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Figure 6. Easting, northing, and positional deviations for Miami Beach: blue and red lines show the positive and negative deviations, respectively.

Figure 6 Easting, northing, and positional deviations for Miami Beach: blue and red lines show the positive and negative deviations, respectively.

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Table 1. Examples of postal addresses scraped from www.booking.com website automatically.
Table 1. Examples of postal addresses scraped from www.booking.com website automatically.
A point of interest (POD) is a specific point location that someone may find useful or interesting. People use POIs for various purposes, such as navigating between different locations, determining the characteristics of a place, studying urban sociology, analyzing city dynamics, and geo-referencing of the texts (Rodrigues 2010). In this study, the accommodation facilities which supply communication between the dynamics of the cities (Bilgi et al. 2019) were used as the reference data. They are usually stored by point symbols in a POI layer of various online maps (Mulazimoglu and Basaraner, 2019). Accordingly, two test regions were selected: the district of Fatih in Turkey and the city of Miami Beach in the USA. Fatih is located in the historical peninsula of Istanbul, surrounded by the Byzantine city walls, the Golden Horn, and the Sea of Marmara (Figure 1). This region, which has harbored many civilizations throughout its thousands of year’s history, is currently the most significant historical tourism center of Istanbul. The other test region, Miami Beach, is a famous coastal resort city in Florida (Figure 2). The city is a major international entertainment and cultural destination. It is a widely visited tourist area and offers various accommodation facilities. Figure 1. Approximate boundary of the study area within Fatih and accommodation facilities (blue points).
A point of interest (POD) is a specific point location that someone may find useful or interesting. People use POIs for various purposes, such as navigating between different locations, determining the characteristics of a place, studying urban sociology, analyzing city dynamics, and geo-referencing of the texts (Rodrigues 2010). In this study, the accommodation facilities which supply communication between the dynamics of the cities (Bilgi et al. 2019) were used as the reference data. They are usually stored by point symbols in a POI layer of various online maps (Mulazimoglu and Basaraner, 2019). Accordingly, two test regions were selected: the district of Fatih in Turkey and the city of Miami Beach in the USA. Fatih is located in the historical peninsula of Istanbul, surrounded by the Byzantine city walls, the Golden Horn, and the Sea of Marmara (Figure 1). This region, which has harbored many civilizations throughout its thousands of year’s history, is currently the most significant historical tourism center of Istanbul. The other test region, Miami Beach, is a famous coastal resort city in Florida (Figure 2). The city is a major international entertainment and cultural destination. It is a widely visited tourist area and offers various accommodation facilities. Figure 1. Approximate boundary of the study area within Fatih and accommodation facilities (blue points).
Figure 3. a) Street view and b) Map view of a reference hotel in Fatih.
Figure 3. a) Street view and b) Map view of a reference hotel in Fatih.
reviewing the entire data. The remaining addresses were considered identifiers of POIs, and their approximate locations were geocoded from the facility name by using Google Maps and Bing Maps services. Then, the advertising signs of the facilities over the street perspectives were checked to confirm the accuracy of the geocoding process based on the facility names (Figure 3a and Figure 4a). Thus, the building footprints of 74 hotels in Fatih and 82 hotels in Miami Beach were verified as reference data for test processes (Figure 3b and Figure 4b). Figure 2. Approximate boundary of the study area within Miami Beach and accommodation facilities (blue points).
reviewing the entire data. The remaining addresses were considered identifiers of POIs, and their approximate locations were geocoded from the facility name by using Google Maps and Bing Maps services. Then, the advertising signs of the facilities over the street perspectives were checked to confirm the accuracy of the geocoding process based on the facility names (Figure 3a and Figure 4a). Thus, the building footprints of 74 hotels in Fatih and 82 hotels in Miami Beach were verified as reference data for test processes (Figure 3b and Figure 4b). Figure 2. Approximate boundary of the study area within Miami Beach and accommodation facilities (blue points).
Table 2. Postal addresses parsed by services in a standard format.
Table 2. Postal addresses parsed by services in a standard format.
Figure 4. a) Street view and b) Map view of a reference hotel in Miami Beach.
Figure 4. a) Street view and b) Map view of a reference hotel in Miami Beach.
In Turkish addressing format, the sub-region generally described by a neighborhood name is a crucial element. In some _ cases, buildings such as accommodation facilities handled in this study may be located within a different sub-region despite being on the same street. This paradox is the most fundamental issue that has not been solved by global geocoding services such as Google Maps and Bing Maps in Turkey. Such examples can be seen in the second and third addresses in Table 3 for both services in Fatih. On the other hand, both services use their particular address formats in Fatih. Bing Maps gives a house number to the structure after the street name and type, yet Google Maps always returns a normalized postal address together with an abbreviation “No:” for the same place used in Bing Maps. Table 3. Postal addresses normalized by services in a standard format.
In Turkish addressing format, the sub-region generally described by a neighborhood name is a crucial element. In some _ cases, buildings such as accommodation facilities handled in this study may be located within a different sub-region despite being on the same street. This paradox is the most fundamental issue that has not been solved by global geocoding services such as Google Maps and Bing Maps in Turkey. Such examples can be seen in the second and third addresses in Table 3 for both services in Fatih. On the other hand, both services use their particular address formats in Fatih. Bing Maps gives a house number to the structure after the street name and type, yet Google Maps always returns a normalized postal address together with an abbreviation “No:” for the same place used in Bing Maps. Table 3. Postal addresses normalized by services in a standard format.
Table 2. (Continued).
Table 2. (Continued).
The calculated deviations in Fatih and Miami Beach are shown on the graphs in Figure 5 and Figure 6, respectively. The horizontal coordinate in all charts represents the rank, which increases depending on the positional deviation value calculated for each accommodation facility. The vertical coordinates of AE and AN graphs show positive or negative deviations Figure 5. Easting, northing, and positional deviations for Fatih: blue and red lines show the positive and negative deviations, respectively.
The calculated deviations in Fatih and Miami Beach are shown on the graphs in Figure 5 and Figure 6, respectively. The horizontal coordinate in all charts represents the rank, which increases depending on the positional deviation value calculated for each accommodation facility. The vertical coordinates of AE and AN graphs show positive or negative deviations Figure 5. Easting, northing, and positional deviations for Fatih: blue and red lines show the positive and negative deviations, respectively.
On t he other hand, when Google Maps’ statistical values were examined, it is found that the amount of deviations within Fatih is lower than the differences wit geocodin geo. and bui stat. test Ma; com) istica! ps. hin Miami Beach. The reason for better results of g in Fatih is the fact that most of the building metry in Miami Beach consists of non-rectangular plex shapes, and also an enormous amount of dings in Fatih has simple geometry. Besides, the values are insufficient to make decisions in the region, where Bing Maps results better than Google Table 4. Statistical measures calculated from deviation values.
On t he other hand, when Google Maps’ statistical values were examined, it is found that the amount of deviations within Fatih is lower than the differences wit geocodin geo. and bui stat. test Ma; com) istica! ps. hin Miami Beach. The reason for better results of g in Fatih is the fact that most of the building metry in Miami Beach consists of non-rectangular plex shapes, and also an enormous amount of dings in Fatih has simple geometry. Besides, the values are insufficient to make decisions in the region, where Bing Maps results better than Google Table 4. Statistical measures calculated from deviation values.
not always possible. The red spot is improper because the geocoding service locates it outside the building footprint. The numbers and percentages of point-in- polygon analysis in each test region used in this study are presented in Table 5. Figure 7. Geocoding points located inside and outside the digitized buildings.
not always possible. The red spot is improper because the geocoding service locates it outside the building footprint. The numbers and percentages of point-in- polygon analysis in each test region used in this study are presented in Table 5. Figure 7. Geocoding points located inside and outside the digitized buildings.
Table 5. Point-in-polygon analysis for geocoding services. On the other hand, the best geocoding location for an address is a place close to the front edge of the building footprint. The front side is determined by considering the actual status of the building's main entrance door. Considering that the proper geocoding point is on a building's footprint boundary, if a geocoded point is within the footprint, the address can be assumed to be geocoded correctly. It means that the location of the geocoded point can be varied from the centroid to the footprint of a building. The quantity of error for a geocoding point, which is outside the footprint, is calculated from the closest distance between the location and the related footprint edges (Figure 7). Table 6 provides some statistical values for the improper geocoding points in each test region. These show that geocoding results obtained from Google Maps are of higher quality than Bing Maps, and the results in Miami Beach are more reliable than the results in Fatih.
Table 5. Point-in-polygon analysis for geocoding services. On the other hand, the best geocoding location for an address is a place close to the front edge of the building footprint. The front side is determined by considering the actual status of the building's main entrance door. Considering that the proper geocoding point is on a building's footprint boundary, if a geocoded point is within the footprint, the address can be assumed to be geocoded correctly. It means that the location of the geocoded point can be varied from the centroid to the footprint of a building. The quantity of error for a geocoding point, which is outside the footprint, is calculated from the closest distance between the location and the related footprint edges (Figure 7). Table 6 provides some statistical values for the improper geocoding points in each test region. These show that geocoding results obtained from Google Maps are of higher quality than Bing Maps, and the results in Miami Beach are more reliable than the results in Fatih.
Table 6. Statistical measures calculated for improper (outside) geocoded points. 4.2 Address Similarity
Table 6. Statistical measures calculated for improper (outside) geocoded points. 4.2 Address Similarity
Table 7. Similarity percentages for Google Maps, Bing Maps geocoded addresses, and reference addresses.
Table 7. Similarity percentages for Google Maps, Bing Maps geocoded addresses, and reference addresses.
Figure 8. Address similarities for geocoding between a) Scraped and Google Maps data, b) Scraped and Bing Maps data c) Google Maps and Bing Maps data.
Figure 8. Address similarities for geocoding between a) Scraped and Google Maps data, b) Scraped and Bing Maps data c) Google Maps and Bing Maps data.
Related topics:
EngineeringComputer ScienceInformation RetrievalText SimilarityBIng MapsAddress GeocodingSimilarity Geometry
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