Non-spatial Data Research Articles

Автоматизация выделения границ медицинских участков на примере Санкт-Петербурга

The article describes an approach to the desktop geographic information system application to optimization and (partial) automation of the spatial and non-spatial medical data processing and analysis when estimating the grid of medical lots in an area of medical service, and when forming (considering current morbidity and demographic indicators) updated boundaries of medical lots. Software tools of the QGIS platform were used to process spatial data (QGIS itself and its extension modules, including the GeoMedic module designed with the participation of the authors for medical statistics data geocoding). All medical statistics data used in the study were previously depersonalized in accordance with the requirements of Russian laws. A methodology prototype is elaborated for preparation, analysis, and processing of the data on patients (potential patients) number in the medical service area, together with spatial data (geographical map) of this area. The data of the St. Petersburg city tuberculosis service were used for experimental purposes in the study. Specialists of the St. Petersburg Research Institute of Phthisiopulmonology are involved in the work. Developed methodological toolset prototype ensures partially automated processing of initial data with the formation of boundaries of the medical lots in the analyzed area of medical service. Formation of the boundaries is carried out taking into account the normative number of patients, which must be ensured for the formed lots. The study was performed on the city scale, on the example of St. Petersburg (Russia). Data analysis was performed for the Moskovsky, Petrogradsky, Primorsky and Nevsky Administrative Districts of St. Petersburg. A grid of medical lot boundaries was formed and mapped in the results of the performed analysis. The methodology prototype proposed in the result of the conducted study can be applied in the analysis of existing schemas of medical lots and their updating.

GEOGRAPHIC INFORMATION SYSTEM MAPPING SENIOR HIGH SCHOOL AND VOCATIONAL HIGH SCHOOL IN BALI ISLAND BASED INTERNET BY USING GOOGLE MY MAPS

This study aims to provide information to prospective students and the public about mapping the profiles of SMA (Senior High School) and SMK (Vocational High School) on the island of Bali online through google my map to assist in making decisions in choosing the school as desired because at the time of school selection based on the zoning system. The method used in this study is a field research method which consists of several stages in the flow of analysis, namely Literature Study, Spatial data collection, Internet-based digital map creation using Google My Maps, Integration of spatial data and non-spatial data and implementation. Integration of spatial data and non-spatial data for SMA and SMK in the form of a digital map based on a Geographic Information System using Google My Maps software and a comparison of the number of SMA and a comparison of SMK spread across several regencies /municipalities where almost every regency/municipality has a number of SMA and the number of Vocational High School balanced, based on the results of the analysis that the number of SMA and the number of SMK that are spread out are for SMA and SMK on the island of Bali, namely 179 and 178 with a comparison of about 50.1% for SMA and around 49.9% for SMK. This study focuses on the dissemination of information for high school and vocational schools that are integrated with school profile information packaged in the concept of a Geographic Information System using google my map which can be accessed by prospective new students to decide to choose a school based on the zoning system or distance from where they live.

An Interactive Decision Support System for Land Reuse Tasks.

Experts face the task of deciding where and how land reuse-transforming previously used areas into landscape and utility areas-can be performed. This decision is based on which area should be used, which restrictions exist, and which conditions have to be fulfilled for reusing this area. Information about the restrictions and the conditions is available as mostly textual, nonspatial data associated to areas overlapping the target areas. Due to the large amount of possible combinations of restrictions and conditions overlapping (partially) the target area, this decision process becomes quite tedious and cumbersome. Moreover, it proves to be useful to identify similar regions that have reached different stages of development within the dataset which in turn allows determining common tasks for these regions. We support the experts in accomplishing these tasks by providing aggregated representations as well as multiple coordinated views together with category filters and selection mechanisms implemented in an interactive decision support system. Textual information is linked to these visualizations enabling the experts to justify their decisions. Evaluating our approach using a standard SUS questionnaire suggests that especially the experts were very satisfied with the interactive decision support system.

Patterns and predictors of household fuel choices in Maswa District, Tanzania

This study assessed the spatial and non-spatial patterns of household fuel choice and the factors influencing choices using data from rural and urban areas of Maswa District, Tanzania. Household spatial information was collected using a handheld Garmin Trex Global Positioning System to enable modeling and visualization of energy consumption. The Hot Spot Analysis (Getis-Ord Gi*) method was used to map the clusters of different types of cooking fuels. The non-spatial data were fitted to a Multinomial Logit model to estimate the significance of the factors that influence the choice of cooking fuels. A transition from traditional to modern fuels as explained in the “energy ladder” model suggests that with increasing affluence, a progression is expected from traditional biomass fuels to more advanced and less polluting fuels. Contrary to this model, however, multiple-fuel use or fuel stacking was more prevalent than fuel switching. Fuel stacking constituted traditional (firewood, 63.9% of the households), transitional (charcoal, 66.6%), and modern (kerosene, liquefied petroleum gas, and electricity, 25.4%) fuels. Demographic and socio-economic factors were essential in household fuel choices. Overall, biofuels are the most common energy sources both in rural and urban areas, albeit at a varying degree of magnitude and significance. These results suggest the need to increase the production of biomass resources and end-use efficiency while promoting the use of clean fuels.

Isolated pressure zones based on GIS as a solution for water network problems

Abstract One of the most important accidents for water transmission lines occur by air entrance due to water hammer into the water pipeline at sub-atmospheric transient pressure. The air can be sucked into the water system causing repeated pipe breaks in sub-atmospheric transient pressure. This work aimed to improve the advanced techniques including remote sensing (RS), and the internet of things (IoT) to control the repeated pipe breaks. According to the geospatial information systems (GIS), the relationship classes between the spatial and non-spatial data were defined for the scaled model. By GIS the location of pipe breaks and sucking air into water pipeline was recognized during fluid instabilities. The location of pipe breaks was defined by the numerical modelling based on GIS. According to numerical analysis, the Courant number was equal to 0.977. In the field test, the surge velocity was 1084 m/s. The pressure wave was detected in 0.005 s by RS facilities equipped with water hammer sensors, advanced modems, and data loggers referring to the IoT technique. The result of this work led to a reduction of the non-revenue water (NRW) and the saving of drinking water.

Analisis Hasil Pertanian Dengan Menggunakan Sistem Informasi Geografis (Studi Kasus : Kota Salatiga)

As a country so rich in natural products, the agricultural sector is an activity of utilizing natural resources carried out by humans so that it can produce food, raw materials for agency and industrial businesses, energy and natural balance. Every year the limited agricultural area of the city of Salatiga can be affected by crop yields and agricultural production. Departing from this problem, it is necessary to analyze agricultural products in order to know the increase or decrease in agricultural yields in Salatiga City, the existence of a Geographic Information System (GIS) that can facilitate tools as a decision-making tool by the government or agency. In this research, the methods used are collecting spatial and non-spatial data, analyzing agricultural product data of Salatiga City and digitizing it into a digital map. The result of this research is a digital map that provides information on the mapping of agricultural products in Salatiga City for 4 years starting from 2016-2019.

Design and Implementation of a Web-GIS for the management of road infrastructure in Zimbabwe

Road asset mapping has the potential of reducing: costs in keeping all assets data, time-consuming activities like retrieving asset attribute from large files, risks associated with losing all the data by using Geographic Information Systems (GIS). Traditional road data has been stored in the form of hard copy maps showing the different road infrastructure. The World Wide Web (WWW), has revolutionized the provision, dissemination, and data access to people in different geographical locations. Web-GIS based applications have gained popularity because their low cost, ease of use and availability to a large population – that is anyone with a web-browser. Through browsers, web-GIS based applications can display a map with useful information. The design and development of an interactive web-GIS based digital road infrastructure management tool not only allows users to visualize the road infrastructure content but also help in decision making. It makes use of open source GIS tools, PostgreSQL and PostGIS (to manage spatial and non-spatial data), Geoserver (to connect the database to the client mapping application) and Apache Tomcat (to build and deploy the application). The maps are published through Geoserver with their associated information using JavaScript libraries (Open Layers and Geoext). Further spatial analysis (attribute queries) can be done online. Results show that a web-GIS was developed that manages road asset infrastructure like road signs, bridges, animal grids, rest areas. A user can query precise assets they want to visualize for instance damaged bridges. HoweGIS:here is still need to further improve the application for instance allowing user to put complaints about damaged road assets. Thus, the development of the application will help decision makers as well as other users to utilize the information for the benefit of the country.

Data Analysis of Building Microrenovation Based on City Information Modeling and Smart Sensor Networks

Building microrenovation refers to the building renewal method implemented through partial demolition, repair, preservation, function replacement, renovation, improvement, protection, decoration, and activation under the premise of maintaining the current construction pattern basically unchanged. City information modeling (CIM) technology provides a three-dimensional space floor with all elements for refined city governance and smart city construction; the data layers of smart sensor network (SSN) technology can aggregate multisource data by docking with facility layers and can classify integrated data resources. On the basis of summarizing and analyzing the previous research results, this study expounded the research status and significance of data analysis of building microrenovation, elaborated the development background, current status, and future outlooks of CIM and SSN, introduced the methods and principles of the aggregation and segmentation of information resources and the aggregation and interconnection of multisource data, performed the feature extraction and data storage of the original buildings, conducted the scheme design and information classification of microrenovation, implemented the data extraction and model construction of building microrenovation based on CIM and SSN, performed the data analysis of building appearance improvement, conducted the data analysis of building function replacement, implemented the database establishment and data analysis of building microrenovation based on CIM and SSN, and finally carried out a case application and its result analysis by taking a middle school two-story teaching building as an example. The study results show that the CIM technology can provide buildings’ actual data information such as geometry, physics, structure, and rules for building microrenovation; the SSN technology greatly reduces a lot of work caused by the rewiring of control system in the construction of building microrenovation. The combination of CIM and SSN can effectively connect various entity elements in building microrenovation to form a theme library through various data spatial relationships and spatial coding, realizing the connection of spatial data and nonspatial data in multiple dimensions. The data analysis of building microrenovation based on CIM and SSN includes multiple dimensions of achievements, processes, resources, characteristics, and applications, which is more in line with CIM integration applications and is easier for realistic information exchange and sharing applications.

Non-Spatial Data and Modelling Multiscale Systems in Archaeology

Abstract This article discusses the multiscale nature of modelling in archaeology and its relationship with higher-level spatial analysis. The application and purpose of modelling in archaeology is as varied as the multidisciplinary field itself. With the increasing integration of geographical information systems (GIS) and other digital methods into the archaeological workflow, both new opportunities and potential pitfalls present themselves. The struggle of balancing informal inferences of human behaviour in a formal system, such as GIS, has been the subject of much discussion, as well as the questioning of whether some modelling tasks would be better suited for implementation outside the GIS environment. Higher-level spatial analysis is dependent on a number of lower-level models, each building on the other, inheriting both information and uncertainties. These nuances can be difficult to demonstrate clearly once they have been incorporated into another model, potentially obscured further when restricted by the “geographical space” that is central to GIS. Rather than forcing informal models into a formal environment, an alternative would be to opt instead for the visualization of these within the more flexible “variable space,” where the data are front and centre, and spatial and temporal concepts can function as a means of explaining patterns in the model. This article discusses aspects of the challenges and opportunities involved in these types of analysis and provides examples of alternate approaches that could be considered non-spatial.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Spatial-based multicriteria decision-making model for coconut sugar agro-industry location selection: A case study at Indragiri Hilir District, Riau Province, Indonesia

Coconut has become a major agricultural commodity because of its significant economic multiplier effects on people in the Indragiri Hilir District, Riau Province, Indonesia. One of the superior coconut products is coconut sugar, which may serve as a healthier substitute for conventional sugar because it has a lower glycemic index. Developing coconut sugar in the agro-industry certainly requires spatial mapping that best fits the criteria of sustainable agro-industry. Therefore, a spatial-based multicriteria decision-making model is required to select the best locations for product development. The integration between geographic information systems (GIS) and the analytical hierarchy process (AHP) was applied to acquire the target. It is relatively easy to handle various criteria with spatial and non-spatial data and balance qualitative and quantitative data. The results showed that the criteria contributing to selecting the location for the coconut sugar agro-industry included economic, social, environmental, technology, and infrastructure. Tembilahan Hulu and Kuala Indragiri became the most potential locations relevant to the analysis results. This work provides significant input and data for local governments in constructing policies related to the development of the coconut sugar agro-industry.

Spatial and Non-Spatial Panel Data Estimators: Simulation Study and Application to Personal Income in U.S. States

The spatial analysis aims to understand and explore the nature of entanglements and interactions between spatial units’ locations. The analysis of models involving spatial dependence has received great attention in recent decades. Because ignoring the presence of spatial dependence in the data is very likely to lead to biased or inefficient estimates if we use traditional estimation methods. Therefore, this paper is an attempt to assess the risks involved in ignoring the spatial dependence that characterizes the panel data by using a Monte Carlo simulation (MCS) study for two of the most common spatial panel data (SPD) models; Spatial lag model (SLM) and spatial error model (SEM), by comparing the performance of two estimators; i.e., spatial maximum likelihood estimator (MLE) and non-spatial ordinary least squares (OLS) within-group estimator, across two levels of analysis; Parameter-level in terms of bias and root mean square error (RMSE), and model-level in terms of goodness of fit criteria under different scenarios of spatial units N, time-periods T, and spatial dependence parameters, by using two different structures of spatial weights matrix; inverse distance, and inverse exponential distance. The results show that the non-spatial bias and RMSE of β ̂ are functions of the degree of spatial dependence in the data for both models, i.e., SLM and SEM. If the spatial dependence is small, then the choice of the non-spatial estimator may not lead to serious consequences in terms of bias and RMSE of β ̂. On the contrary, the choice of the non-spatial estimator always leads to has disastrous consequences if the spatial dependence is large. On the other hand, we provide a general framework that shows how to define the appropriate model from among several candidate models through application to a dataset of per capita personal income (PCPI) in U.S. states during the period from 2009 to 2019, concerning three main aspects: educational attainment, economy size, and labour force type. The results confirm that PCPI is spatially dependent lagged correlated.

Application of 3D Virtual Reality Sensor in Tourist Scenic Navigation System

In order to solve the application of 3D virtual reality technology in the practical development of scenic spot navigation system, this paper proposes a scenic spot navigation system based on multimedia technology and component geographic information system (ComGIS). The system takes Visual Basic 6.0, MapObjectst2.4, and GeoLOD 4.0 as the development platform and develops the tourism navigation system based on component GIS and multimedia technology, which manages nonspatial data with access 2003. The results are as follows: the path length designed by the system in this paper and the three compared navigation apps is 1.56 km, respectively. Although the path designed in this paper is not the shortest, the number of scenic spots passed by this path is up to 6; The accuracy of the navigation system designed in this paper is 97.2%, about 5% higher than that of similar navigation APPs; 55.2% of users felt that the navigation system designed in this paper was excellent. The results show that the system can basically meet the needs of tourists, hoping to promote the information construction of tourism.

A Research & Development Model of Resource Mapping for Livelihood Planning

The envisioned R&D Model of Resource Mapping for Livelihood Planning is based on the experience gained in a survey-based research work on resource mapping for livelihood planning, conducted in the backward district of Purulia (West Bengal, India) in 2018-20. The research work identified some parameters and attributes associated in the villagelevel resource mapping work, and highlighted the scope of the application of information technology in the work process through analysis of geo-referenced data – to render an extra edge in real time analysis. The research work produced a combined database having 67 baselevel attributes, containing spatial and non-spatial data of the area of study comprising of 606 mouzas. It was felt that such database, when powered with GIS-enabled search engine and WebGIS infrastructure, may generate huge volume of strategic just-in-time information, which when linked with e-governance mechanism of the government, may help promote livelihood at the village level through optimal utilization of resources. Technological tools like Geographical Information System (GIS) and Remote Sensing Data Analysis help us to develop spatially connected databases. Such database when combined with strategic non-spatial data-sets may produce a combined database, which once made up to village level, can provide a tremendous boost in the planning and monitoring of activities of social projects of the governments and NGOs, as well as may provide efficient and quality service to the entrepreneurs by way of providing just-in-time information. In livelihood resource mapping work, criteria analysis of resource mapping data can help in extracting relevant information in super-imposed form from multi-layered datasets, through multiple object-based inter-related queries. Four case studies are illustrated in this paper that offer examples of the ways to generate criteria maps for strategic information (along with its dataset) from combined database of GIS, in response to multi-level queries. Such criteria maps provide information on the existing and other socio-economic conditions of the region. Thousands of such multi-level queries may generate numerous Resource Planning Maps and Participatory Planning Maps, useful for livelihood planning and its implementation. Livelihood planning and its implementation at the grass-root level need collaborative efforts at various levels – both the government and non-government institutions may join hands in the fields of survey works, compilation of combined databases, analysis of the data, generating strategic information for livelihood planning, and most important, in the task of livelihood cluster development works at the grass-root level. A participative planning roadmap is given in the concluding note.

Abstract 3687: Implementing geospatial technology to understand the prostate cancer health burdens and socioeconomic status in Alabama

Abstract Socioeconomic status and health disparities are a significant liability for the prevalence of many diseases, including cancer. Among many contributing factors, lack of socioeconomic advancement and inequality in resource distribution is closely associated with prostate cancer occurrence. The State of Alabama has one of the highest rates of prostate cancer in the nation and the third cancer epidemic at the state level. Due to widespread disparities in infrastructure development, political willpower, equal healthcare, and education opportunity, Alabama's Black Belt region has been further divided by socioeconomic statuses among the majority (Whites) and minority (African American) populations. The purpose of this research is to recognize Alabama's health burdens and socioeconomic status using geo-spatial technology (GIS) to examine prostate cancer health disparity. GIS has become a valuable decision-making tool in understanding the public health care sectors, including prostate cancer prevalence. GIS supports assembling spatial and non-spatial data, displays the spatial distribution of health disparities, and underlines further interconnectivity of policy-related questions. This research incorporates county-level prostate cancer data using GIS and outlining the correlation between prostate cancer prevalence and socioeconomic factors to understand Alabama's cancer health disparity and equity. A combination of socioeconomic factors and state and federal-level cancer epidemic data sources underline Alabama's black belt region (predominantly African American) prostate cancer prevalence is higher than the non-black belt. The GIS analysis demonstrated that effective cancer prevention programs are needed to eliminate health disparities and enhance health equity among underserved populations of Alabama. Citation Format: Ram Alagan, Seela Aladuwaka, Rajesh Singh, Upender Manne, Manoj K. Mishra. Implementing geospatial technology to understand the prostate cancer health burdens and socioeconomic status in Alabama [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3687.

Efficient spatial data partitioning for distributed kNN joins

Parallel processing of large spatial datasets over distributed systems has become a core part of modern data analytic systems like Apache Hadoop and Apache Spark. The general-purpose design of these systems does not natively account for the data’s spatial attributes and results in poor scalability, accuracy, or prolonged runtimes. Spatial extensions remedy the problem and introduce spatial data recognition and operations. At the core of a spatial extension, a locality-preserving spatial partitioner determines how to spatially group the dataset’s objects into smaller chunks using the distributed system’s available resources. Existing spatial extensions rely on data sampling and often mismanage non-spatial data by either overlooking their memory requirements or excluding them entirely. This work discusses the various challenges that face spatial data partitioning and proposes a novel spatial partitioner for effectively processing spatial queries over large spatial datasets. For evaluation, the proposed partitioner is integrated with the well-known k-Nearest Neighbor (kNN) spatial join query. Several experiments evaluate the proposal using real-world datasets. Our approach differs from existing proposals by (1) accounting for the dataset’s unique spatial traits without sampling, (2) considering the computational overhead required to handle non-spatial data, (3) minimizing partition shuffles, (4) computing the optimal utilization of the available resources, and (5) achieving accurate results. This contributes to the problem of spatial data partitioning through (1) providing a comprehensive discussion of the problems facing spatial data partitioning and processing, (2) the development of a novel spatial partitioning technique for in-memory distributed processing, (3) an effective, built-in, load-balancing methodology that reduces spatial query skews, and (4) a Spark-based implementation of the proposed work with an accurate kNN spatial join query. Experimental tests show up to 1.48 times improvement in runtime as well as the accuracy of results.

Pemetaan Persebaran Startup Digital dalam Penerapan Society of Creativity di Indonesia Berbasis WebGIS

Starter companies or technology-based startups or better known as Digital Startups have been booming in many countries, especially in Indonesia in recent years. Various ideas and solutions are offered to be a way out of a problem that is often faced by the community. Incubators, Investors and even the Government are also competing to provide digital Startup development programs, because of this many digital startups have sprung up and started to develop in Indonesia such as its predecessors, namely GoJek, Bukalapak, Tokopedia, and Tiket.com which have become Unicorns. even Decacorn in Indonesia. The mapping of the distribution of the Digital Startup itself needs to be done so that the application of a creative society or the Society of Creativity can be carried out in Indonesia, especially in the current Era 5.0 Society. Geographic Information System is a platform that provides management of spatial data (spatial data) and non-spatial data (attribute data) that can be used to map specific case studies to achieve a specific goal. At present Geographic Information Systems can be developed based on Web or WebGIS. The mapping of the distribution of Digital Startups in Indonesia is currently carried out to facilitate the Government in monitoring and developing the number of digital startups in Indonesia, so that people who are capable of being competitive and creative can be formed in Indonesia because they are always monitored and well developed by the Government. Research on Mapping the Distribution of Digital Startups in the Implementation of the Society of Creativity in Indonesia is based on WebGIS. The editor that will be used is ArcGIS Online.

MADplots: A methodology for visualizing and characterizing energy-dependent attenuation of tissues in spectral computed tomography.

A method for visualizing and analyzing the complete information contained in spectral CT scans using two-dimensional histograms (i.e. Material Attenuation Decomposition plots - MADplots) of the water-photoelectric attenuation versus water-scatter attenuation at the cohort (combination of multiple studies across patients), examination, series, slice, and organ/ROI levels is described. The appearance of a MADplot with several standard biological materials was predicted using ideal material properties available from NIST and the ICRU to generate a map for this non-spatial data space. Software tools were developed to generate MADplots as new DICOM series that facilitate spectral analysis. Illustrative examples were selected from an IRB-approved, retrospective cohort of Spectral Basis Images (SBIs) scanned using a pre-release, dual-layer detector spectral CT. By combining all of the voxels for contrast and non-contrast studies, the predicted appearance of the MADplot was confirmed. Locations of several kinds of tissue, the shape of the tissue distributions in normal lung, and the variations in the manner in which organ-specific MADplots change with pathology are demonstrated for the presence of fat in both the liver and pancreas highlighting the potential use for identifying pathologies on spectral CT images. The examples of MADplots shown at cohort (combined studies), examination, series, slice, organ, and ROI levels illustrate their potential utility in analyzing and displaying spectral CT data. Future studies are directed at developing MADplot based organ segmentation and the automated detection and display of organ based pathologies.

WEB GIS: The Promotion Model of Ngerangan Tourist Village in Klaten Regency through The Sustainable Rural Tourism Context During The Covid-19 Pandemic

The application of large-scale social restrictions (LSSR) during the Covid 19 pandemic in Indonesia significantly impacted the tourism sector. A Sustainable Rural Tourism model is expected to develop tourist village development during the Covid 19 pandemic through the WEB GIS (Website Geographical Information Systems) approach. It creates smart tourism through tourist information systems integrated with tourist websites. Ngerangan Tourist Village in Klaten Regency is a village where most of the population's economy is dependent on MSME (Micro, Small, and Medium enterprises) and farming activities. The research aims to formulate a tourism promotion model in Ngerangan tourism village through WEB GIS. The method used is quantitative. First, identifying the data needs such as spatial data (tourist attractions, tourist safety routes types, and infrastructure for implementing health protocols) and non-spatial data (information on MSME and virtual tours). Second, designing the tourism promotion system through WEB GIS and third, testing to see the output of the WEB GIS produced. The research output is Web GIS using story maps application contains two spatial information about the Ngerangan tourist village. First, spatial information about tourist attractions includes locations, photos, and descriptions of tourist attractions. Second, spatial information regarding health protocols facilities during the Covid-19 pandemic includes locations, photos, and descriptions of health protocol facilities

Development Tourism Geographic Information System of Samarinda

This research is motivated by the low level of dissemination of tourism information in the city of Samarinda. Samarinda is one of the cities with very diverse and interesting tourism potentials to explore. The beauty of the urban area and being one of the cities that is crossed by the largest river in the province of East Kalimantan, namely the Mahakam river, makes it one of the tourist destinations that has its own charm, but unfortunately at this time the tourism places in the city of Samarinda have not been widely publicized due to lack of information. About tourism that is given to the public and there are also some places that have not been or have just been opened to become tourist sites. This study uses spatial data in the form of location coordinates and non-spatial data in the form of attribute data including tourist names, addresses, and types of tours, descriptions and pictures. Making the Samarinda City Tourism Geographic Information System website using the Leaflet API as a base map. The database created using PHPMyAdmin on the Samarinda City Tourism Geographic Information System is based on collecting secondary data on tourism information at the Samarinda City Tourism Office and also collecting primary data by interviewing tourist objects that have not been recorded at the Samarinda City Tourism Office. The system and appearance of the Samarinda City Tourism Geographic Information System website was built with the CodeIgniter 3 framework using Visual Studio Code as a text editor. The results of this study are the Samarinda City Tourism Geographic Information System which provides information on tourist names, addresses, types of tours, location coordinates, descriptions, images and is equipped with search features and also road routes to tourist sites. It is hoped that the Samarinda City Tourism Geographic Information System can provide adequate information about tourism information in the city of Samarinda so as to facilitate the delivery and acceptance to the public, especially prospective tourists.