Connecting the dots: information visualization and text analysis of the Searchlight Project newsletters

This report is the product of the Pardee Center’s work on the Searchlight:Visualization and Analysis of Trend Data project sponsored by the Rockefeller Foundation. Part of a larger effort to analyze and disseminate on-the-ground information about important societal trends as reported in a large number of regional newsletters developed in Asia, Africa and the Americas specifically for the Foundation, the Pardee Center developed sophisticated methods to systematically review, categorize, analyze, visualize, and draw conclusions from the information in the newsletters.The Rockefeller Foundatio

Sub-Saharan Africa at a crossroads: a quantitative analysis of regional development

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.Sub-Saharan Africa is at a crossroads of development. Despite a quarter of a century of economic reforms propagated by national policies and international financial agencies and institutions, sub-Saharan Africa is still lagging in development. In this paper, the authors adopt two techniques using both qualitative (e.g. governance) and quantitative factors (e.g., GDP) to examine regional patterns of development in sub-Saharan Africa. More specifically, they examine and analyze similarities and differences among the countries in this region using a multivariate statistical technique, Principal Component Analysis (PCA), and a unsupervised neural network called Kohonen’s Self-Organizing Map (SOM) to cluster levels of development. PCA serves as a tool for determining regional patterns while SOM is more useful for determining continental patterns in development. Both PCA and SOM results show a “developed” cluster in Southern Africa (South Africa, Namibia, Botswana, and Gabon). SOM exhibits a cluster of least developed countries in southern Western Africa and western Central Africa. The results demonstrate that the applied techniques are highly effective to compress multidimensional qualitative and quantitative data sets to extract relevant information about development from a policy perspective. Our analysis indicates the significance of governance variables in some clusters while a combination of variables explains other regional clusters. Zachary Tyler works for a consulting firm in Massachusetts that conducts program evaluations for energy efficiency programs, and he continues to work on statistical and geospatial analyses of human development issues. In 2010, he will receive a master’s degree in energy and environmental analysis from Boston University. Sucharita Gopal is Professor and Director of Graduate Studies in the Department of Geography and Environment and a member of the Cognitive & Neural Systems (CNS) Technology Lab at Boston University. She teaches and conducts research in geographical information systems (GIS), spatial analysis and modeling, and remote sensing for environmental and public health applications. Her recent research includes the development of a marin integrated decision analysis system (MIDAS) for Belize, Panama, and Massachusetts, and a post-disaster geospatial risk model for Haiti. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications, and symposia exploring African futures in various aspects related to development on continental and regional scales. For more information, visit www-staging.bu.edu/pardee/research/

A Neural Network Method for Efficient Vegetation Mapping

This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing

Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

A Neural Network Method for Land Use Change Classification, with Application to the Nile River Delta

Detecting and monitoring changes in conditions at the Earth's surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for analyzing long-term changes, provided that available methods can keep pace with the accelerating flow of information. This paper introduces an automated technique for change identification, based on the ARTMAP neural network. This system overcomes some of the limitations of traditional change detection methods, and also produces a measure of confidence in classification accuracy. Landsat thematic mapper (TM) imagery of the Nile River delta provides a testbed for land use change classification methods. This dataset consists of a sequence of ten images acquired between 1984 and 1993 at various times of year. Field observations and photo interpretations have identified 358 sites as belonging to eight classes, three of which represent changes in land use over the ten-year period. Aparticular challenge posed by this database is the unequal representation of various land use categories: three classes, urban, agriculture in delta, and other, comprise 95% of pixels in labeled sites. A two-step sampling method enables unbiased training of the neural network system across sites.National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-1-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-042

ARTMAP Neural Network Classification of Land Use Change

The ability to detect and monitor changes in land use is essential for assessment of the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could be used for detecting and monitoring long-term changes. Existing methods are insufficient for detecting subtle long-term changes from high-dimensional data. This project employs neural network architectures as alternatives to conventional systems for classifying changes in the status of agricultural lands from a sequence of satellite images. Landsat TM imagery of the Nile River delta provides a testbed for these land use change classification methods. A sequence often images was taken, at various times of year, from 1984 to 1993. Field data were collected during the summer of 1993 at88 sites in the Nile Delta and surrounding desert areas. Ground truth data for 231 additional sites were determined by expert site assessment at the Boston University Center for Remote Sensing. The field observations are grouped into classes including urban, reduced productivity agriculture, agriculture in delta, desert/coast reclamation, wetland reclamation, and agriculture in desert/coast. Reclamation classes represent land use changes. A particular challenge posed by this database is the unequal representation of various land use categories: urban and agriculture in delta pixels comprise the vast majority of the ground truth data available in the database. A new, two-step training data selection method was introduced to enable unbiased training of neural network systems on sites with unequal numbers of pixels. Data were successfully classified by using multi-date feature vectors containing data from all of the available satellite images as inputs to the neural network system.National Science Foundation Graduate Fellowship; National Science Foundation (SBR 95-13889); Office of Naval Research (N00014-95-I-409, N00014-95-0657); Air Force Office of Scientific Research (F49620-0l-1-0397)

Geographically weighted regression models in estimating median home prices in towns of Massachusetts based on an urban sustainability framework

Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR) and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC) values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants—such as unemployment and senior population—can help modify urban sustainable housing policies.This research is based upon work supported by the National Science Foundation under Grant No. CNH #1617053, CNH-S and Boston University Initiative on Cities, 2017. (1617053 - National Science Foundation; CNH-S; Boston University Initiative on Cities)https://doi.org/10.3390/su10041026Published versio

Mapping China’s foreign direct investment in the global energy sector

As China’s current account surplus has grown, and the nation has liberalized its capital account, Chinese overseas foreign direct investment has increased significantly. In 2016, Chinese overseas foreign investment flows were more than $1.3 trillion, and outward investment from Hong Kong was$1.5 trillion, combining to $2.8 trillion in total—up a factor of 10 since 2005 and second only second to the United States in total outbound foreign direct investment. This paper examines the extent to which Chinese overseas foreign investment is significantly different from the United States and other OECD foreign investors in general, and with a specific focus on the electricity energy sector. After creating a unique spatial database that allows us to analyze both greenfield and merger & acquisition flows into the electricity generation sector, we examine the extent to which Chinese foreign investment differs from the other major players in electricity by energy source, level of technology, and level of pollution. We examine the geographical concentration of China’s FDI investment in Asia and Europe. We present initial results of our origin and destination analysis. China’s FDI trends have tremendous policy implications, both in geopolitical terms and for international trade and investment promotion policies.Othe

Evaluation of neural network pattern classifiers for a remote sensing application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing setshttps://ssrn.com/abstract=1523788%20or%20http://dx.doi.org/10.2139/ssrn.1523788Published versio

Neural Network Models and Interregional Telephone Traffic. Comparative Performance Comparisons between Multilayer Feedforward Networks and the Conventional Spatial Interaction Model

(no abstract avilable)Series: Discussion Papers of the Institute for Economic Geography and GIScienc