ESRI ArcGIS Research Articles

Morphotype differentiation in the Great Barrier Reef Halimeda bioherm carbonate factory: Internal architecture and surface geomorphometrics

AbstractThe calcareous Halimeda bioherms of the northern Great Barrier Reef, Australia are the largest actively accumulating Halimeda deposits worldwide. They contribute a substantial component of the Great Barrier Reef neritic carbonate factory, as well as the geomorphological development of Australia's northeast continental shelf. Halimeda bioherm geomorphology is complex, expressing three distinct variations in morphotype patterns: annulate, reticulate and undulate. Similar regular and irregular geomorphological patterning often results from scale‐dependent biophysical feedback mechanisms. Therefore, a better understanding of morphotype differentiation can inform the biotic and abiotic drivers of spatial heterogeneity in the bioherm ecosystem. Here, 3D LiDAR bathymetry is integrated with 2D sub‐bottom profile datasets to investigate surface topography and internal sedimentary architecture of Halimeda bioherms through space and time. Using the ESRI ArcGIS 3D Analyst and Benthic Terrain Modeller extensions, the bioherm surface and subsurface geomorphometric characteristics were quantified for the annulate, reticulate and undulate morphotypes. Significant variation was found between the three bioherm morphotypes in their surface topography, internal structure, volume, slope gradients and terrain complexity. Therefore, their geomorphology is probably influenced by differing processes and biophysical feedback mechanisms. The complex surface topography does not appear to be inherited from the antecedent substrate, and preferred aspect orientations resulting from hydrodynamic forcing appear to be limited. It is suggested here that autogenic dynamics or biotic self‐organization similar to patterns and processes in other marine organo‐sedimentary systems modulates Halimeda bioherm geomorphology, and some hypotheses are offered towards future studies. Morphotype differentiation has implications for the development of the Halimeda bioherm carbonate factory, rates of sediment aggradation and progradation, and variable capacity to fill accommodation space. Self‐organization dynamics and morphology differentiation in Modern bioherm systems could potentially inform palaeo‐environmental interpretations of fossil bioherms and phylloid algal mounds on geological timescales.

A Geographic Information System-Based Indicator of Waste Risk to Investigate the Health Impact of Landfills and Uncontrolled Dumping Sites.

Uncontrolled and poor waste management practices are widespread. The global health impact of hazardous waste exposure is controversial, but the excess of some diseases appears to be consistent. The Geographic Information System (GIS, ESRI Inc., Rome, Italy) method used to estimate the waste risk exposure, in an area with many illegal waste dumps and burning sites, is described. A GIS geodatabase (ESRI ArcGIS format) of waste sites’ data was built. A municipal GIS-based indicator of waste risk (Municipal Risk Index: MRI) has been computed, based on type and quantity of waste, typology of waste disposal, known or potential environmental contamination by waste and population living near waste sites. 2767 waste sites were present in an area 426 km2 large. 38% of the population lived near one or more waste sites (100 m). Illegal/uncontrolled waste dumps, including waste burning areas, constituted about 90% of all sites. The 38 investigated municipalities were categorized into 4 classes of MRI. The GIS approach identified a widespread impact of waste sites and the municipalities likely to be most exposed. The highest score of the MRI included the municipalities with the most illegal hazardous waste dumps and burning sites. The GIS-geodatabase provided information to contrast and to prosecute illegal waste trafficking and mismanagements.

Using the ArcGIS Collector Mobile App for Settlement Survey Data Collection in Armenia

AbstractRecent years have seen the rapid adoption of digital site recording strategies following the proliferation of GPS-enabled mobile devices and data collection apps. Much of the emerging literature on digital—or paperless—archaeology, however, has focused on excavation contexts, with fewer discussions of mobile-GIS solutions on archaeological survey. This article discusses the design and implementation of a site survey workflow based on Esri's ArcGIS Collector mobile app in the context of Project ArAGATS's Kasakh Valley Archaeology Survey in northwestern Armenia. The Collector app provides a simple, map-centric user interface that allows surveyors with little-to-no GIS experience to record site locations, enter attribute data on customized digital forms, and attach photographs. With a network connection, the Collector app instantly uploads site information as GIS data to the project geodatabase and refreshes the data across surveyors’ mobile devices. Although the Collector app lacks certain GIS features and requires an institutional Esri license, we found that the native integration with our project GIS and broad access to visualization and recording tools in the app made in-field decision-making and interpretation more collaborative and inclusive across the survey team.

Free, open, quantitative and adaptable digital soil map data and database for Nigeria.

Nigeria's digital soil map (DSM) and database is the most comprehensive and detailed source of quantitative information on the country's soil distribution. Digital cartography and geographic information system (GIS) operations were methods used in producing the DSM and database. We obtained analogue soil data in 8 hard copy maps (each at a displayable scale of 1: 650,000) from the Federal Department of Agricultural Land Resources (FDALR), which archived the result of Nigeria's reconnaissance soil survey. The survey started in 1985 and by 1990; it has completed the compilation of hard copy maps that outline Nigeria's major soil distribution. Our experimental design begins with electronic scanning of these paper maps. We set the scanning system to 500 dpi, creating high-resolution raster images, which were imported into ESRI ArcGIS software, for orthorectification by geo-referencing to WGS 1984 geographic coordinate system. We applied a spatial processing tool on the orthorectified images and created a geometrically-seamless mosaicked raster image for the soil data of the whole Nigeria. Using GIS on-screen digitization – with optimal snapping tolerance – we created vector polygons (spatial data) of soil components (mapping units). Finally, we coded the metadata (attributes) of Nigeria's soil distribution into Microsoft EXCEL spreadsheet, which we linked to the soil spatial data. The combined spatial and attribute soil data forms the soil database for Nigeria and provides, on-demand, vital soil information, such as thematic maps of soil characteristics. The department of Geoinformatics and surveying University of Nigeria, Enugu Campus (UNEC) is the major repository of Nigeria's DSM and database.

A Multi-User Mobile GIS Solution for Documenting Large Surface Scatters: An Example from the Doring River, South Africa

ABSTRACT This article discusses the development and implementation of a mobile GIS catch-and-release system for documenting large surface artifact scatters along the Doring River in South Africa. An integrated, cloud-based mobile GIS solution was built using a suite of ESRI ArcGIS applications with an aim to maximize the speed and breadth of techno-typological data capture, while minimizing data collection errors and post-processing requirements. The system was successfully implemented during the 2019 field season of the Doring River Archaeological Project. With the ability for project-specific customization and interchangeable hardware components, the system transcends geographic region and temporal focus. Moreover, the system accommodates connectivity limitations commonly faced by archaeologists seeking distributed database solutions. Other challenges embraced in the design include rotating personnel throughout a field season, scalability without large financial investment, and the ability to accommodate data collection needs of other components of the larger multi-disciplinary research project.

Fading Boundaries: Religion and Spatial Mapping of Nigeria’s Jos North Urban Centre

ABSTRACT Grounded in critical spatial theories, drawing on ethnographic data and with the aid of ESRI ArcGIS (Geographical Information System cartographical software) to represent the city’s religious and spatial formations and transitioning, this article argues that the spatial essentialization of the city of Jos North and the sense of place its inhabitants construe seems to imply nontemporality and inherent fixity of place. It examines how Christians perceive the city of Jos North as a Christian and homogeneous spatial entity that needs preservation in its “original” forms against Hausa-Fulani Muslim migrations and spatial practices. Historically, the continuous migration of Hausa-Fulani Muslims to the city since 1902 has left an indelible spatial imprint on the city’s cultural and geopolitical landscapes. The efforts to halt such spatial transitioning now range from difficulty in securing land by Muslims for residential, economic, or religious purposes to space for burial rites. This research reveals an ongoing effort by Hausa-Fulani Muslims to gain and maintain spatial visibility while Christians strive to reverse such visibility or eclipse it with an aggressive (re)assertion of Christian spatial practices.

Assessment of the Level of Compliance of Petroleum Filling Stations to Development Control Standards on Land Space/Size and Setbacks in Anambra State.

Sequel to the loss of lives and properties with other attendant negative socio-economic effects of non-compliance to development control standards, this study was conceived with the aim of assessing the level of compliance of petroleum filling stations to development control standards on land space/size and setbacks in Anambra State. The study adopted the survey research design and field measurement. There were actual field measurements of the filling stations under study as well as the use of imageries of the study areas to identify these filling stations and measure their level of compliance to set standards for distances and location. 270 filling stations which is about 31% of the total number of filling stations were sampled. Arc GIS 10.1 software was utilized to measure the distances between the sited Petroleum Filling Stations and the roads with the help of a computer system. The imagery used was Quickbird (0.5m resolution) and Ikonos (1m resolution). The age ranges between 2015 and 2018. The Software used was ESRI ArcGIS 10.3, GIS Software. It has a measuring tool which was used to measure the distance. The Imagery were added using the ‘Add’ tool. It was then displayed (re-projected) on Projected Coordinate System (UTM, WGS 1984, Zone 32) for accuracy. It was found that the overall level of compliance to land space/size and setback standards is 44.1 percent across the filling stations in Anambra State. That is to say that only 44.1 percent of the PFSs in Anambra State complied with the land space/size and setback standards of building filling stations. Also that there is significant difference between the distances of the filling stations sampled from the road and the set standards for setback. The study thus recommends that: the law should empower the planning officer(s) to prosecute all cases of illegal developments and plans by non-planners without the planners approval should be subjected to prosecution as well. There is great need for public participation as the owners of these lands can join hands to say that a wrongly sited structure cannot be. Campaign should be mounted to conscientize the filling station owners and other developers of the dangers of non-compliance to set standards. Open punishment should be given to all defaulters to the set standards and corrupt officials of the enforcement agencies/bodies for others to learn their lessons. Finally, there should be proper planning to accommodate future expansion of roads. KEYWORDS: Development Control Standards, Compliance, Land Space/Size, Setbacks, Petroleum Filling Stations. DOI: 10.7176/CER/12-2-10 Publication date: February 29 th 2020

The effects of DEM interpolation on quantifying soil surface roughness using terrestrial LiDAR

Soil surface roughness (SSR) is often calculated based on the Digital Elevation Models (DEMs) obtained by interpolating points from terrestrial LiDAR measurements. This study aimed to investigate the effects of DEM interpolation and interpolation methods on quantification of SSR. A series of rainfall at the intensity of 59 and 178 mm hr−1 were simulated on a 2 by 6.1 m stony soil plot under 12 % and 20 % slope treatments. LiDAR measurements were conducted after each rainfall simulation at six positions around the soil plot. DEMs gridded at 5 and 10 mm were generated from LiDAR points using Inverse Distance Weighting, Natural Neighbors and Universal Kriging methods implemented in ESRI ArcGIS 10.5. Random roughness index (RR) was calculated based on the LiDAR-interpolated DEMs and LiDAR points directly and then compared. Results showed: 1) average values of RR from the DEMs were under-predicted compared to those directly calculated from LiDAR points by 7%–20%, indicating smoothing of the modeled surface during the interpolation process; 2) RR from LiDAR points indicated that soil surface became rougher as rainfall was progressed; 3) DEM errors increased as the surfaces evolved to rougher states and when 10 mm resolution was used; 4) the temporal SSR variations using the DEMs were -0.27 and -0.19 mm at 20 % slope and 0.30 and 0.36 mm at 12 % slope for the resolution 10 and 5 mm, respectively, while they were 0.22 mm at 20 % slope and 0.65 mm at 12 % slope when LiDAR points were directly used, the differences are due to the resulting increased DEM errors as the surface became rougher that masked the true changes in soil surface roughness. This study shows that at plot scale, DEMs generated through interpolations from LiDAR points underestimate soil surface roughness and are ineffective at tracking changes in soil surface roughness over time, and that LiDAR point data must be used instead at present scale.

Районирование закарстованных территорий Калужской области с применением интегрального подхода с целью выявления благоприятных площадей для строительства

The article considers 17 factors of natural structure of the territory of the Kaluga region divided into 4 groups: geological, hydrogeological, structural-tectonic and geomorphological. The relationship between each factor and the development of surface karst forms has been analyzed. Laws of distribution of karst forms and factors boundary values corresponding to intervals with different degree of danger are defined. The final assessment of the karst hazard was carried out by means of ESRI ArcGIS 10 using an integrated approach that takes into account the role of each factor in the development of the karst process as much as possible.

Картографирование растительных сообществ подзоны темнохвойных лесов юга Сахалина на основе космических съёмок

Research was carried out improve efficiency of thematic mapping based on the recognition of plant communities in the subzone of dark coniferous forests for South of Sakhalin on multi-time satellite images of average resolution Landsat 8. We used reference samples of sites where geobotanical studies were conducted, for improve the quality of recognition during automated decryption. Experiments were conducted decode vegetation on singlechannel, synthesized multi-zone images obtained in different seasons of year. Spectral characteristics allow us identify plant communities in images based on morphological and physiological properties of various plants, which were quantified by reflection of vegetation in the spring image, and an integral indicator of photosynthetic activity of vegetation, which was evaluated by NDVI index calculated from spring and autumn images. Conceptual and methodological aspects of direct expert interpretation of vegetation from Landsat images by classification methods using ESRI ArcGIS raster algebra tools are considered. On example of study of vegetation communities of subzone of dark-coniferous forests of the South of Sakhalin with sufficient level of reliability, dark-coniferous forests, stone birch forest, cedar elfin formation, valley forests, thickets of Kuril bamboo, as well as residential zones, agricultural lands, areas devoid of vegetation as result of gravitational slope processes, wetlands, windfalls and man-made wasteland were identified. Decoding of vegetation cover from Landsat images showed that use of seasonal time series can significantly increase the reliability of the interpretation of most species of plant communities for the South of island. The research area is characterized by significant difference in altitude from 0 to 1100 m, as a result presence of high-altitude zone in the vegetation cover, which must be taken into account when decoding. Mapping is completed by performing automatic vectorization of raster layers and further generalization of vector polygons in accordance with selected map scale.

Геоинформационный мониторинг демографических процессов в регионах юга Европейской России

The paper presents the results of the demographic processes analyses study in the South of European part of Russia in comparison with national outcomes. The research is based on a GIS monitoring concept and reveals the main characteristics of population size dynamic pattern with the influence of prevailing reproduction processes. The main sources of data are the office for national statistics and various regional statistics sources. ESRI ArcGIS Spatial Analyst software platform is used as a primary analytical framework. The study case covers the time frame from 1959 to 2018. Developing map models of the population’s natural reproduction brings out the depopulation speed and direction as a primary trend in Russia’s demographic development. At the beginning of the XXI century’s third decade, there is a significant reduction in the number of regions with positive natural population growth. The regions with decreasing in population have prevailed. The positive natural population growth preserves mainly in national territorial subdivisions with an unfinished demographic transition. The demographic picture in the south of European part of Russia looks relatively propitious. Negative demographic trends arise with a delay and have not reached the critical levels yet. The obvious polarization of demographic space in the south of Russia has educed — there are two areas with unequal trends of population growth: South-East area with a sustainable increase in the population and a migration outflow, the North-West area with a natural decline in the population and an erratic positive migration balance. Though the area with a sustainable increase in the population is reducing, showing positive trends in Krasnodar Krai and Republic of Adygea. The ethnic republic’s migration outflow is stable.

Variations in tick-borne disease incidence rate by rural-urban county classification

Background: Surveillance data of tick-borne disease (TBD) incidence in the United States are compiled at the county level, yet few studies have classified TBD risk using established county classification systems. Objective: Determine if significant differences in TBD incidence rates exist between Indiana counties based on population size classification (i.e. urban, rural, and rural-mixed). Methods: County TBD data for the period 2009 to 2016, were obtained from the Epidemiology Resource Center at the Indiana State Department of Health. Using the 2010 decennial population census, we normalized TBD counts to derive incidence rates per 1,000 population. We classified Indiana counties as either rural, rural-mixed, or urban based on population size. We used Kruskal-Wallis nonparametric test to determine if median TBD incidence rates differed between urban, rural, and rural-mixed urban counties. We used choropleth maps in ESRI ArcGIS to display TBD incidence rate by county classification. Results: Kolmogorov-Smirnov pairwise comparisons test results, revealed no evidence of a difference in TBD incidence rates between rural, rural-mixed, and urban counties (p≥ 0.1208 ± 0.0065). Furthermore, Kruskal-Wallis test showed no evidence of a difference in the median TBD incidence rates by county classification (p = 0.9754). Higher TBD incidence rate counties occur in the western region, while lower rate counties occur in the eastern region. Although no differences exist in incidence rates by county classification, the two highest incidence rates were recorded in rural counties. Conclusion: A classification of Indiana counties based on population size is inadequate in identifying counties with a greater or lesser risk of TBD incidence. For a better understanding of county population-level TBD risk, future studies should aim at obtaining and exploring TBD incidence data at more granular, sub-county population levels such as zip codes, census- blocks and tracts.

Spatial typification of river basin geosystems in the Kama river catchment area in relation to the regional tectonic structures

This study was aimed to reveal relationships between the formation of the structures of river systems and to discover the tectonic structural features of the territory. We analyzed the morphometric parameters of river catchment areas of various orders, which comprise the Kama river basin. A digital terrain model in ESRI ArcGis 10.4 geoinformation system was used. Within the identified river basin geosystems, quantitative morphometric characteristics of the terrain were calculated for comparative analysis. Multidimensional statistical analysis methods were used to group the basins with respect to their sets of geometric and morphometric characteristics. A spatial typification technique was developed to distinguish the basin geosystems by their morphometric indicators. This study pioneered in using the results of cluster analysis for identification of seven groups of basins, which reflect the spatial heterogeneity of the ancient tectonic elements and the geomorphological conditions within the entire territory of the Kama river basin. It is established that the types of the river basin geosystems with characteristic morphometric features are related to various ancient tectonic structures, which suggests a genetic relationship of endogenous processes of river channel fracturing and the diversity of geometric parameters of different river basins.

Remote monitoring of a deep‐sea marine protected area: The Endeavour Hydrothermal Vents

Abstract Deep‐sea marine protected areas (MPAs) present particular challenges for management. Their remote location means there is limited knowledge of species and habitat distribution, and rates and scales of change. Yet, evaluating the attainment of conservation objectives and managing the impact of human activities both require a quantitative understanding of natural variability in species composition/abundance and habitat conditions. Ocean Networks Canada (ONC) and Fisheries and Oceans Canada are collaborating in the development of remote monitoring tools for the Endeavour Hydrothermal Vents MPA in the north‐east Pacific. This 98.5 km2 MPA, located 250 km offshore Vancouver Island, encompasses five major fields of hydrothermal vents, at depths of 2200–2400 m. A real‐time cabled observatory was installed at the Endeavour site in 2010. Scientific research for the conservation, protection and understanding of the area is permitted within the MPA and is the primary activity impacting the area. Research activities require the use of submersibles for sampling, surveying and observatory infrastructure maintenance. Data and imagery from remotely operated vehicle dives and fixed subsea observatory sensors are archived in real time using ONC's Oceans 2.0 software system, enabling evaluation of the spatial footprint of research activity in the MPA and the baseline level of natural ecosystem change. Recent examples of database queries that support MPA management include: (1) using ESRI ArcGIS spatial analysis tools to create kernel density ‘heat maps’ to quantify the intensity of sampling and survey activity within the MPA; and (2) quantifying high‐frequency variability in vent fauna and habitat using sensor and fixed camera data. Collaboration between researchers and MPA managers can help mitigate the logistical challenges of monitoring remote MPAs. Recognition at the policy level of the importance of such partnerships could facilitate the extension of scientific missions to support more formal monitoring programmes.

Spatial analysis of road traffic accident hotspots: evaluation and validation of recent approaches using road safety audit

Reducing road traffic accidents (RTA) and their socioeconomic costs is an increasingly important priority in many countries. In recent years, many authors have proposed various approaches to analyse historical RTA data using Geographic Information System (GIS) tools, so that locations with high instances or risk of RTA – i.e., RTA hotspots or blackspots – could be identified and ranked. This could then enable limited road engineering resources to be prioritised for remediating more critical RTA hotspots and therefore reducing RTA rates. This paper presents the development of a new validation method to evaluate the application of four different hotspot analysis methods in ESRI ArcGIS 10.2 to identify and rank RTA hotspots using historical RTA data on a section of a road in Brunei Darussalam as a case study. A road safety audit was undertaken for six locations along the study road section to identify the risk levels at these locations. These risk levels were then compared with risk levels at the same six locations computed by four hotspot analysis methods: Network KDE by SANET, KDE+, Getis-Ord Gi*, and a recently proposed risk-based method – STAA – that accounts for RTA frequency, severity and socioeconomic costs. It was found that SANET-KDE overestimated the risk levels of road sections that are free of RTA. The results from KDE + showed the most deviations compared to the identified risk levels by the road safety audit. Getis-Ord Gi* was not able to identify RTA hotspots along the study road section of Jalan Tutong. However, the STAA method provided the most consistent results in comparison with the identified risk levels by the road safety audit. STAA, by having a better validity compared to other methods, provides a promising way to prioritise RTA hotspots for remediation, and thus reducing the associated socioeconomic costs. In addition, these findings demonstrated that the novel method of validation via risk levels determined by road safety audit was able to provide an independent measure of the validity of the hotspot analysis (HSA) methods.

SMODERP2D SOIL EROSION MODEL ENTERING AN OPEN SOURCE ERA WITH GPU-BASED PARALLELIZATION

Abstract. SMODERP2D is a runoff-soil erosion physically-based distributed episodic model used for calculation and prediction processes at agricultural areas and small watersheds. The core of the model is a raster based cell-by-cell mass balance calculation which includes the key hydrological processes, such as effective precipitation, surface runoff and stream network routing. Effective precipitation, the forcing of the runoff and erosion processes, is reduced by surface retention and infiltration. Surface runoff consists of two components: slower sheet and concentrated rapid rill flow. Stream network routing is performed line-by-line in the user predefined polyline layer.SMODERP is a long-term project driven by the Department of Landscape Water Conservation at the Czech Technical University in Prague. At the beginning, SMODERP has been developed as a surface runoff simulated by profile model (1D). Later the model has been redesigned using a spatially distributed method. This version is named SMODERP2D. Ongoing development is focused on obtaining parameters of the hydrological models, incorporating new infiltration and flow routing routines, and conceptualization of a rill flow and rill development. The model belongs to a family of so-called GIS-based hydrological models utilizing capabilities of GIS software for geospatial data processing. Importantly, the SMODERP2D project is currently entering an open source world. Originally the model could be run only in proprietary Esri ArcGIS platform. A new version of the model presented by this manuscript adds support for two key open source GIS platforms, GRASS GIS and QGIS. A newly developed GRASS module and QGIS plugin significantly increases the accessibility of the SMODERP2D model for research purposes and also for engineering practice.Middle scale distributed hydrological models often encounter with high computation costs and long model runtime. Long runtime is caused by high-resolution input data which is easily available nowadays. The project also includes an experimental version of the SMODERP2D model enabling the parallelization of computations. This parallelization is done using TensorFlow, and its goal is to decrease the time needed for its run. It is supported by both CPU and GPU. Parallelization of computations is an important step towards providing SMODERP2D web processing services in order to allow quick and easy integration to highly specialized platforms such as Atlas Ltd.

GIS Maps as Powerful Curriculum Artefacts

Abstract. Maps have always been central to high quality geography education. Recent developments in web GIS have opened up new potential for teachers using GIS maps as powerful curriculum artefacts. Curriculum artefacts are resources that have signature meaning for teaching and learning. This paper argues that the use of GIS maps as curriculum artefacts can significantly enhance geography teaching and learning in schools. To illustrate this line of argument, a GIS curriculum artefact constructed in ESRI ArcGIS Online is critically evaluated using Maude’s typology of powerful geography knowledge as an analytical framework. The analysis identifies a number of educational benefits of using GIS maps as curriculum artefacts in school geography via a GeoCapabilities approach. The paper concludes with recommendations for the future geography curriculum development with GIS map artefacts in schools.

Scientific Data Management in the Age of Big Data: An Approach Supporting a Resilience Index Development Effort.

The increased availability of publicly available data is, in many ways, changing our approach to conducting research. Not only are cloud-based information resources providing supplementary data to bolster traditional scientific activities (e.g., field studies, laboratory experiments), they also serve as the foundation for secondary data research projects such as indicator development. Indicators and indices are a convenient way to synthesize disparate information to address complex scientific questions that are difficult to measure directly (e.g., resilience, sustainability, well-being). In the current literature, there is no shortage of indicator or index examples derived from secondary data with a growing number that are scientifically focused. However, little information is provided describing the management approaches and best practices used to govern the data underpinnings supporting these efforts. From acquisition to storage and maintenance, secondary data research products rely on the availability of relevant, high-quality data, repeatable data handling methods and a multi-faceted data flow process to promote and sustain research transparency and integrity. The U.S. Environmental Protection Agency recently published a report describing the development of a climate resilience screening index which used over one million data points to calculate the final index. The pool of data was derived exclusively from secondary sources such as the U.S. Census Bureau, Bureau of Labor Statistics, Postal Service, Housing and Urban Development, Forestry Services and others. Available data were presented in various forms including portable document format (PDF), delimited ASCII and proprietary format (e.g., Microsoft Excel, ESRI ArcGIS). The strategy employed for managing these data in an indicator research and development effort represented a blend of business practices, information science, and the scientific method. This paper describes the approach, highlighting key points unique for managing the data assets of a smaller scale research project in an era of "big data."

Mapping of Urban Features of Nnewi Metropolis Using High Resolution Satellite Image and Support Vector Machine Classifier

When maps and master plan of an area are available, they will definitely guide in the urban development, especially as a working document for enforcing planning laws by both government and the private urban developers. However, such basic geospatial information is reasonably lacking in this study area and sequel to this, the researcher aims at Mapping of Urban Features (Natural and Man-Made), which includes the Vegetation, wetlands/water bodies, buildings/pavement, open/bare surfaces and farm lands captured in GeoEye-1, High Resolution Satellite Image (HRSI) of 2016 using Support Vector Machine Classifier (SVMC) with a view of developing a reliable urban land use and land cover map of the area, which will serve as a base map for land-use planning and monitoring for a variety of end-users. The objectives include: to identify and extract features/regions of interest (ROIs) in a subset HRSI of the study area, to perform supervised classification using SVM in ENVI Software. The methodology used include Image acquisition, Image enhancement, Image Sub-setting, Image masking, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC, Post-processing Accuracy Assessment, and Preparation of maps. Environment for Visualizing Image (ENVI 5.1) incorporated with Interactive Data Language (IDL 8.3) software was used for image processing, masking, spatial data analysis and image classification. Meanwhile, Esri ArcGIS 10.2 was employed for database development and production of thematic maps. Microsoft Excel and word was used for statistical analysis and result presentation. The result of image classification using SVMC, Radial Basis Function (BRF) default kernel in ENVI 5.1 indicates that Nnewi-North L.G.A is having 13.52% of Built-up Areas, 24.23% of Vegetation, 22.05% of Water bodies, Farm lands is equal to 39.40% and open/bare surface is 0.81% and result of image classification was validated using Error Matrix and Kappa Coefficient which results revealed that (SVMC overall Accuracy =98.07% and Kappa Coefficient = 0.97. The result revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare for every Remote Sensing Analysis. The research recommends that it is imperative to check for ‘ROIs index separability’ before using it for classification, also there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of Urban Areas.. Keywords: Support Vector Machine Classifier, Extraction of Regions of Interests (ROIs), ROIs separability index analysis, High Resolution Satellite Image (HRSI), Urban Landuse and Landcover. DOI : 10.7176/JEES/9-6-11 Publication date :June 30 th 2019

Deeply integrating Linked Data with Geographic Information Systems

AbstractThe realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.