & Neis, 2013). (Coleman, Georgiadou, & Labonte, 2009) identify different contexts in which individuals voluntarily contribute spatial information in support of a given purpose. These contexts include mapping and navigation, social networks, civic/governmental, and emergency reporting. The paper proposes a classification of data contributors into five overlapping categories, ranging between “neophyte” (someone with no formal background in a subject) and “expert authority” (someone who possesses an established record of providing high-quality products). The study consolidates and summarizes also the various motivators behind contributions to free or open-source software and Wikipedia, which include altruism, professional or personal interest, intellectual stimulation, social reward, and pride of place. The latter is a

driving factor for individual mappers to update road centerlines in OSM or Google Map Maker in their hometown. Constructive motivational factors for contributions to VGI can be grouped into intrinsic (e.g. altruism, fun/recreation, and learning) and extrinsic (e.g. social rewards, career, and personal reputation) (Neis & Zielstra, 2014). Casual OSM mappers are primarily motivated by general principles of free availability of mapping data, i.e. help others by providing free digital maps, whereas serious mappers were more motivated by learning, gaining local knowledge, and to some extent by career motivations (Budhathoki & Haythornthwaite, 2013). (Zook, Graham, Shelton, & Gorman, 2010) review how the information production in different Web-based services was used during the Haiti relief effort. The authors point out duplicated efforts between OSM and Google Map Maker for people who utilize both sources. Since Google retains the intellectual property of all information created with Map Maker, data is not portable between OSM and Map Maker, leading to different spatial coverage between both data sources. Not all VGI contributors may be interested in providing objective or reliable information. This could be motivated by mischief, a hidden agenda of special interest groups, or malice and/or criminal intent in hope of personal gain, leading to vandalism. Vandalism is an individual or group attack on active sites for the purpose of data corruption (Neis, Goetz, & Zipf, 2012). For example, in 2015 Google experienced attacks from a user of the Map Maker platform who created a large-scale prank on the map. Therefore, Map Maker has since then been limited to selected countries (Map Maker, 2015) before it is scheduled to be retired entirely in 2017 (Map Maker, 2016). Several VGI platforms do include automated vandalism detections tools. Existing methods to counteract data vandalism in OSM, based on historical user edits, are reviewed in (Neis et al., 2012).

As opposed to VGI mapping platforms, such as OSM, Wikimapia, and Google Map Maker, photo-sharing platforms focus on point events. These platforms allow a Web user to upload geotagged images to a server and to share them with the community. Geotagging is a common method to georeference user-generated content online and to turn photographs into geographic information (Elwood, Goodchild, & Sui, 2012). Shared photos can be annotated with a variety of metadata, including textual tags, title, geographic position, and capture time. (Ames & Naaman, 2007) developed a taxonomy of motivations to annotate Flickr photos. The first dimension, “sociality,” relates to whether the tag’s intended usage is by the individual photographer him or herself, or by others. The second dimension, “function,” refers to a tag’s intended uses, which can be either to facilitate later organization and photo retrieval or to communicate some additional context to photo viewers. (Hollenstein & Purves, 2010) found that 70% of all analyzed georeferenced Flickr images include specific place name tags, where place names at the granularity of city names were by far the most common ones. Comparing geotagged position and image content a study by (Zielstra & Hochmair, 2013) revealed that Panoramio photos have a better positional accuracy than Flickr images and that positional accuracy varies by world region for both data sources. Regarding the localness of user generated content, (Hecht & Gergle, 2010) found that 53 percent of Flickr users contribute, on average, content that is 100 km or less from their specified home location, whereas the corresponding number drops to 23% for the English Wikipedia. (Neis, Zielstra, & Zipf, 2013) showed for OSM that for some world regions a large percentage of contributions (e.g. Istanbul with slightly over 50%) comes from members whose home region is separated by more than 1000 km from the analyzed area. (Zielstra, Hochmair, Neis,

& Tonini, 2014) found that OSM users contribute a more diverse set of features in their home region than in external regions.

3. Methodology

3.1. Study sites

This research uses two districts, Tripoli and Benghazi, as study sites. Tripoli is one of 22 districts of Libya and located in the northwestern part of the country on the Mediterranean Sea. Its population is about 1,067,000 and its area is about 835 km² (322 mi²). Benghazi is another district of Libya and located in the northeastern part of the country on the Mediterranean Sea with a population of about 667,000 and an area of about 11,372 km² (4390 mi²). By comparing five VGI data sources, this study reviews name changes of 49 artificial areal geographic features in the Tripoli district and 44 in the Benghazi district in or after 2011. The analysis is restricted to those features for which a name change was either reflected in any of the five VGI platforms or in one of the alternative data sources (e.g. Facebook Websites) we had access to. Areal features analyzed include universities, institutes, schools, kindergartens, mosques, clinics, stadiums, towers, neighborhoods, squares, and markets. In addition, the study analyzes name changes for five major roads in Tripoli and for four major roads in Benghazi.

3.2. Data sources and data collection methods

The analysis in this study is limited to artificial geographic features and excludes natural geographic features, such as lakes and rivers. This is because natural features in the study area did not undergo name changes as a result of political changes. Information about name changes of artificial geographical features comes from two types of sources which are (1) VGI datasets (OSM, Wikimapia, Google Map Maker, Panoramio, and Flickr) and (2) Facebook sites listing local school directories as well as local knowledge and personal communication with local residents. Regarding VGI as a data source for name changes, only features satisfying at least one of the following conditions were retained for further analysis: (a) At least one old VGI name among all VGI sources was associated with the previous regime; (b) At least one new VGI name was associated with the revolution.

OSM is an open source mapping platform that provides online map tools to create, store, and edit geographic features (e.g., roads, land use, or buildings) on a worldwide map. For the analysis, the full history planet dump file, which stores edits of each feature ever created in OSM, was used. The file was downloaded in March 2015 in pbf format and the history data of Libya was extracted using the OSM-history-splitter tool. This splitter tool extracts a data subset from the full history planet dump based on geographic boundaries. The data was then imported into a PostgreSQL database, and SQL queries were used to identify dates of name changes for all features in the database. Only features falling inside the boundaries of the two analyzed districts in Libya were retained for further analysis.

Wikimapia, another mapping platform analyzed for this study, allows users to draw rectangles and polygons at places of interest, to add an object description, and to modify place entries (both location and description) that were created by other users (Mummidi & Krumm, 2008). The

Wikimapia application programming interface (API) facilitates searching, updating, and downloading data from Wikimapia maps. However, the history of feature edits must be reviewed manually on the website itself. Besides using the typical search terms associated with the former regime and the Libyan revolution in the free text search field, features from relevant classes, such as schools, hospitals, and universities, were selected through the “Categories” menu and manually reviewed for name changes. Figure 1 illustrates the identification of a feature name change as an example. It shows that the Al-Fateh University (a name associated with the Gadhafi dictatorial regime) was renamed to the more neutral name Tripoli University (see upper text box). Besides changing a place name, Wikimapia allows users also to edit the description of a feature, as shown in the lower text box in Figure 1.

Figure 1. Editing history in Wikimapia.

Google Map Maker is an online application that provides users with tools to add, edit, and update map information of geographic features, such as roads and buildings. Feature changes will, after a review process by Google, be reflected on Google Maps. Google Map Maker has been available since 2008 and is to be retired in March 2017 (Map Maker, 2016), with its editing functions being gradually migrated to Google Maps. For our study, the free text search field was used to search for features whose names were associated with the former regime and the revolution. The editing history was manually reviewed for identified objects in March 2105. As opposed to Wikimapia, Google Map Mapper provides exact dates of edits independent of the time that has passed since the edit. Figure 2 shows the editing of the same university feature as before in Google Map Maker, which was performed on August 26, 2011. As can be seen, the old and new names match between Wikimapia and Google Map Maker in Arabic and English except for small differences in the order of words in English, revealing general data consistency in this example.

Figure 2. Editing history in Google Map Maker.

The photo-sharing platforms Panoramio and Flickr are the two remaining VGI data sources in the presented analysis. Both platforms provide an API, which was used to download geotagged photos for Tripoli and Benghazi in April 2015. Place names, which can be attached to the photo by the user, were obtained from photo titles and other tags. Place names in analyzed geotagged photos stem exclusively from the knowledge of local users who manually geotagged photos. This is because automated geotagging of photos through mobile devices requires detailed base maps with feature names. Such base maps and underlying detailed gazetteers do not exist for the analyzed areas in this study. Since the Panoramio and Flickr APIs do not provide a photo editing history, a name change of the artificial geographic feature of interest could only be identified by comparing titles and tags between nearby photos that were posted before and after the revolution date, respectively. The geographic focus for this comparison was on locations where the other three map based VGI sources, or alternative data sources, indicated a name change during or after 2011.

The second source of information on feature name changes was knowledge from local residents and local institutions that was not mapped on a VGI platform but available in some other form. We reached out to the Urban Planning Department of the Libyan government to obtain documentation about changes of place names after the revolution, which was, however, not available from the department. Instead, the website called “The Electronic Gate for Schools” (http://smsm.ly/maps/), which was created by the Department of Technologies and Maintenance of Educational Facilities, provided changed names of schools. The Website allows the public to add new schools to the map, to submit a report about name changes of existing schools, and to describe and categorize schools. Facebook, which is popular in Libya, provided further information on this topic. Several Facebook pages were used by local teachers to introduce the new names of local schools whose names were changed after the revolution. Parents use these

pages as directories to identify new names given to the schools of their children. Also, for elections taking place after the revolution, government agencies used the schools listed in these directories as election centers. Another resource for changed place names was local residents in both districts. Some of these residents were contacted and asked to share their local knowledge about name changes of any artificial geographic features.

The first author’s local knowledge about naming conventions of places in Libya before and after the 2011 revolution was crucial to identify feature names associated with the former regime and with the revolution. Typical terms used in names associated with the former regime include “First of September”, “Al-Jamahiriya”, and “Al Fateh Revolution”, whereas terms associated with the Libyan revolution in 2011 include, for example, “17th February”, “independence”, and “martyrs”. Especially for OSM, Wikimapia, and Google Map Maker searching for these core terms written in Arabic language was the key to find features with names relating to the period before and after the revolution. VGI platforms allow typing in tag values as free text and in different languages, and spelling errors may occur (Longueville, Luraschi, Smits, Peedell, & Groeve, 2010). For the analyzed features, Arabic and English were the only two languages that users used to add or update place information on the five used VGI sources. Therefore, before-after name comparison of a feature may involve these two languages. Use of Arabic, which is the official language in that region, points towards a local user, as opposed to tourists who more likely add place related information in English. Several spelling errors were detected in VGI based place names. However, spelling errors were ignored for the purpose of name change detection in our analysis if the name portraits the correct meaning or the correct description.

Although most of the old feature names were associated with the names of the Gadhafi dictatorial regime, not all of them were changed to be associated with names of the revolution. Instead, some feature names were changed to be associated with their city or region (e.g. University of Tripoli), or they represented more general names (e.g. New Libya School). Similarly, some of the old features names were not associated with the Gadhafi dictatorial regime, but they were nevertheless changed to be associated with the names of the revolution. For example, the Court Square in Benghazi was renamed to Freedom Square when the revolution began.

Figure 3 maps polygon and road features in both districts that were analyzed in this study. With a few exceptions, all of these features have new names that are associated with the Libyan revolution in at least one of the used reference sources. Features in the map are classified by the source that provides the new feature name, i.e. VGI, other local knowledge (labeled “people”), or both. Figure 3 shows that VGI and alternative information sources mostly complement each other since for only a few features new names were mentioned in both types of data sources (orange square symbol). Although the location with new names obtained from VGI overlaps only little with local residents’ knowledge about actual name changes, the first author’s local knowledge was used to check VGI name changes for plausibility, besides applying VGI internal data checks. Information on new names for road features was either provided through VGI or local knowledge, but not through both.

Figure 3. New names of artificial geographic features from VGI and people.

3.3. Analysis

Not all objects (polygons and streets) were present in each of the five VGI sources. Therefore, the first step was to identify all locations that had either (1) an artificial object with a name change in at least one VGI data source or (2) an artificial object mentioned in at least one of the alternative data sources (e.g. Facebook page). This process resulted in 49 polygon and five road features for Tripoli, and 44 polygon and four road features for Benghazi. Next, all of these locations were classified into five labeling categories for each VGI source (see Table 1 through Table 4). The labeling categories are sorted by the amount of information their features provide regarding names and name changes. A feature was assigned to the first labeling category if it was completely missing in a VGI source, i.e. not mapped, but if it existed in another data source. For Panoramio and Flickr, the existence of a feature was determined by whether it was shown in at least one photo nearby the location or not (independent of title or tag information). A feature was assigned to the second labeling category if it was mapped in the VGI dataset but lacked any name information.

An object was assigned to the third labeling category (“old name”) if it had only a name that was used before the revolution but not updated. The “new name” category holds features that contain only names used after the revolution but no names from before the revolution. Finally, the fifth labeling category contains features with both old and new names, i.e. reflecting a name change during or after the revolution. An example for the last class was provided in Figure 1 and Figure 2. Changes were only analyzed up to the first occurrence during or after the revolution for the analysis. Since the posed research question evolves around the 2011 revolution, subsequent name changes were not considered.

Another aspect of the conducted analysis was the distinction between local and external VGI users who contributed to name changes of the artificial features in a specific VGI data source. For the three-map based VGI sources (OSM, Wikimapia, and Google Map Maker), the distinction between local and external user was determined from the number of contributions within and outside Libya. A contributor is considered local if the total number of changes made by the contributor within Libya exceeds or equals that of the number of changes made outside Libya. A contributor is considered external if that is not the case. In Wikimapia and Google Map Maker, there are some contributors that cannot be identified as local or external because they contributed to these VGI sources without usernames, making it impossible to see their editing history. These contributors were classified as unknown users in the analysis. For Panoramio and Flickr, local and external contributors were distinguished by the number of days between first and last photo contribution of a user in Libya (Hauthal & Burghardt, 2016). More specifically, the contributor is considered external if this date difference is 30 days or less, and local if the difference is more than 30 days. A contributor with just one photo uploaded was classified as an unknown user.

The second source of information on feature name changes was local knowledge. For the few features where new feature names come both from VGI and local knowledge, the latter was used as a reference data source to check the former regarding the correctness of the changed names. That is, if local knowledge was available for a feature, only those VGI features where the old or new name matched the local knowledge were retained.

When no alternative local data source was available the most likely correct feature name was based on the majority of the used VGI data sources that shared the same feature name. Besides a few exceptions, all features analyzed in this study that had an old or new feature name in several VGI data sources shared similar names that matched and were therefore assumed to be correct. If an old and new feature name appeared in only one VGI data source that feature was still retained (and assumed correct). This was to increase the sample size of the study in order to be able to derive more informative VGI editing patterns as a result of political changes.

4. Results

The section begins with presenting results relating to the primary research question of this study, namely to what extent which VGI data is able to indicate a name change. This is followed by characterizing the user base that caused name changes in the analyzed VGI data sources in terms of local vs. external users. The last part reports results about the reliability of name changes.

4.1. Detection of name changes

Table 1 lists for Tripoli and the five VGI sources the classification of polygon features into the five labeling categories. Results in the first category show that Flickr has the highest number of missing features among the five VGI sources, whereas Wikimapia has no missing features. Looking at the last three categories, it can be seen that Wikimapia provides the highest number of objects with old and new names (21), which means that it is the most complete individual data source to consult for name changes. The next best data source in this aspect are Google Map Maker and Panoramio with seven features containing old and new names. OSM provides the least amount of relevant information since only one feature was found to reflect name changes. The shared photo portal Flickr ranges somewhere in-between. The bottom row in Table 1 provides for each VGI data source the number of different contributors that added new feature names for those features falling into the fourth and fifth category. It shows that each data source has a diverse pool of data contributors and that updates were not provided by a single person only. Wikimapia, with the largest number of mapped features, exhibits also the largest number of different mappers. Figure 4 maps the classification results. The maps reveal different spatial patterns of mapped and missing features between the five used VGI sources. Wikimapia maps all analyzed features both in the center of Tripoli and its suburbs. For OSM and Flickr, mapped features are primarily concentrated in the city center, whereas Google Map Maker and Panoramio demonstrate about the same level of completeness in the city center and in the suburbs.

Table 1. Number of geographical polygon features in different labeling categories for Tripoli. Labeling category OSM Wikimapia Google Map Maker Panoramio Flickr Feature does not exist 34 0 20 20 43 Feature without name 9 2 3 8 2 Feature with old name only 3 22 9 11 0 Feature with new name only 2 4 10 3 1 Feature with old and new name 1 21 7 7 3 Total 49 49 49 49 49 No. of different users updating 3 21 13 4 4

Based on data from Table 1, a chi-square test of independence was performed to examine the relation between labeling category and VGI platform. The relation between these variables is significant, X2 (16, N=245) =131.46, p < 0.00001. This means that the proportion of features falling into the different labeling categories varies by VGI data source.

Figure 4. Labeling of geographic features in Tripoli.

Table 2 shows the same type of count data for Benghazi. Some differences compared to the Tripoli count data (Table 1) can be observed. First, Panoramio has a much higher proportion of missing features than for Tripoli. A reason could be that this district is less frequently visited by tourists who are the main group of contributors to Panoramio. Second, the number of features reflecting a name change (last category) is for all VGI categories equal to or lower than the numbers found for Tripoli. Wikimapia is the only VGI data source providing name updates for more than one feature. Although the total number of mapped OSM features is higher for Benghazi than for Tripoli, for only one feature the name has been updated as well. In general, the low combined numbers from the last two categories indicate that the major source for updated names in this district comes from sources other than VGI, e.g. Facebook or personal communication, revealing that the VGI community is not as established in this district as in Tripoli. Figure 5 maps results from Table 2 for Benghazi. As for Tripoli, Wikimapia maps all the features both within the center and the suburbs of the city. OSM and Google Map Maker show similar relative densities in mapped

features between the city center and suburbs. Both Panoramio and Flickr map the same two features, one in the city center, and one far out in the suburbs.

Table 2. Number of geographic polygon features in different labeling categories for Benghazi. Labeling category OSM Wikimapia Google Map Maker Panoramio Flickr Feature does not exist 26 0 31 42 42 Feature without name 5 1 1 0 1 Feature with old name only 9 38 6 1 0 Feature with new name only 3 1 5 1 1 Feature with old and new name 1 4 1 0 0 Total 44 44 44 44 44 No. of different users updating 2 7 5 1 1

Based on data from Table 2, a chi-square test of independence was performed to examine the relation between labeling category and VGI platform. The relation between these variables is significant, X2 (16, N=220) =157.10, p < 0.00001. This means that the proportion of features falling into the different labeling categories varies by VGI data source.

Figure 5. Labeling of geographic features in Benghazi.

Table 3 lists for Tripoli and the five VGI sources the classification of road features into the five labeling categories. In general, few roads with name changes were identified from VGI and non-VGI sources both for Tripoli and for Benghazi, meaning that apparently fewer roads than areal features were renamed after the revolution. All five data sources mapped at least some streets, and all data sources except for Flickr reflected also name changes for some streets. OSM, Wikimapia, and Google Map Maker provide, relatively speaking, the most comprehensive information about road name updates, whereas shared images provide the least comprehensive information. This pattern differs somewhat from Table 1, where shared photo services performed better than OSM in the fifth category. This indicates that the OSM community focuses generally more on roads, whereas shared photos are mostly taken from areal objects of limited spatial extent, such as buildings or plazas. Figure 6 maps these results, where black and green for Flickr, indicate that this data source does not provide any information on street names.

Table 3. Number of streets in different labeling categories in Tripoli. Labeling category OSM Wikimapia Google Map Maker Panoramio FlickrStreet does not exist 0 0 0 1 2 Street without name 0 0 0 1 3 Street with old name only 1 1 1 1 0 Street with new name only 0 0 1 0 0 Street with old and new name 4 4 3 2 0 Total 5 5 5 5 5 No. of different users updating 4 3 2 1 0

Figure 6. Labeling of streets in Tripoli.

Table 4 provides corresponding numbers for Benghazi. As was already observed for polygon features, the data is less complete than for Tripoli with respect to name update information. Benghazi streets are the only dataset where Wikimapia does not provide any name updates, whereas Google Map Mapper reveals the highest number of features that contain old and new

names. None of the two photo sharing services provides any information about updated names. Results for Benghazi streets are mapped in Figure 7. OSM, Wikimapia, and Google Map Maker map all the used streets of the city, whereas Panoramio and Flickr have just one mapped street.

Table 4. Number of streets in different labeling categories in Benghazi. Labeling category OSM Wikimapia Google Map Maker Panoramio FlickrStreet does not exist 0 0 0 3 3 Street without name 1 0 0 0 0 Street with old name only 0 4 2 1 1 Street with new name only 2 0 0 0 0 Street with old and new name 1 0 2 0 0 Total 4 4 4 4 4 No. of different users updating 2 0 2 0 0

Figure 7. Labeling of streets in Benghazi.

4.2. Local and external contributors

Figure 8 plots for each data source the number of local, external, and unknown contributors who contributed at least one old feature name. For Flickr, Panoramio, and OSM old feature names come primarily from local contributors. Similarly, in Wikimapia most contributors who could be classified based on their editing history were local. However, more than a half of user could not be classified. Google Map Maker reveals very little information in this aspect, due to numerous anonymous edits.

Figure 8. Type of contributors providing old place names.

Figure 9 shows a similar pattern for users contributing new place names. It clearly demonstrates that the local contributors are primarily responsible for providing new name information in the various VGI data sources. The percentage of unknown users in Google Map Maker and Wikimapia declined compared to contributions of old feature names.

Figure 9. Type of contributors providing new place names.

0 10 20 30 40 50 60 70 80 90 100

OSM

Wikimapia

Google Map Maker

Panoramio

Flickr

Number of users

local external unknown

0 10 20 30 40

OSM

Wikimapia

Google Map Maker

Panoramio

Flickr

Number of users

local external unknown

4.3. Reliability of name changes

For features where a local reference information (e.g. Facebook page entry) was not available to verify the new name, the most likely old and new feature names were determined based on the majority vote among the five VGI data sources. Figure 10 shows how many VGI data sources shared an old or new name on features that had no local knowledge reference. In general, the old feature names appear more often on more than one different VGI platform than the new names, meaning that old names are somewhat more reliable than new ones. Features that have their name shown in only one VGI data source (left most group of bars) cannot be checked through VGI data sources alone, but were still retained for further analysis.

Figure 10. Number of old and new feature names with no local knowledge reference shared on different numbers of VGI sources.

Sometimes, since different mappers were involved in reporting name changes for the same feature on different VGI websites, updated feature names varied slightly between VGI data sources. Differences can be partially explained by variations in the translation from Arabic to English. An example is the new name of the Al-Fateh University (a name associated with the Gadhafi dictatorial regime). The name was updated to Tripoli University in Wikimapia and to University of Tripoli in Google Map Maker (compare Figure 1 and Figure 2). However, for some updated names, differences between VGI data sources went beyond the typical nuances caused by translations or spelling errors. In total, two names in OSM, two names in Google Map Maker and Panoramio, and six names in Wikimapia were updated differently than the most likely correct new names. For such a feature, the new (incorrect) name for the affected VGI source was ignored (i.e. treated like absent) when it came to assigning the VGI data source to one of the five labeling categories (Table 1, Table 2). Such a feature could then only be assigned either to the first or the second label category for that VGI data source.

0

10

20

30

40

1 source 2 sources 3 sources 4 sources 5 sources

Number of features

old name new name

5. Conclusions and Future Work

Political changes, which occurred as a result of the revolution in Libya in 2011, had a clear impact on names of artificial geographical features, such as streets, schools, and hospitals, which were originally associated with the former regime. Some information about these changes was reflected in data disseminated by the geospatial Web community. This is particularly valuable because the Libyan government has no websites or maps that individuals can consult to obtain current name information. This case study demonstrates an example of the democratization of GIS, where citizens and grassroots groups share their knowledge to generate spatial data that are not provided by government officials (Dunn, 2007; Elwood, 2008). In addition to this, VGI provides, especially in the absence of official governmental data, information in a special social context (that of the aftermath of the Libyan revolution), and may therefore not be completely objective (Glasze & Perkins, 2015). VGI platforms offer therefore a forum for different voices, reflecting that state of perception of a local environment at a given time.

For Tripoli, more data reflecting name changes could be obtained from VGI data sources than from local knowledge sources, illustrating the effectiveness of a system that uses citizens as voluntary sensors (Goodchild, 2007b) in an organized way. The comparison of VGI data completeness indicates that Wikimapia, at least in this region of the world, provides generally the most complete data source among all analyzed VGI data sources and is one of the preferred VGI platforms by local citizens. In general, VGI data provided more information in Tripoli than in Benghazi, reflecting the varying degree of data quality between different locations (Hochmair & Zielstra, 2015; Neis et al., 2013).

The presented approach can be transferred to other places around the world and be applied to man-made events (e.g. political) or natural events (e.g. earthquakes) that have effects on geographic features mapped in several VGI sources. The challenge of finding named features in sparsely mapped regions, like in Tripoli and Benghazi, and determining the characteristics of toponymy before and after the event, will remain.

For future work, we plan to expand this study to other regions of the Arab world and beyond, where we expect that political events cause a substantial number of changes in feature names, too. We will also review alternative data sources, such as tweets or images from social media, regarding their usefulness of detecting name changes of man-made features.

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