Land Use Land Cover (LULC) Change Projection in Kathmandu Valley using the CLUE-S Model

In the past few decades the urbanization pattern of the Kathmandu valley has rapidly increased and the process was sensed through the increase in the urban facilities, population growth, and changed LULC pattern. The historical LULC change was analyzed using the generated map and the future scenario was found through the CLUE-S LULC change model and processed in GIS environment. Five scenarios and nine driving forces were considered for the sensitivity and future analysis of the model. Based on the evaluation of the historical maps and the conservation matrix, the built-up area is found to be increased nearby by 5% and the agricultural area decreased by 6.5% during 2010 to 2018. It is concluded that the normal LULC conservation scenario provides more reliable information for the future projection. The simulation result highlights that nearly 4 km2 of fertile and open area will be converted to built-up areas due to the rapid urbanization per decade. This increase in urbanization process leads to more challenges in urban environment management in future.

The History of Urbanization in Java Island: Path to Contemporary Urbanization

The paper presents the historical analysis of the spatial transformation and emerging urban reality in Java Island. The historical approach used to understand the urbanization dynamics from the year 1200 until the present time. The study passes through important historical events: early Archipelago, precolonial, colonial state, late colonialization, Japanese occupation, Indonesia’s independence, Indonesia’s democratic experiment, guided democracy, new order, fall of the new order, and post-Suharto era, in which the history of urbanization pattern is also visualized on the map. From a long time frames the colonial state, new order, and the post-1997 financial crisis are the most important influence for Java’s urbanization. From the study, it reveals that the urbanization in Java Island has undergone a series of events that made the urban population contracted or expanded; hence its centers moved to different places. The study also underlines the influence of colonial and economic crises which made Java, and particularly Jakarta, to emerge as the epicenter of urbanization in Indonesia, as Jakarta’s urban development was further enhanced after Indonesia’s independence.

A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering

With the rapid industrialization and urbanization, pattern mining of soil contamination of heavy metals is attracting increasing attention to control soil contamination. However, the correlation over various heavy metals and the high-dimension representation of heavy metal data pose vast challenges on the accurate mining of patterns over heavy metals of soil contamination. To solve those challenges, a multiview Gaussian mixture model is proposed in this paper, to naturally capture complicated relationships over multiviews on the basis of deep fusion features of data. Specifically, a deep fusion feature architecture containing modality-specific and modality-common stacked autoencoders is designed to distill fusion representations from the information of all views. Then, the Gaussian mixture model is extended on the fusion representations to naturally recognize the accurate patterns of the intra- and inter-views. Finally, extensive experiments are conducted on the representative datasets to evaluate the performance of the multiview Gaussian mixture model. Results show the outperformance of the proposed methods.

Analyzing Temporal Changes in an Urbanized Area Using Densely Staked Image Classification and Multinomial Logistic Regression (MLR) Technique

Monitoring transformation of non-built-up area to urban spread via densely-stacked Land-Use-Land-Cover (LULC) classification offers a catalogue of spatio-temporal statistics to evaluate discrepancies instigated by transition factors. Impacts of major transition apparatuses in an area persuading the haphazard urbanization pattern are evaluated for Vellore acts a major contribution to Smart city project. Implications of causative factors: i) Population density; ii) proximity from rail-road-network; and iii) commercial areas are scrutinized with respect to urbanization upsurge. Multi-variate correlation is established using trend analysis and Multinomial Regression (MLR) technique for individual and homogeneous amalgamation of the aforementioned factors. Resulting equations obtained is formally used to detect closeness of urban extent from several landscapes. Research outcomes exhibited that the built-up straggling occurs from 30 to 232 m along the landscapes with a maximum of 336 m. Illustration of this study can also be assessed for various social and economic causative factors against urbanization for other smart cities.

Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India

The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry.

Spatial Analysis of Urbanization in Major Asian and African Cities

<p><strong>Abstract.</strong> The world statistics shows that 54% of the world population had been accumulated in urbanized areas by 2014. It is estimated that the global urban population will be increasing up to 66% of the total by 2050. According to the United Nations (UN) projection, the urbanization will be faster in Asian and African countries than the other continents. It is obvious this rapid increase will bring about serious socio-economic and environmental problems in the near future. Thus the geographical thinking of urbanization is becoming a vital topic to introduce proper urban planning for the future sustainability.</p><p>Many geographers have focused on the urbanization pattern and process of the developing countries during the last two decades. In this connection, the scarcity of the spatial data has been an obstacle to study quantitatively urbanization especially in Asian and African cities. Based on this background, since the 2000s, the Division of Spatial Information Sciences, University of Tsukuba, has conducted the research to establish the city-based spatial information platform to overcome this obstacle, i.e., the lack of the data. The main objective of this project is to capture the urbanization pattern and process over the time by using Remote Sensing (RS) and Geographical Information Systems (GIS) techniques. The data on land use and land cover, land surface temperature, energy consumption, and population density are now available with the same spatial resolution in terms of selected cities.</p><p>Our goal is to open the web-based GIS system to provide the geospatial database of Asian and African cities and scientifically discuss the urban sustainability by using scenario-based simulation. The projected future scenario will be useful for the urban policy by the “back-casting method.”<p>

Research on Population-Land-Industry Relationship Pattern in Underdeveloped Regions: Gansu Province of Western China as an Example

Urbanization is a three-dimensional process including population, spatial, and economic changes. The coordination among the three dimensions is the key to sustainable urban development. Here, a population-land-industry index system of urbanization is constructed, and the degree of coupling and mutual feedback among population urbanization, land urbanization, and industrial urbanization are analyzed. The urbanization patterns and their spatiotemporal variation are identified. The results show that: (1) Population and land urbanization proceeded slowly in Gansu Province and their trends were similar, whereas industry urbanization proceeded faster than the two. From a spatial perspective, population, land, and industrial urbanization levels (PUi, LUi, and IUi) decreased from southwest to northeast. The coupling degree of population, land, and industrial urbanization increased from 1998 to 2016 and showed significant spatial variation, decreasing from northwest to southeast. (2) Population, land, and industry all play a role in urbanization. PUi was significantly and positively correlated with LUi. However, there was no significant correlation between IUi and PUi and between IUi and LUi. The improvement of PUi, LUi, and IUi effectively promoted the coupling degree of population, land, and industrial urbanization. (3) Seven urbanization patterns were identified in Gansu Province and evaluation units with the same urbanization pattern tended to be spatially close to each other. IUi > PUi > LUi (IX), IUi > LUi > PUi (X) and IUi > PUi = LUi (XI) were the dominant urbanization patterns. There was crisscross distribution of various urbanization patterns and, thus, it was not easy to observe the agglomeration center of certain urbanization pattern. (4) The urbanization pattern of the same evaluation unit changed with time. This change was mainly reflected in the change of relationship between population and land urbanization. Urbanization pattern changed more significantly in 2008–2016 than in 1998–2008. The changes were dominant by IX→XI, X→XI, XI→IX, and XI→X.