Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

GIS Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to witness substantial growth over the forecast period, with a notable CAGR of 11.2% from 2024 to 2032. In 2023, the market size was valued at approximately USD 9.1 billion and is expected to reach around USD 23.5 billion by 2032. This growth trajectory is primarily driven by the increasing integration of GIS across various industries, advancements in spatial data analysis technologies, and heightened demand for location-based services. The rising need for urban planning and smart city projects also significantly contributes to the market's expansion, alongside growing investments in infrastructure development across the globe.

Several key factors underpin the robust growth of the GIS software market. Firstly, the surge in demand for spatial data analytics is transforming decision-making processes across sectors such as agriculture, construction, and transportation. GIS software enables organizations to visualize, analyze, and interpret data to understand spatial relationships, patterns, and trends. This capability is crucial for efficient resource management, strategic planning, and effective deployment of assets. Furthermore, the integration of GIS with artificial intelligence and machine learning technologies enhances predictive analytics, enabling more precise forecasting and decision-making, which drives further adoption in both private and public sectors.

Secondly, the expansion of smart city initiatives worldwide is propelling the demand for GIS software. As urban areas continue to grow, there is an increasing need for sophisticated tools that can aid in planning and managing complex infrastructural developments. GIS software plays a pivotal role in urban planning by providing detailed visualization and analysis of spatial data, thereby aiding in effective decision-making concerning transportation, utilities, land use, and environmental management. This is further bolstered by government initiatives aimed at improving urban infrastructure and sustainability, thus contributing significantly to market growth.

Additionally, the growing adoption of location-based services across various industries is another major driver for the GIS software market. These services leverage GIS technology to provide real-time data and analytics, which are essential for navigation, asset tracking, and location-based marketing. The transportation and logistics sectors, in particular, are extensively utilizing GIS for route optimization, fleet management, and logistics planning. Moreover, the proliferation of smartphones and mobile applications has accelerated the demand for these services, further spurring the growth of the GIS software market.

The regional outlook for the GIS software market highlights a varied growth trajectory across different geographies. North America currently holds a significant market share due to the presence of major GIS software vendors and early adoption of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid urbanization, infrastructure development, and increasing investments in smart city projects in countries like China and India are key factors driving the market in this region. Europe also shows promising growth prospects, particularly with the European Union's emphasis on sustainable development and environmental management, which necessitates the use of GIS technology.

Component Analysis

The GIS software market segmentation by component includes both software and services. The software segment is anticipated to hold the largest market share, driven by the increasing adoption of advanced software solutions that offer comprehensive tools for data analysis, mapping, and visualization. Software platforms that integrate GIS with cloud computing, IoT, and AI are seeing heightened demand as they provide more robust, scalable, and efficient solutions for complex spatial data analysis. Companies are continuously innovating to enhance the functionalities of GIS software, which is further propelling the growth of this segment.

Within the software segment, desktop GIS applications continue to dominate due to their widespread use in detailed data analysis and map creation. However, WebGIS and mobile GIS applications are rapidly gaining traction owing to their accessibility and convenience, allowing users to analyze spatial data from anywhere and at any time. This shift is largely attributed to the growing need for real-time data access and the integration

Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

Rocky desertification, a pressing environmental concern in Southwest China, significantly impacts local living conditions and regional sustainability. Employing remote sensing on a macro scale, this study focuses on identifying and analyzing the spatial distribution and driving factors of rocky desertification. Conducted in Southwest China, using Landsat data from Google Earth Engine for 2020, the research quantitatively extracts information on rocky desertification patches through traditional methods. Excluding unlikely areas using land use data, spatial distribution features and driving factors are examined via GIS spatial analysis and a geodetector model. The main conclusions are as follows. Rocky desertification covers 217,530.4 km2 (accounting for 15.6% of Southwest China), with areas of slight, moderate, and severe rocky desertification at 81.3%, 7.1%, and 11.6%, respectively. Spatially, rocky desertification primarily occurs in areas where lithology is carbonate rock between clastic rocks and continuous limestone, slope exceeds 15°, elevation ranges is 1000–2000 m, land use types are grassland and woodland, precipitation is 80–120 mm, and population density is below 50 people/km2. Human activities have minimal influence. Geodetector analysis identifies lithology, land use type, and slope as primary driving factors, with interactive effects of lithology and land use type and slope and land use type jointly influencing rocky desertification formation in Southwest China. Methods The rocky desertification data were obtained from Landsat 8 operational land imager (OLI) image data provided by the U.S. Geological Survey (USGS) ("https://www.usgs.gov"), de-clouded based on the Google Earth Engine (GEE), and atmospherically corrected using ENVI5.3 Fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction tool with a spatial resolution of 30 m [37,38]. The land use type data with a spatial resolution of 1000 m were downloaded from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences ("https://www.resdc.cn"). The land use types in these data mainly include watersheds, rivers, and urban industrial construction land, cultivated land, woodland, grassland, and unutilized land. The overall accuracy of this dataset reached 95.41%, which met the needs of this study. The digital elevation model (DEM) data for the study area were obtained from the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences ("http://www.gscloud.cn"), with a spatial resolution of 30 m. The precipitation data for 2020 were obtained from the China Meteorological Administration ("http://www.cma.gov.cn/"). The monthly average precipitation data of Southwest China for 2020 were obtained after kriging interpolation. The population density data were obtained from the 2020 Yearbook of the National and Local Government Statistical Bureau ("http://www.stats.gov.cn/tjsj/ndsj/").

Holocene climate variability in the Mediterranean Basin is often cited as a potential driver of societal change, but the mechanisms of this putative influence are generally little explored. In this paper we integrate two tools–agro-ecosystem modeling of potential agricultural yields and spatial analysis of archaeological settlement pattern data–in order to examine the human consequences of past climatic changes. Focusing on a case study in Provence (France), we adapt an agro-ecosystem model to the modeling of potential agricultural productivity during the Holocene. Calibrating this model for past crops and agricultural practices and using a downscaling approach to produce high spatiotemporal resolution paleoclimate data from a Mediterranean Holocene climate reconstruction, we estimate realistic potential agricultural yields under past climatic conditions. These serve as the basis for spatial analysis of archaeological settlement patterns, in which we examine the changing relationship over time between agricultural productivity and settlement location. Using potential agricultural productivity (PAgP) as a measure of the human consequences of climate changes, we focus on the relative magnitudes of 1) climate-driven shifts in PAgP and 2) the potential increases in productivity realizable through agricultural intensification. Together these offer a means of assessing the scale and mechanisms of the vulnerability and resilience of Holocene inhabitants of Provence to climate change. Our results suggest that settlement patterns were closely tied to PAgP throughout most of the Holocene, with the notable exception of the period from the Middle Bronze Age through the Early Iron Age. This pattern does not appear to be linked to any climatically-driven changes in PAgP, and conversely the most salient changes in PAgP during the Holocene cannot be clearly linked to any changes in settlement pattern. We argue that this constitutes evidence that vulnerability and resilience to climate change are strongly dependent on societal variables.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.

Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.html

The spatial configuration and social performance of public sports facilities serve as crucial indicators for evaluating the equity of public sports services and the coherence of urban spatial structure. As Shanghai accelerates its development into a globally renowned sports city, the construction of public sports facilities has encountered significant opportunities. However, challenges persist in the spatial distribution, accessibility, and quality of these facilities. This study investigates the spatial agglomeration characteristics, accessibility, and social performance of urban public sports facilities in Shanghai at both the street and grid scales. Using geographic information system (GIS) tools and analytical methods such as kernel density estimation, standard deviation ellipse, spatial autocorrelation, Gaussian two-step moving search, and the Gini coefficient, the analysis yields the following findings: 1) Public sports facilities in Shanghai are concentrated in the central urban areas and exhibit scattered spatial distribution patterns in peripheral regions. These facilities display a significant directional coupling with population distribution (northeast-southwest), reflecting pronounced spatial imbalances. 2) Social performance analysis reveals clear regional inequities in Shanghai’s public sports facilities. While overall accessibility is relatively high, disparities remain, with suburbs facing facility shortages. Regional equity measurements indicate that the Gini coefficient for public sports facilities in Shanghai is 0.58. Central urban areas possess a high density of facilities, while suburban areas suffer from inadequate facility coverage, leading to uneven service radii and a pattern of high agglomeration but low coverage. 3) The social equity analysis shows that the service capacity entropy of public sports facilities exhibits a distinct spatial distribution, characterized by high values in the east and west and low values in the center. The highest entropy value is 4.25, while the lowest is 0.02. This study provides valuable insights for the planning and optimization of urban public sports facilities in Shanghai, contributing to the enhancement of spatial equity and service effectiveness.

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

NOAA and Rutgers University Distribution Mapping and Analysis PortalThis interactive user-friendly website provides visualization and analysis tools to better track, understand, and respond to shifting distributions of marine species. It allows users to examine changes in species distribution over time by looking at location maps and graphs of key indicators (changes in latitude, depth, and range limits). Understanding how species are distributed in space and time and the factors that drive spatial patterns in distribution and abundance are central questions in ecology and important for species conservation and management.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

Geographic Information System (GIS) Tools Market Outlook

The global Geographic Information System (GIS) Tools market is poised for significant expansion, with a projected market size of approximately $15.2 billion in 2023, anticipated to reach $28.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.3%. This growth can be attributed to the increasing integration of advanced GIS technologies across various sectors such as agriculture, transportation, and government services, driven by the need for efficient data management and spatial analysis capabilities. The adoption of GIS tools is further influenced by the growing demand for real-time geographic data, which plays a crucial role in decision-making processes across multiple industries.

One of the primary growth factors for the GIS Tools market is the burgeoning demand for high-precision mapping and spatial data analytics. Industries such as agriculture and construction are increasingly relying on GIS technology to optimize resource management and streamline operations. The ability of GIS tools to provide detailed insights into geographical patterns and trends allows companies to make informed decisions, thereby improving operational efficiency and reducing costs. Additionally, advancements in remote sensing technology and data collection methods have significantly enhanced the accuracy and reliability of GIS data, further fueling its adoption across various sectors.

The increasing deployment of GIS tools in urban planning and smart city projects is another key driver of market growth. Governments worldwide are leveraging GIS technology to enhance infrastructure planning, improve public services, and manage environmental resources more effectively. The integration of GIS in smart city initiatives enables authorities to monitor and manage urban environments in real-time, leading to better resource allocation and improved quality of life for residents. As cities continue to expand and evolve, the demand for advanced GIS solutions is expected to grow exponentially, providing significant opportunities for market players.

Furthermore, the rise of location-based services and telematics has expanded the application of GIS tools in the transportation and logistics sectors. Companies are utilizing GIS technology to optimize route planning, track assets, and enhance supply chain management. The integration of GIS with telematics systems allows for real-time monitoring and analysis of vehicle movements, improving fleet efficiency and reducing operational costs. As the transportation industry continues to embrace digital transformation, the demand for GIS tools is likely to increase, further driving market growth.

In terms of regional outlook, North America currently leads the GIS Tools market, driven by high adoption rates of advanced technologies and significant investments in infrastructure development. The presence of major GIS solution providers and a well-established IT infrastructure further contribute to the region's dominance. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, driven by rapid urbanization, increasing government initiatives for infrastructure development, and the growing adoption of GIS technology in emerging economies such as China and India. Europe and the Middle East & Africa regions are also expected to experience steady growth, supported by advancements in GIS applications and the rising need for efficient spatial data management solutions.

The role of a Gis Data Collector is increasingly becoming pivotal in the GIS Tools market. These professionals are responsible for gathering, verifying, and maintaining the spatial data that forms the backbone of GIS applications. With the growing emphasis on high-precision mapping and real-time data analysis, the demand for skilled Gis Data Collectors is on the rise. They play a crucial role in ensuring the accuracy and reliability of geospatial information, which is essential for effective decision-making across various sectors. As industries continue to leverage advanced GIS technologies, the expertise of Gis Data Collectors will be indispensable in facilitating seamless data integration and enhancing the overall quality of GIS solutions.

Component Analysis

The GIS Tools market can be segmented by component into software, hardware, and services, each playing a vital role in the overall market dynamics. The software segment is expected to hold the largest market

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others.

This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

Appendix items and Supplementary items (data and programming codes) for Spatial Distribution Justice Framework project.

This research explores the innovative use of a 3D gaming engine, Minetest, for visualizing changes in canopy cover change at the University of British Columbia (UBC) campus, addressing the pressing challenge of urban expansion on green spaces. We compared and visualized canopy height change for UBC campus in both 2D traditional environment and 3D gaming engine environment and we revealed a consistency between the spatial patterns of canopy cover change observed in both environments. Our findings indicate 3D environment provided multi-dimensional insights into canopy cover changes, offering decision-makers more straightforward and transparent insight than traditional maps can achieve in an immersive and interactive environment. We observed there is a significant change in canopy cover with 25 percent loss in total where Wesbrook community area experienced the most significant canopy cover loss in past 5 years due to rapid urban development. Our findings goes beyond merely presenting geographic maps and attributes from a 3D voxel game perspective. Instead, it will serve as a useful tool and references for UBC decision makers and planners to inform management plan on the pathway of building a green, well-planned community.

The GIS layer "Census_sum_15" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2015 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2015). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California.

This research study analysed the crime rate spatially and it examined the relationship between crime and spatial factors in Saudi Arabia. It reviewed the related literature that has utilised crime mapping techniques, such as Geographic Information Systems (GIS) and remote sensing (RS); these techniques are a basic part of effectively helping security and authority agencies by providing them with a clear perception of crime patterns and a surveillance direction to track and tackle crime. This study analysed the spatial relationships between crime and place, immigration, changes in urban areas, weather and transportation networks. The research study was divided into six parts to investigate the correlation between crime and these factors. The first part of the research study examined the relationship between crime and place across the 13 provinces of Saudi Arabia using GIS techniques based on population density in order to identify and visualise the spatial distributions of national and regional crime rates for drug crimes, thefts, murders, assaults, and alcohol-related and ‘outrageous crimes’ (offences against Islam) over a 10-year period from 2003 to 2012. Social disorganisation theory was employed to guide the study and explain the diversity in crime patterns across the country. The highest rates of overall crimes were identified in the Northern Borders Province and Jizan, which are located in the northern and southern regions of the country, respectively; the eastern area of the country was found to have the lowest crime rate. Most drug offences occurred in the Northern Borders Province and Jizan; high rates of theft were recorded in the Northern Borders Province, Jouf Province and Makkah Province, while the highest rates of homicide occurred in Asir Province. The second part of the research study aimed to determine the trends of overall crime in relation to six crime categories: drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous or sex-related crimes, in Saudi Arabia’s 13 provinces over a 10-year period from 2003 to 2012. The study analysed the spatial and temporal changes of criminal cases. Spatial changes were used to determine the differences over the time period of 2003–2012 to show the provincial rates of change for each crime category. Temporal changes were used to compute the trends of the overall crime rate and crimes in the six categories per 1,000 people per year. The results showed that the overall crime rate increased steadily until 2008; thereafter it decreased in all areas except for the Northern Borders Province and Jizan, which recorded the highest crime rates throughout the study period. We have explained that decrease in terms of changes in wages, support for the unemployed and service improvements, which were factors that previous studies also emphasised as being the primary cause for the decrease. This study includes a detailed discussion to contribute to the understanding of the changes in the crime rates in these categories throughout this period in the 13 provinces of Saudi Arabia. The third part of the research study aimed to explain the effects of immigration on the overall crime rate in the six most significant categories of crime in Saudi Arabia, which are drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous crimes, during a 10-year period from 2003 to 2012, in all 13 administrative provinces. It also sought to identify the provinces most affected by the criminal activities of immigrants during this period. No positive association between immigrants and criminal cases was found. It was clearly visible that the highest rate of overall criminal activities was in the south, north and Makkah areas, where there is a high probability of illegal immigrants. This finding supports the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study’s results provide recommendations to the Saudi government, policy-makers, decision-makers and immigration authorities, which could assist in reducing crimes perpetrated by immigrants. In the fourth part of the research study, urban areas were examined in relation to crime rates. Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased population growth and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanisation and crime are related. However, those studies focused on land uses, types of land use and urban forms, such as the physical features of neighbourhoods, roads, shopping centres and bus stations. It is very important for criminologists and urban planning decision-makers to understand the correlation between urban area expansion and crime. In this research, satellite images were used to measure urban expansion over a 10-year period; the study tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. The findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. Moreover, the results show that the correlations are stronger in areas that have undergone greater urban growth. This study did not evaluate many other factors that might affect the crime rate, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, the quality of neighbourhoods, and the number of police officers. However, this research will be of particular interest to those who aim to use remote sensing to study crime patterns. The fifth part of the research study investigated the impacts of weather on crime rates in two different cities: Riyadh and Makkah. While a number of studies have examined climate influences on crime and human behaviour by investigating the correlation between climate and weather elements, such as temperature, humidity and precipitation, and crime rates, few studies have focused on haze as a weather element and its correlation with crime. This research examined haze as a weather variable to investigate its effects on criminal activity and compare its effects with those of temperature and humidity. Monthly crime data and monthly weather records were used to build a regression model to predict crime cases based on three weather factors using temperature, humidity and haze values. This model was applied to two provinces in Saudi Arabia with different types of climates: Riyadh and Makkah. Riyadh Province is a desert area in which haze occurs approximately 17 days per month on average. Makkah Province is a coastal area where it is hazy an average of 4 days per month. A measurable relationship was found between each of these three variables and criminal activity. However, haze had a greater effect on theft, drug-related crimes and assault in Riyadh Province than temperature and humidity. Temperature and humidity were the efficacious variables in Makkah Province, while haze had no significant influence in that region. Finally, the sixth part of the research study examined the influence of the quality and extent of road networks on crime rates in both urban and rural areas in Jizan Province, Saudi Arabia. We performed both Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) where crime rate was the dependent variable and paved (sealed) roads, non-paved (unsealed/gravel) roads and population density were the explanatory variables. Population density was a control variable. The findings reveal that, across all 14 districts in that province, the districts with better quality paved road networks had lower rates of crime than the districts with unpaved roads. Furthermore, the more extensive the road networks, the lower the crime rate whether or not the roads were paved. These findings concur with those reported in studies conducted in other countries, which revealed that rural areas are not always the safe, crime-free places they are often believed to be. This research contributes knowledge about the geographical information of criminal movement, and it offers some conceivable reasons for crime rates and patterns in relation to the spatial factors and the socio-cultural perspectives of Saudi Arabian life. More geographical research is still needed in terms of criminology, which will provide a better understanding of crime patterns, particularly in Saudi Arabia, and across the globe, where the spatial distribution of criminal cases is an essential base in crime research. Furthermore, additional studies are needed to investigate the complex interventions of the effect of different spatial variables on crime and the uncertainties correlation with the impact of environmental factors. This can help predict the impact of socioeconomic and environmental factors. The greater part of such an investigation will enhance the understanding of crime patterns, which is imperative for advancing a framework that can be used to address crime reduction and crime prevention.

This geodataabase provides an estimate to the spatial distribution of potential historical habitat for California Coastal Chinook Salmon, Central California Coast Coho Salmon, Northern California Steelhead and Central California Coast Steelhead. Intrinsic potential measures the potential for development of favorable habitat characteristics as a function of the underlying geomorphic and hydrological attributes, as determined through a Digital Elevation Model (DEM) and mean annual precipitation grid. The model does not predict the actual distribution of "good'' habitat, but rather the potential for that habitat to occur, nor does the model predict abundance or productivity. Additionally, the model does not predict current conditions, but rather those patterns expected under pristine conditions as related through the input data. Thus, IP provides a tool for examining the historical distribution of habitat among and within watersheds, a proxy for population size and structure, and a useful template for examining the consequences of recent anthropogenic activity at landscape scales.

Understanding building metabolism is critical for guiding urban resource management and addressing challenges in urban sustainable development. Key attributes of buildings, including geolocation, footprint, height, and vintage, are crucial to characterizing spatiotemporal patterns of building metabolism. However, these attributes are usually challenging to obtain broadly and automatically, which obscures a comprehensive understanding and accurate assessment of urban metabolism. Moreover, the lack of a finer spatial pattern of these attributes shadows a spatially explicit characterization of material stock and flow in cities. In this study, we took Shenzhen—whose urbanization over the past three decades has been unprecedented in China and even around the world— has been taken as an example to develop a city-level building dataset based on a random-forest model and quantify the spatiotemporal patterns of material metabolism at relatively high spatial resolution (in 500 m × 500 m grids) by combing material flow analysis (MFA) with geographic information system (GIS). The results show that Shenzhen grew from a small town with 281.02 × 106 m3 of buildings in the 1990s to a mega-city with 3585.5 × 106 m3 of buildings in 2018 and expanded both outward and upward from downtown to suburban areas. The urban “weight” (material stock) increased from 92.69 Mt in the 1990s to 1667.8 Mt in 2018 and tended to be saturated, with an average growth rate of 9.5% per year. Spatially, the south-central areas were the largest container of material stocks and generated the most demolition waste. The spatially explicit maps of building three-dimensional (3-D) form and vintage provide detailed information for architectural conservation and could support the decision-making for urban renewal planning. The spatiotemporal patterns of in-use material stocks and potential generation of construction and demolition waste (CDW) provide a benchmark of environmental risk assessment and potential secondary resources to reduce “original” material consumption, which could help alter urban renewal to an environmental-friendly and sustainable trajectory.