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Renderings of infrastructure at three different resolutions and zoom levels: (a) Sina at zoom level 9, 0.7 degrees per grid cell; (b) Open at zoom level 10, 0.35 degrees per grid cell; (c) Google at zoom level 11, 0.18 degrees per grid cell. All three images have been brightened and cropped for publication. For convenience, the data are available from the author's website at 168.144.172.143/infra.html.
Source publication
Researchers in many fields have needed to develop a measure of infrastructure, and many proxies have been used toward this end, such as night light data and the Digital Chart of the World. Yet there are issues in using these methods. This paper presents a new way of proxying infrastructure: analysing the file sizes of map images on the Bing, Google...
Contexts in source publication
Context 1
... that the basics of using map size as a proxy of infra- structure have been outlined, it is possible to present some renderings of the measure. Figure 3 shows the infrastructure levels of the world at three different zoom levels (9, 10 and 11) for Sina, Open and Google. As was mentioned earlier, the priority of Sina is to provide maps of China: this is evi- dent in Figure 3. ...
Context 2
... 3 shows the infrastructure levels of the world at three different zoom levels (9, 10 and 11) for Sina, Open and Google. As was mentioned earlier, the priority of Sina is to provide maps of China: this is evi- dent in Figure 3. For Open, Europe has by far the highest values, with Japan, South Korea and the east coast of the US also showing high numbers. ...
Citations
... In our case, we use the common definition of a map with a width and height of 256 · 2 z px for zoom levels z ∈ N 0 . This corresponds to zoom levels in common map applications from Google, Bing and OpenStreetMaps [13] as well as comparable research [11]. ...
The visualization of spatial data is becoming increasingly important in science, business and many other areas. There are two main reasons for this: First, the amount of spatial data is growing continuously, making it impossible for people to manually process the data in raw form. Secondly, users have very high demands on the interactive processing of big spatial data in visual form. For instance in geography, data often corresponds to a large number of point observations that should be displayed on a constrained screen with limited resolution. This causes two crucial problems: drawing a lot of points is expensive at runtime and leads to a loss of information due to an overloaded and occluded visualization. In this paper we present a new efficient visualization algorithm that avoids these problems by aggregating point data into a set of non-overlapping circles with the following properties: (i) they follow the distribution of the data, (ii) they represent the cardinality of the underlying point subset by the circle area, (iii) they reveal hot spots while simultaneously keeping outliers, and (iv) the number of circles is typically much smaller than the number of points. Based on a quadtree, our algorithm computes the circles in linear time with respect to the number of points. Experimental results confirm its excellent runtime and quality in comparison to competitors.
How are resources distributed when administrative units merge? We take advantage of recent, large-scale municipal mergers in Japan to systematically study the impact of municipal mergers within merged municipalities and, in particular, what politicians do when their districts and constituencies suddenly change. We argue that when rural and sparsely populated municipalities merge with more urban and densely populated municipalities, residents of the former are likely to see a reduced share of public spending because they lost political leverage through the merger. Our empirical analyses detect changes in public spending before and after the municipal mergers with remote sensing data, which allows for flexible units of analysis and enables us to proxy for spending within merged municipalities. Overall, our results show that politicians tend to reduce benefits allocated to areas where there are a small number of voters, while increasing the allocation to more populous areas. The micro-foundation of our argument is also corroborated by survey data. The finding suggests that, all things being equal, the quantity rather than quality of electorates matters for politicians immediately after political units change.
