# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 2 Materials And Methods
### Data Pre-Processing  
We encountered data quality problems with our geospatial data. Many data points appear to be mislocated or mislabeled in our collected dataset. The descriptive statistics revealed that ads are being published in very close locations. For instance, in an area of approximately 100 m\({}^{2}\) located tens of real estates, indicating an issue of disguised missing data, which is defined as intentionally and systematically coding missing data as valid data values. In [PERSON] et al. (2014), a police station's coordinates were entered as the locations of some traffic accidents in a region, which appeared as a hot spot in the analysis. Similar to this problem, the same coordinates might be systematically used for different real estates. To solve this problem, we first rounded the latitude and longitude decimals to a certain accuracy after experimenting with a different number of decimal places and considering the GPS signal sensitivity. We have rounded them to up to four decimal places resulting in, on average, a 3.78 m change between the actual locations and the rounded latitude and longitude decimals. Table 1 shows the highest number of POI counts per latitude and longitude pairs rounded up to four decimal places. As a result, we ended up with only 61,096 unique locations for Ankara and 4921 unique points for Kadikoy. However, we still observed that even rounded coordinates of the locations included several ads, which is questionable as it is very unlikely to see so many ads in a very limited area. Therefore, we manually checked these locations. We noticed that several real estate ads are located very close to a real estate agency, indicating that the coordinates of the ads belong to the real estate agency but not the advertised address in the text. For instance, Figure 2 presents a building where tens of real estate locations are advertised as the same building and having a real estate agency in the same building according to the Google Maps data. To solve the issue of the disguised missing data problem, we finally used the Google Maps API to check whether there is any real estate agency around each rounded location. We discovered that at least one or more real estate properties are available in places with a high number of real estate advertisements. We kept looking at the vicinity of places with a high number of real estate accumulations until we did not discover any real estate agency nearby. In the end, we found that places with less than three real estate do not have any close real estate agencies within a radius of 250 m. That is why we assumed all the rounded locations having more than two real estates as disguised and discarded. After removing \begin{table}
\begin{tabular}{c c c} \hline
**Latitude** & **Longitude** & **Real Estate Count** \\ \hline
39.9836 & 32.8218 & 201 \\
39.9838 & 32.8217 & 153 \\
39.9755 & 32.8281 & 112 \\
39.9838 & 32.8218 & 107 \\
40.0430 & 32.8998 & 97 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of real estate locations per rounded latitude and longitude. Figure 2: Dots represent the real estate located in the same building, and a small image located on the top left presents the real estate agency located in the same spot. them, we ended up with 60,031 unique ad listings for Ankara and 4847 unique ad listings for Kadikoy. We noticed that out of the 650,000 shapefiles in the land registry data, only 45,000 of them were unique. The reason why we have seen lots of duplicate shapefiles result from the small step size we defined in order not to miss any shapefiles. After eliminating the duplicates, we examined data quality issues. We noticed that some of the parcels that we downloaded have issues of conflicting with other parcels. That is, the parcels intersect with others. Usually, one does not expect to see any intersection with parcels as each parcel represents someone's registered land, according to Turkey's land registry office.