World Population Prospects, the 2010 Revision: Estimation and projection methodology

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.

https://orbit.dtu.dk/files/117885360/PHOTO_D_15_00278R1B.pdf

Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

/pdf/crowdsourcing-citizen-science-or-volunteered-geographic-4qhdzgct9c.pdf

Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information

This paper introduces a new classification scheme - head/tail breaks - in order to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This heavy-tailed distribution is also called scaling, hierarchy or scaling hierarchy. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. We therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. We demonstrate the advantages of the head/tail breaks method over Jenks' natural breaks in capturing the underlying hierarchy of the data. Keywords: data classification, head/tail division rule, natural breaks, scaling, and hierarchy

Head/tail Breaks: A New Classification Scheme for Data with a Heavy-tailed Distribution

This paper introduces a new classification scheme - head/tail breaks - in order to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This heavy-tailed distribution is also called scaling, hierarchy or scaling hierarchy. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. We therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. We demonstrate the advantages of the head/tail breaks method over Jenks' natural breaks in capturing the underlying hierarchy of the data. 
Keywords: data classification, head/tail division rule, natural breaks, scaling, and hierarchy

Scaling of geographic space refers to the fact that for a large geographic area its small constituents or units are much more common than the large ones. This paper develops a novel perspective to ...

/pdf/scaling-of-geographic-space-from-the-perspective-of-city-and-573f32o7nj.pdf

Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information

Scaling of geographic space refers to the fact that for a large geographic area its small constituents or units are much more common than the large ones. This paper develops a novel perspective to the scaling of geographic space using large street networks involving both cities and countryside. Given a street network of an entire country, we decompose the street network into individual blocks, each of which forms a minimum ring or cycle such as city blocks and field blocks. The block sizes demonstrate the scaling property, i.e., far more small blocks than large ones. Interestingly, we find that the mean of all the block sizes can easily separate between small and large blocks- a high percentage (e.g., 90%) of smaller ones and a low percentage (e.g., 10%) of larger ones. Based on this regularity, termed as the head/tail division rule, we propose an approach to delineating city boundaries by grouping the smaller blocks. The extracted city sizes for the three largest European countries (France, Germany and UK) exhibit power law distributions. We further define the concept of border number as a topological distance of a block far from the outmost border to map the center(s) of the country and the city. We draw an analogy between a country and a city (or geographic space in general) with a complex organism like the human body or the human brain to further elaborate on the power of this block perspective in reflecting the structure or patterns of geographic space. 
Keywords: Power law distribution, scaling of geographic space, data-intensive geospatial computing, street networks

Scaling of Geographic Space from the Perspective of City and Field Blocks and Using Volunteered Geographic Information

To understand the spatial dependency of bioavailable micronutrients in paddy soils, 134 soil samples were taken from Pinghu County, Zhejiang Province, south-east China to characterize the spatial variabilities of bioavailable Cu, Zn, Fe, and Mn. The geostatistics and geographic information system (GIS) techniques were applied. Bioavailable Zn and Mn were fitted to an exponential model with a range of 4.4 km and 2.97 km, respectively. Bioavailable Fe was fitted to the linear model with the longest range of 23.48 km. Bioavailable Cu did not show any spatial correlation with the fitted pure nugget effect model. Bioavailable Zn and Mn had strong spatial dependence due to the effects of intrinsic factors such as parent material, relief and soil types. Bioavailable Fe had moderate spatial dependence over a long distance. The spatial distribution of the four bioavailable micronutrients were significantly correlated to the soil formation factors. Human activity, such as application of fertilizer and pesticide, emission of waste gas and industrial pollution also had significant effects on the spatial distributions of the bioavailable micronutrients.

Application of geostatistics and GIS technique to characterize spatial variabilities of bioavailable micronutrients in paddy soils

Map generalization is a process of producing maps at different levels of detail by retaining essential properties of the underlying geographic space. In this article, we explore how the map generalization process can be guided by the underlying scaling of geographic space. The scaling of geographic space refers to the fact that in a geographic space, small things are far more common than large ones. In the corresponding rank-size distribution, this scaling property is characterized by a heavy-tailed plot such as a power law, lognormal, or exponential function. In essence, any heavy-tailed distribution consists of the head of the distribution (with a low percentage of vital or large things) and the tail of the distribution (with a high percentage of trivial or small things). Importantly, the low and high percentages constitute an imbalanced contrast, e.g., 20 versus 80. We suggest that the purpose of map generalization is to retain the objects in the head and to eliminate or aggregate those in the tail. We...

Scaling of Geographic Space as a Universal Rule for Map Generalization

Based on the concepts of isovists and medial axes, we developed a set of algorithms that can automatically generate axial lines for representing individual linearly stretched parts of open space of an urban environment. Open space is the space between buildings where people can freely move around. The generation of the axial lines has been a key aspect of space syntax research, conventionally relying on hand-drawn axial lines of an urban environment, often called axial map, for urban morphological analysis. Although various attempts have been made towards an automatic solution, few of them can produce the axial map that consists of the least number of longest visibility lines, and none of them really works for different urban environments. Our algorithms provide a better solution than existing ones. Throughout this article, we have also argued and demonstrated that the axial lines constitute a true skeleton, superior to medial axes, in capturing what we perceive about the urban environment.

/pdf/automatic-generation-of-the-axial-lines-of-urban-2gbxe6aezx.pdf

Xintao Liu

Papers

Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information

Scaling of Geographic Space from the Perspective of City and Field Blocks and Using Volunteered Geographic Information

Application of geostatistics and GIS technique to characterize spatial variabilities of bioavailable micronutrients in paddy soils

Scaling of Geographic Space as a Universal Rule for Map Generalization

Automatic generation of the axial lines of urban environments to capture what we perceive