Hybrid Machine Learning and Geographic Information Systems Approach — A Case for Grade Crossing Crash Data Analysis

2020 ◽  
Vol 12 (01) ◽  
pp. 2050003
Author(s):  
Ahmed Lasisi ◽  
Pengyu Li ◽  
Jian Chen
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Geographic Information Systems ◽  
The United States ◽  
Geographic Information ◽  
Supervised Machine Learning ◽  
Machine Learning Classification ◽  
Rail Network ◽  
Grade Crossing ◽  
Hybrid Machine

Highway-rail grade crossing (HRGC) accidents continue to be a major source of transportation casualties in the United States. This can be attributed to increased road and rail operations and/or lack of adequate safety programs based on comprehensive HRGC accidents analysis amidst other reasons. The focus of this study is to predict HRGC accidents in a given rail network based on a machine learning analysis of a similar network with cognate attributes. This study is an improvement on past studies that either attempt to predict accidents in a given HRGC or spatially analyze HRGC accidents for a particular rail line. In this study, a case for a hybrid machine learning and geographic information systems (GIS) approach is presented in a large rail network. The study involves collection and wrangling of relevant data from various sources; exploratory analysis, and supervised machine learning (classification and regression) of HRGC data from 2008 to 2017 in California. The models developed from this analysis were used to make binary predictions [98.9% accuracy & 0.9838 Receiver Operating Characteristic (ROC) score] and quantitative estimations of HRGC casualties in a similar network over the next 10 years. While results are spatially presented in GIS, this novel hybrid application of machine learning and GIS in HRGC accidents’ analysis will help stakeholders to pro-actively engage with casualties through addressing major accident causes as identified in this study. This paper is concluded with a Systems-Action-Management (SAM) approach based on text analysis of HRGC accident risk reports from Federal Railroad Administration.

A Multi-Feature Based Automatic Approach to Geospatial Record Linking

2019 ◽  
pp. 10-30
Author(s):  
Ying Zhang ◽  
Puhai Yang ◽  
Chaopeng Li ◽  
Gengrui Zhang ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Geographic Information ◽  
Geospatial Data ◽  
Data Sources ◽  
Supervised Machine Learning ◽  
Multiple Features ◽  
Feature Based ◽  
Record Similarity

This article describes how geographic information systems (GISs) can enable, enrich and enhance geospatial applications and services. Accurate calculation of the similarity among geospatial entities that belong to different data sources is of great importance for geospatial data linking. At present, most research works use the name or category of the entity to measure the similarity of geographic information. Although the geospatial relationship is significant for geographic similarity measure, it has been ignored by most of the previous works. This article introduces the geospatial relationship and topology, and proposes an approach to compute the geospatial record similarity based on multiple features including the geospatial relationships, category and name tags. In order to improve the flexibility and operability, supervised machine learning such as SVM is used for the task of classifying pairs of mapping records. The authors test their approach using three sources, namely, OpenStreetMap, Google and Wikimapia. The results showed that the proposed approach obtained high correlation with the human judgements.

scholarly journals Disparities in Air Pollution Exposure in the United States by Race-Ethnicity and Income, 1990 – 2010

2021 ◽  
Author(s):  
Jiawen Liu ◽  
Lara P. Clark ◽  
Matthew Bechle ◽  
Anjum Hajat ◽  
Sun-Young Kim ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Demographic Data ◽  
The United States ◽  
Geographic Information ◽  
Air Pollution Exposure ◽  
Race Ethnicity ◽  
Pollution Exposure

All data used are publicly available. Demographic data are available via IPUMS National Historic Geographic Information Systems [<a href="http://www.nhgis.org/" target="_blank">www.nhgis.org</a>]; air pollution estimates are available via the EPA CACES project [<a href="http://www.caces.us/" target="_blank">www.caces.us</a>]).

scholarly journals Disparities in Air Pollution Exposure in the United States by Race-Ethnicity and Income, 1990 – 2010

2021 ◽  
Author(s):  
Jiawen Liu ◽  
Lara P. Clark ◽  
Matthew Bechle ◽  
Anjum Hajat ◽  
Sun-Young Kim ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Demographic Data ◽  
The United States ◽  
Geographic Information ◽  
Air Pollution Exposure ◽  
Race Ethnicity ◽  
Pollution Exposure

All data used are publicly available. Demographic data are available via IPUMS National Historic Geographic Information Systems [<a href="http://www.nhgis.org/" target="_blank">www.nhgis.org</a>]; air pollution estimates are available via the EPA CACES project [<a href="http://www.caces.us/" target="_blank">www.caces.us</a>]).

scholarly journals Disparities in Air Pollution Exposure in the United States by Race-Ethnicity and Income, 1990 – 2010

2021 ◽  
Author(s):  
Jiawen Liu ◽  
Lara P. Clark ◽  
Matthew Bechle ◽  
Anjum Hajat ◽  
Sun-Young Kim ◽  
...  
Keyword(s):  
United States ◽  
Air Pollution ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Demographic Data ◽  
The United States ◽  
Geographic Information ◽  
Air Pollution Exposure ◽  
Race Ethnicity ◽  
Pollution Exposure

All data used are publicly available. Demographic data are available via IPUMS National Historic Geographic Information Systems [<a href="http://www.nhgis.org/" target="_blank">www.nhgis.org</a>]; air pollution estimates are available via the EPA CACES project [<a href="http://www.caces.us/" target="_blank">www.caces.us</a>]).

Digital Government and Geographic Information Systems

2011 ◽  
pp. 248-262 ◽  
Author(s):  
Jon Gant ◽  
Donald S. Ijams
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Local Governments ◽  
The United States ◽  
Geographic Information ◽  
Government Agencies ◽  
Digital Government ◽  
Gis Applications ◽  
Technological Advances ◽  
State And Local

The focus of this chapter is to examine how government agencies are deploying geographic information systems (GIS) to enhance the delivery of digital government. We will explain how critical technological advances are enabling government agencies to use GIS in web-based applications In addition, we will illustrate the approaches that state and local governments in the United States are taking to deploy GIS for e-government applications using examples from Indianapolis, Indiana, Tucson, Arizona, Washington D.C. and the State of Oregon’s Department of Environmental Protection. While these examples greatly improve service delivery performance and enhance public decision-making, we raise the issue that e-government GIS applications may be more broadly deployed in organizations that are better adept at dealing with the managerial and technical issues related to using GIS.

Challenges and Critical Issues for Temporal GIS Research and Technologies

2010 ◽  
pp. 144-153 ◽  
Author(s):  
May Yuan
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
The United States ◽  
Geographic Information ◽  
Gis Technology ◽  
Temporal Gis ◽  
Critical Issues ◽  
Spatiotemporal Representation ◽  
Technical Advances ◽  
Trade Magazines

Temporal Geographic Information Systems (GIS) technology has been a top research subject since late the 1980s. Langran’s Time in Geographic Information Systems (Langran, 1992) sets the first milestone in research that addresses the integration of temporal information and functions into GIS frameworks. Since then, numerous monographs, books, edited collections, and conference proceedings have been devoted to related subjects. There is no shortage of publications in academic journals or trade magazines on new approaches to temporal data handling in GIS, or on conceptual and technical advances in spatiotemporal representation, reasoning, database management, and modeling. However, there is not yet a full-scale, comprehensive temporal GIS available. Most temporal GIS technologies developed so far are either still in the research phase (e.g., TEMPEST developed by Peuquet and colleagues at Pennsylvania State University in the United States) or with an emphasis on mapping (e.g., STEMgis developed by Discovery Software in the United Kingdom).

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