The boundaries of the cities are not immutable, they can be changed. With the development of the economies and societies, the population and pollution of cities are increasing. Some urban areas are expanding with more population or other dynamics of urbanization, while other urban areas are reducing with the changing of the dynamics. Therefore, detecting urban areas or delineating the boundaries of the cities is one of the most important steps for urban studies, which is closely related to human settlements and human activities. Remote sensing data (RS) is widely used to monitor and detect land use and land cover on the surface of the earth. But the extraction of urban areas from the ordinary RS data is not easy work. The Operational Linescan System (OLS) is the sensors of the Defense Meteorological Satellite Program (DMSP). The nighttime lights from the DMSP/OLS provide worldwide remotely sensed data to analyze long-term light emissions which are closely related to human activities. But the nighttime lights imagery data contains inherent errors. Therefore, the approaches to calibrate the data and extract the urban areas from the data are complicated. The long-term objective of this thesis is to delineate the boundaries of the natural cities of the continental United States of America (USA) from 1992 to 2010 of nightlight imagery data with all the different satellites. In this thesis, the coefficients for the intercalibration of the nightlight imagery data have been calculated based on the method developed by Elvidge, et al. (2009), but the coefficients are new and available. The approach used to determine the most appropriate threshold value is very important to eliminate the possible data error. The method to offset this possible error and delineate the boundaries of the cities from nightlight imagery data is the head/tail breaks classification, which is proposed by Jiang (2012b). The head/tail breaks classification is also useful for finding the ht-index of the extracted natural cities which is developed by Jiang and Yin (2013). The ht-index is an indicator of the underlying hierarchy of the data. The results of this study can be divided into two categories. In the first, the achieved coefficients for the intercalibration of nightlight images of the continental USA are shown in a table, and the achieved data of the urban areas are stored in a data archive. In the second, the different threshold values of the uncalibrated images and the individual threshold value of the calibrated images are shown in tables, and the results of the head/tail breaks classification and power law test are also drawn. The results show that the acquired natural cities obey the power law distribution. And the results also confirm that the head/tail breaks classification is available for finding a suitable threshold value for the nightlight imagery data. Key words: cities’ boundaries; DMSP/OLS; head/tail breaks classification; nighttime lights; power law; urban areas

Our direct environment affects our lives directly. Christopher Alexander saw that we are able to feel or see if an object or structure is natural through the characteristics of them. He also saw that we generally feel better near these living, natural structures as it more closely resembles ourselves. Our bodies and our surroundings are made up of far more smaller than large things. When structures follow this pattern they are considered to be more natural, and when they move away from this pattern they are considered to be less natural and thus often boring or ugly. This scaling law is used to analyse the complex networks within cities. By analysing underlying structures instead of direct geometry it becomes possible to identify how living they are.This study applies these theories to analyse urban morphology within different cities. By identifying living structure within cities comparisons can be made between different types of cities. Specifically artificial and historical cities are analysed as they are counterparts in livingness. Following the identification of the living structure within these different types of cities an assessment can be made on what kind of an effect this has on our wellbeing based on Alexander’s theory. To see how living structure evolves over time a second analysis is performed which compares a city with its own evolution through time.Firstly natural cities and natural streets are identified in a bottom up approach based on the underlying structures of OpenStreetMap road data. Thereafter historical cities are compared with artificial cities because historical cities generally have living structure while artificial cities lack this. Then the developments of a historic city are identified and compared temporally. This research finds that current usage of concrete, steel and glass combined with very fast development speeds is detrimental to living structure within cities currently. Newer city developments should be performed in symbiosis with older city structures and the structure of the development should inhibit scaling as well as the buildings themselves. It is not sufficient to look only at geometry when managing cities, the importance of the fractal geometry, which is initially invisible must not be underestimated.

The most widely used classification method for statistical mapping is Jenks’s natural breaks. However, it has been found that natural breaks is not good at classifying data which have scaling property. Scaling property is ubiquitous in many societal and natural phenomena. It can be explained as there are far more smaller things than larger ones. For example, there are far more shorter streets than longer ones, far more smaller street blocks than bigger ones, and far more smaller cities than larger ones. Head/tail breaks is a new classification scheme that is designed for values that exhibit scaling property. In Digital Elevation Models (DEMs), there are far more lower elevation points than higher elevation points. This study performs both head/tail breaks and natural breaks for values from five resolutions of DEMs. The aim of this study is to examine advantages and disadvantages of head/tail breaks classification scheme compared with natural breaks. One of the five resolutions of DEMs is given as an example to illustrate the principle behind the head/tail breaks in the case study.The results of head/tail breaks for five resolutions are slightly different from each other in number of classes or level of details. The similar results of comparisons support the previous finding that head/tail breaks is advantaged over natural breaks in reflecting the hierarchy of data. But the number of classes could be reduced for better statistical mapping. Otherwise the top values, which are very little, would be nearly invisible in the map.A main conclusion to be drawn from this study is that head/tail breaks classification scheme is advantaged over natural breaks in presenting hierarchy or scaling of elevation data, with the top classes gathered into one. Another conclusion is when the resolution gets higher; the scaling property gets more striking.

A map combines scientific facts with aesthetic perceptions. This study argues that scaling is universal in mapping reality and evoking a sense of beauty. Scaling laws are used to reveal the underlying structures and dynamics of spatial features. Complex systems, such as living cities involve various interacting entities at all scales. Each individual coherently interacts and overlaps with others to create an unbreakable entity. Scaling structures are also known as fractals. Fractal geometry is used to depict a complex system. Natural objects, such as trees, contain a similar geometry (branches) at all scales. This study attempts to effectively visualize the scaling pattern of geographic space. In this regard, the head/tail breaks classification is applied to visualize the scaling pattern of spatial features. A scaling pattern underlies a geographic space. Visualizing the scaling structure using the head/tail breaks classification can further evoke a sense of beauty. This kind of beauty is on the structural level and was identified by Christopher Alexander, who asserted that beauty is not a personal experience but objectively exists in any space. Alexander developed the theory of centers to broaden the concepts of life and beauty.  A structure with a scaling property (with recursive centers) has high quality of life, and a scaling pattern has positive effects on individual’s psychological and physical well-being. To verify the concept of objective beauty, human beings are used as measuring instruments to examine the assumptions. This study adopts the mirror-of-the-self test to examine human reactions to 23 pairs of images, including photographs of buildings and two types of map. The idea is that participants sense the quality of life by comparing a pair of objects and selecting the object that presents a better picture of themselves. Once individuals feel the self in a picture, they are able to detect real beauty. In this manner, individuals can detect real beauty and life that deeply connect to their inner hearts. The tests were conducted through personal interviews and Internet surveys with the public and with professionals, and 392 samples were collected. The study results show that more than 60% of the individuals selected images with a scaling pattern. These results are in accordance with Alexander’s assumption. In particular, more than 65% individuals selected maps that depict scaling forms. Moreover, this study conducted a training test with a particular group of individuals, after which more than 70% of individuals selected scaling maps. The results reveal that scaling laws are applicable for creating maps and evoking a sense of beauty.

A city consists of many elements such as humans, buildings, and roads. The complexity of cities is difficult to measure using Euclidean geometry. In this study, we use fractal geometry (scaling analysis) to measure the complexity of urban areas. We observe urban development from different perspectives using the bottom-up approach. In a bottom-up approach, we observe an urban region from a basic to higher level from our daily life perspective to an overall view. Furthermore, an urban environment is not constant, but it is complex; cities with greater complexity are more prosperous. There are many disciplines that analyze changes in the Earth’s surface, such as urban planning, detection of melting ice, and deforestation management. Moreover, these disciplines can take advantage of remote sensing for research. This study not only uses satellite imaging to analyze urban areas but also uses check-in and points of interest (POI) data. It uses straightforward means to observe an urban environment using the bottom-up approach and measure its complexity using fractal geometry.   Web 2.0, which has many volunteers who share their information on different platforms, was one of the most important tools in this study. We can easily obtain rough data from various platforms such as the Stanford Large Network Dataset Collection (SLNDC), the Earth Observation Group (EOG), and CloudMade. The check-in data in this thesis were downloaded from SLNDC, the POI data were obtained from CloudMade, and the nighttime lights imaging data were collected from EOG. In this study, we used these three types of data to derive natural cities representing city regions using a bottom-up approach. Natural cities were derived from open geographic data without human manipulation. After refining data, we used rough data to derive natural cities. This study used a triangulated irregular network to derive natural cities from check-in and POI data.   In this study, we focus on the four largest US natural cities regions: Chicago, New York, San Francisco, and Los Angeles. The result is that the New York City region is the most complex area in the United States. Box-counting fractal dimension, lacunarity, and ht-index (head/tail breaks index) can be used to explain this. Box-counting fractal dimension is used to represent the New York City region as the most prosperous of the four city regions. Lacunarity indicates the New York City region as the most compact area in the United States. Ht-index shows the New York City region having the highest hierarchy of the four city regions. This conforms to central place theory: higher-level cities have better service than lower-level cities. In addition, ht-index cannot represent hierarchy clearly when data distribution does not fit a long-tail distribution exactly. However, the ht-index is the only method that can analyze the complexity of natural cities without using images.

Map generalization is a traditional cartographical issue which should be particularly considered in today’sinformation age. The aim of this study is to find some characteristics about head/tail breaks which worksas generalization method compared with the well known Topfer’s method. A questionnaire survey wasconducted to let 30 users choose either of the series maps of both methods and the reason(s) for thatchoice. Also to test their understanding of the series maps histograms were added for them to match.Afterwards the sample results were analyzed using both univariate and bivariate analysis approaches. Itshows that the head/tail breaks method was selected by 58%, compared with 38.7% of Topfer’s method,because of its simplicity. By checking the correctness of histogram question it also shows that those whowell understood answers choose the head/tail breaks rather than the Topfer’s method. However in somecases, where the amount of geographical features is relatively small, Topfer’s method is more selectedbecause of its informative characteristic and similar structure to the original map. It was also found that inthe comparison the head/tail breaks is more advantageous in line feature type generalization than in arealfeature type. This is probably because Topfer’s method changes its minority selection rule to half selectionin line feature type, whereas the head/tail breaks keeps the scaling property. Any difference between thetwo tested scales, Finland level and Helsinki level, is not found in this comparison study. However, futurework should explore more regarding this and other issues.

Geographic space is not neutral or lifeless, but an intricate living structure composed of numerous small features and a few large ones across all scales. The living structure is crucial for comprehending how geographic space shapes human activities. With the emerging geospatial big data, researchers now have unprecedented opportunities to study the relationship between geographic space and human behaviour at a finer spatial resolution. This thesis leverages multisource geospatial big data, including Twitter check-in locations, street networks from OpenStreetMap, building footprints, and night-time light images, to explore the fundamental mechanisms of human activities that underlie geographic space. To overcome the limitations of conventional analytics in this era of big data, we propose the topological representation and living structure based on Christopher Alexander's conception of space. We utilize scaling and topological analyses to reveal the underlying living structure of geographic space with various big datasets. Our results demonstrate that tweet locations or human activities at different scales can be accurately predicted by the underlying living structure of street nodes. We also capture and characterize human activities using big data and find that building footprints and tweets show similar scaling patterns in terms of sizes of their spatial clusters. We also propose an improved spatial clustering method to increase the processing speed of geospatial big data. Finally, we adopt topological representation to identify urban centres by the fusion of multi-source geospatial big data. The living structure, together with its topological representation can help us better understand human activities patterns in the geographic space at both city and country levels.