LandMark Geospatial

28/12/2023

28/12/2023

USDA today announced a proposal to amend all 128 forest land management plans to conserve and steward old-growth forest conditions on national forests and grasslands nationwide.

https://www.usda.gov/media/press-releases/2023/12/19/biden-harris-administration-proposes-first-its-kind-national-forest

16/12/2023

Surface Analysis Map in ArcGIS

Surface Analysis represents height values for an Elevation surface or structure of a Surface. In this Tutorial, Learn how to create different types of Surface Analysis map in ArcGIS.
Now perform to 6 Surface Analysis model.
1. Aspect
2. Contour
3. Curvature
4. Hillshade
5. Slope
6. Viewshade

Launch ArcGIS Software, then open the Toolbox, ArcToolbox window is appears to expend Spatial Analyst Tools, then expend the Surface subgroup. All are surface analysis models available in this group. Select and double click it.

Aspect
The Aspect identifies the down-slope direction of the maximum rate of change in value from each cell to its neighbors. Now Aspect can be thought of as the slope direction.
Open Aspect. The Aspect window appears to select Input Raster, and Output Raster, and click OK.

Contour
Contours are line features that connect locations of equal elevation value in a raster dataset. That represents continuous phenomena such as elevation.
Open Contour. The Contour window appears to select Input Raster, Output Features, and the most important input contour interval (ex.- 100), then click OK.

Curvature
Curvature represents, determine the acceleration and convergence of flow in a drainage basin to better understand topsoil erosion.
Open Curvature. The Curvature window appears to select Input Raster, and Output Raster, and click OK.

Hillshade
Create a Hillshade relief from an elevation surface raster by considering appropriate illumination source between Angel and Shadow.
Open Hillshade, The Hillshade window appears to select Input Raster, and Output Raster, and click OK.

Slope
The Slope represents the variations of the landscape. You want to find the areas most at risk of landslide based on the angle of steepness in an area.
Open Slope, The Slope window appears to select Input Raster, and Output Raster (you can choose output measurement- Degree or Percent), and click OK.

Viewshed
A Viewshed is useful when you want to know how visible objects will be. For instance, you might want to find the location with the most expansive view in an area. Because you want to choose the best location for an observation tower or scenic overlook.
Perform Viewshade first, create a Point Vector file and point out any particular place in your Raster data. Open Viewshade. The Viewshade window appears to select Input Raster, Input point feature, and Output Raster, and click OK.

16/12/2023

New Zealand from the ISS

16/12/2023

5 Types of Network Analysis in GIS

1. Point-to-point analysis
A point-to-point analysis is the most common routing problem. It consists of a set of points to find the most optimal route based on specific criteria.
Find Nearest – Where is the closest destination? It finds the nearest destination based on a starting point with multiple potential destinations.
Shortest Distance – What’s the fastest route? This analysis accumulates all distances, as you travel out from one point to the other. Then, it finds the route with the least distance.
Fastest route – Which route takes the least amount of time? This network analysis takes into account speed limits, road classification, and other costs to determine the least travel time.
Other types of point-to-point analysis include the most eco-friendly, scenic, and winding routes. Each type of network analysis generates directions from origin to destination.
It can also include the capability to select the mode of travel such as emergency vehicles, trucks, pedestrians, transit, or cycling.

2. Finding Coverage
In this type of network analysis, drive-time areas correspond to the distance that can be reached within a specific amount of time.
Service Areas – Which houses are within 5, 10, and 15 minutes from a fire station? This type of network analysis also can understand where businesses cover and if there are any gaps.
Drive-time areas are different from buffers because it takes into account a street network. Buffers can cross water bodies but drive-time areas can only cross water when there’s a bridge.

3. Optimize Fleet
This tool is ideal when your main goal is to service a set of orders in the traveling salesperson problem. Also, you can best minimize the overall operating cost, by managing sets of vehicles and drivers.
Optimize Fleet – The purpose of this network analysis tool is to find the most efficient route for delivery, repair, transit, or any type of fleet service.
For example, a furniture company might want to use several trucks to deliver furniture to homes. Alternatively, a fleet might want to schedule their weekly visits, including all the logistics.

4. Select Optimal Site
Optimal site selection takes into account the demand to locate the best location given several facilities. For example, it can help decide where to build new hospitals depending on existing hospitals and the available demand.
Location-allocation – This network analysis tool helps business owners pinpoint optimal location for their store. It can also compare with competing stores to target market share.

5. Origin-Destination – OD Cost Matrix
In ArcGIS, this is the OD Cost Matrix, which measures the least cost path from multiple origin points to multiple destinations.
OD Cost Matrix – This type of network uses two sets of locations to find the distances between all of the locations in two sets.
For example, it can list the routes and directions for all stores and warehouses. In emergency dispatching, one set of locations consists of the incident, and the other set is all the available fire stations.

16/12/2023

Types of network analysis layers

The ArcGIS Network Analyst extension allows you to solve common network problems, such as finding the best route across a city, finding the closest emergency vehicle or facility, identifying a service area around a location, servicing a set of orders with a fleet of vehicles, or choosing the best facilities to open or close.
Route
Network Analyst can find the best way to get from one location to another or to visit several locations. The locations can be specified interactively by placing points on the screen, entering an address, or using points in an existing feature class or feature layer. If you have more than two stops to visit, the best route can be determined for the order of locations as specified by the user. Alternatively, Network Analyst can determine the best sequence to visit the locations, which is known as solving the traveling salesman problem.
What's the best route?
Whether finding a simple route between two locations or one that visits several locations, people usually try to take the best route. But "best route" can mean different things in different situations.
The best route can be the quickest, shortest, or most scenic route, depending on the impedance chosen. If the impedance is time, then the best route is the quickest route. Hence, the best route can be defined as the route that has the lowest impedance, where the impedance is chosen by the user. Any valid network cost attribute can be used as the impedance when determining the best route.
In the example below, the first case uses time as an impedance. The quickest path is shown in blue and has a total length of 4.5 miles, which takes 8 minutes to traverse.

In the next case, distance is chosen as the impedance. Consequently, the length of the shortest path is 4.4 miles, which takes 9 minutes to traverse.

Along with the best route, Network Analyst provides directions with turn-by-turn maps that can be printed.

Closest facility
Finding the closest hospital to an accident, the closest police cars to a crime scene, and the closest store to a customer's address are all examples of closest facility problems. When finding closest facilities, you can specify how many to find and whether the direction of travel is toward or away from them. Once you've found the closest facilities, you can display the best route to or from them, return the travel cost for each route, and display directions to each facility. Additionally, you can specify an impedance cutoff beyond which Network Analyst should not search for a facility. For instance, you can set up a closest facility problem to search for hospitals within 15 minutes' drive time of the site of an accident. Any hospitals that take longer than 15 minutes to reach will not be included in the results.

The hospitals are referred to as facilities, and the accident is referred to as an incident. Network Analyst allows you to perform multiple closest facility analyses simultaneously. This means you can have multiple incidents and find the closest facility or facilities to each incident.

Service areas
With Network Analyst, you can find service areas around any location on a network. A network service area is a region that encompasses all accessible streets, that is, streets that lie within a specified impedance. For instance, the 10-minute service area for a facility includes all the streets that can be reached within 10 minutes from that facility.
What is accessibility?
Accessibility refers to how easy it is to go to a site. In Network Analyst, accessibility can be measured in terms of travel time, distance, or any other impedance on the network. Evaluating accessibility helps answer basic questions, such as, How many people live within a 10-minute drive from a movie theater? or How many customers live within a half-kilometer walking distance from a convenience store? Examining accessibility can help you determine how suitable a site is for a new business. It can also help you identify what is near an existing business to help you make other marketing decisions.
Evaluating accessibility
One simple way to evaluate accessibility is by a buffer distance around a point. For example, find out how many customers live within a 5-kilometer radius of a site using a simple circle. However, considering people travel by road, this method won't reflect the actual accessibility to the site. Service networks computed by Network Analyst can overcome this limitation by identifying the accessible streets within five kilometers of a site via the road network. Once created, you can use service networks to see what is alongside the accessible streets, for example, find competing businesses within a 5-minute drive.
Multiple concentric service areas show how accessibility changes with an increase in impedance. It can be used, for example, to show how many hospitals are within 5-, 10-, and 15-minute drive times of schools.
By solving with traffic data, you can see which hospitals can be reached within these drive times for different times of the day. The reachable hospitals may change due to traffic conditions.
OD cost matrix
With Network Analyst, you can create an origin-destination (OD) cost matrix from multiple origins to multiple destinations. An OD cost matrix is a table that contains the network impedance from each origin to each destination. Additionally, it ranks the destinations that each origin connects to in ascending order based on the minimum network impedance required to travel from that origin to each destination.
The best network path is discovered for each origin-destination pair, and the cost is stored in the attribute table of the output lines. Even though the lines are straight for performance reasons, they always store the network cost, not straight-line distance. The graphic below shows the results of an OD cost matrix analysis that was set to find the cost to reach the four closest destinations from each origin.
The straight lines can be symbolized in various ways, such as by color, representing which point they originate from, or by thickness, representing the travel time of each path.

Vehicle routing problem
A dispatcher managing a fleet of vehicles is often required to make decisions about vehicle routing. One such decision involves how to best assign a group of customers to a fleet of vehicles and to sequence and schedule their visits. The objectives in solving such vehicle routing problems (VRP) are to provide a high level of customer service by honoring any time windows while keeping the overall operating and investment costs for each route as low as possible. The constraints are to complete the routes with available resources and within the time limits imposed by driver work shifts, driving speeds, and customer commitments.
Network Analyst provides a vehicle routing problem solver that can be used to determine solutions for such complex fleet management tasks.
Consider an example of delivering goods to grocery stores from a central warehouse location. A fleet of three trucks is available at the warehouse. The warehouse operates only within a certain time window—from 8:00 a.m. to 5:00 p.m.—during which all trucks must return back to the warehouse. Each truck has a capacity of 15,000 pounds, which limits the amount of goods it can carry. Each store has a demand for a specific amount of goods (in pounds) that needs to be delivered, and each store has time windows that confine when deliveries should be made. Furthermore, the driver can work only eight hours per day, requires a break for lunch, and is paid for the amount spent on driving and servicing the stores. The goal is to come up with an itinerary for each driver (or route) such that the deliveries can be made while honoring all the service requirements and minimizing the total time spent on a particular route by the driver. The figure below shows three routes obtained by solving the above vehicle routing problem.
Location-allocation
Location-allocation helps you choose which facilities from a set of facilities to operate based on their potential interaction with demand points. It can help you answer questions like the following:
Given a set of existing fire stations, which site for a new fire station would provide the best response times for the community?
If a retail company has to downsize, which stores should it close to maintain the most overall demand?
Where should a factory be built to minimize the distance to distribution centers?

In these examples, facilities would represent the fire stations, retail stores, and factories; demand points would represent buildings, customers, and distribution centers.
The objective may be to minimize the overall distance between demand points and facilities, maximize the number of demand points covered within a certain distance of facilities, maximize an apportioned amount of demand that decays with increasing distance from a facility, or maximize the amount of demand captured in an environment of friendly and competing facilities.
The map below shows the results of a location-allocation analysis meant to determine which fire stations are redundant. The following information was provided to the solver: an array of fire stations (facilities), street midpoints (demand points), and a maximum allowable response time. The response time is the time it takes firefighters to reach a given location. The location-allocation solver determined that the fire department can close several fire stations and still maintain a three-minute response time.
Out of the current set of fire stations, nine fire stations can close, and a minimum of seven are needed for the department to still be able to respond to emergencies within three minutes.

Time-dependent analysis
All of the solvers listed above allow you to incorporate live and historical traffic data into an analysis so you can find the best route for a given time of day; locate the best place to preposition an ambulance at 8:00 a.m., 12:00 p.m., 4:00 p.m., and so on; and generate service areas for different times of the day. The results of any analysis may change for different dates and times because traffic conditions and travel times may change.

16/12/2023

Hazard Maps
Hazard maps are developed to illuminate areas that are affected or vulnerable to a particular hazard. They are typically made for natural hazards such as earthquake ground motion, flooding, landslides, liquefaction, and tsunami. Hazard maps are tools that when properly utillized by planners, developers, and engineers, can save lives and economic losses by avoiding exposure to some hazards while designing other development to mitigate or neutralize the potential negative effects of these hazards.

Earthquakes and their secondary effects involve numerous hazards that can and are mapped separately where hazard identification and mitigation are a priority. Ground shaking can lead to soil liquefaction and landslides where there is a susceptibility to these hazards. Ground failure leading to damage to dikes and dams can lead to flooding so understanding the nature and extent of local flood plains is important in managing earthquake risk.

This section provides an overview of hazard mapping in the Pacific Northwest and provides links that lead to additional information and original source material:

Tsunami hazard and inundation maps for the coasts of Washington, Oregon, California, and British Columbia.
Landslide hazard maps.
Washington State Department of Natural Resources Geology Information Portal

Hazard maps are created and used in conjunction with several natural disasters.
Different hazard maps have different uses. For instance, the hazard map created by the Rizal Geological Survey is used by Rizalian insurance agencies in order to properly adjust insurance for people living in hazardous areas.Hazard maps created for flooding are also used in insurance rate adjustments.Hazard maps can also be useful in determining the risks of living in a certain area.Hazard maps can help people become aware of the dangers they might face from natural disasters in a specific area.
Types:
Natural Disasters
Geological disasters
Avalanches and landslides
Earthquakes

Hydrological disasters
Floods
Tsunami
Wildfires

Non-Natural Disasters
Traffic accidents

16/12/2023

What is Geostatistics ?
Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. Many geostatistical tools were originally developed as a practical means to describe spatial patterns and interpolate values for locations where samples were not taken. Those tools and methods have since evolved to not only provide interpolated values, but also measures of uncertainty for those values. The measurement of uncertainty is critical to informed decision making, as it provides information on the possible values (outcomes) for each location rather than just one interpolated value. Geostatistical analysis has also evolved from uni- to multivariate and offers mechanisms to incorporate secondary datasets that complement a (possibly sparse) primary variable of interest, thus allowing the construction of more accurate interpolation and uncertainty models.

Geostatistics is widely used in many areas of science and engineering, for example:

The mining industry uses geostatistics for several aspects of a project: initially to quantify mineral resources and evaluate the project's economic feasibility, then on a daily basis in order to decide which material is routed to the plant and which is waste, using updated information as it becomes available.
In the environmental sciences, geostatistics is used to estimate pollutant levels in order to decide if they pose a threat to environmental or human health and warrant remediation.
Relatively new applications in the field of soil science focus on mapping soil nutrient levels (nitrogen, phosphorus, potassium, and so on) and other indicators (such as electrical conductivity) in order to study their relationships to crop yield and prescribe precise amounts of fertilizer for each location in the field.
Meteorological applications include prediction of temperatures, rainfall, and associated variables (such as acid rain).
Most recently, there have been several applications of geostatistics in the area of public health, for example, the prediction of environmental contaminant levels and their relation to the incidence rates of cancer.
In all of these examples, the general context is that there is some phenomenon of interest occurring in the landscape (the level of contamination of soil, water, or air by a pollutant; the content of gold or some other metal in a mine; and so forth). Exhaustive studies are expensive and time consuming, so the phenomenon is usually characterized by taking samples at different locations. Geostatistics is then used to produce predictions (and related measures of uncertainty of the predictions) for the unsampled locations. A generalized workflow for geostatistical studies is described in The geostatistical workflow.

What is the Importance of Geostatistics?
Geostatistics is the study of statistics with a focus on spatial and temporal information. The aim is to model and find patterns of geographic phenomena.
The field of geostatistics covers a wide range of spatial statistical topics such as:
* Semi-variograms to characterize the spatial pattern in the data
* Kriging for spatial prediction
* Standard error to measure uncertainty about unsampled values
Geostatistics is a growing field of study that we use in mining, climate studies, soil science, and most environmental fields.

Why use geostatistics?
The three main tools that geostatistics provides are:
• Semi-variograms to model the relationship between all pairs of points.
• Kriging modeling to predict values at unsampled locations.
• Standard error to measure confidence at unsampled values.
For example, if you have soil samples at specific locations, geostatistics can answer these types of questions:
• What is the forecasted amount of soil moisture at unsampled locations?
• How confident is the spatial prediction for the amount of soil moisture true?
This is different from deterministic interpolation techniques like Inverse Distance Weighting (IDW), where it just estimates unknown locations.

In general, IDW uses a predefined power function. Then, the result is what it is. But it doesn’t tell you how confident you are.

Geostatistics Tools and Topics
Semi-variograms
Geostatistics provides descriptive tools like semivariograms to identify underlying trends in spatial phenomena. According to Tobler’s First Law of Geography, closer things are more related than things farther away. This is also the main idea for the concept of spatial autocorrelation.
The semivariogram graphs out all pairs of data according to distance. Observations closer together have a higher correlation. But at a certain distance (range), there is no longer a relationship between points that are close together.

The semivariogram depicts the relationship until it hits the sill where further samples are no longer correlated. The purpose is to fit a mathematical function that models the trend in your semivariogram.

For example, you can select a semivariogram that is:
�Spherical��Circular��Exponential��Gaussian��Linear
Kriging Interpolation
Kriging is an interpolation technique that leverages the spatial correlation between samples to predict values at unsampled locations. But the main difference is that you can build it using the mathematical function obtained from the semivariogram.
Here are the different types of kriging available in geostatistics:

Co-Kriging adds a second related variable so you can improve the prediction with secondary information. For example, to predict precipitation change in mountainous areas, you can add elevation data as a covariate to rainfall amounts.
Empirical Bayesian Kriging (EBK) can help by treating local variance separately. Instead of having a similar variance to a whole extent, EBK performs kriging as a separate underlying process in different areas. It still performs kriging, but it is done locally.
Universal Kriging adds trend surface analysis (or drift) with ordinary kriging by accounting for trends.
Indicator Kriging carries through ordinary kriging with binary data (0 and 1) such as urban and non-urban cells.
Probability Kriging uses binary data (similar to indicator kriging) and estimates unknown points for a series of cutoffs.
Standard Error
Geostatistics is advantageous because it assesses uncertainty for unsampled values with a standard error surface map. A standard error map represents a measure of confidence of how likely that prediction will be true.

Standard error assesses the robustness of your kriging model. By comparing actual versus predicted values, it assesses uncertainty by building a surface of residuals.
In general, you get a higher standard of error when you have a sparse amount of observations. When error exceeds a critical threshold, expert knowledge can contribute to the process of the variogram.
Applications and Uses
Geostatistics was originally developed for the mining industry to estimate and manage ore and mineral resources.
But geostatistics applies to various types of spatial phenomena with local variation. For example, we use it to:
• Predict weather, climate, pollution, and other atmospheric phenomena.
• Assess soil attributes and chemistry which vary at all scales.
• Measure the abundance of fish for a sustainable population in fisheries.
Geostatistics is an emerging field of study in engineering, geophysics, and most natural phenomena.

16/12/2023

Line Density
The Line Density tool calculates a magnitude-per-unit area from polyline features that fall within a radius around each cell.
The Line Density tool calculates the density of linear features in the neighborhood of each output raster cell. Density is calculated in units of length per unit of area.
Conceptually, a circle is drawn around each raster cell center using the search radius. The length of the portion of each line that falls within the circle is multiplied by its Population field value. These figures are summed, and the total is divided by the circle's area.

Usage
1- Only the portion of a line within the neighborhood is considered when calculating the density. If no lines fall within the neighborhood at a particular cell, that cell is assigned NoData.

2- Larger values of the radius parameter produce a more generalized density raster. Smaller values produce a raster that shows more detail.

3- The values on the output raster will always be floating point.

4- The Output cell size parameter can be defined by a numeric value or obtained from an existing raster dataset. If the cell size hasn’t been explicitly specified as the parameter value, it is derived from the Cell Size environment if it has been specified. If the parameter cell size or the environment cell size have not been specified, but the Snap Raster environment has been set, the cell size of the snap raster is used. If nothing is specified, the cell size is calculated from the shorter of the width or height of the extent divided by 250 in which the extent is in the output coordinate system specified in the environment.

5- If the cell size is specified using a numeric value, the tool will use it directly for the output raster.�If the cell size is specified using a raster dataset, the parameter will show the path of the raster dataset instead of the cell size value. The cell size of that raster dataset will be used directly in the analysis, provided the spatial reference of the dataset is the same as the output spatial reference. If the spatial reference of the dataset is different than the output spatial reference, it will be projected based on the selected Cell Size Projection Method value.

6- For data formats that support Null values, such as file geodatabase feature classes, a Null value will be ignored when used as input.

7- If the area unit scale factor units are small relative to the features (length of line sections), the output values may be small. To obtain larger values, use the area unit scale factor for larger units (for example, square kilometers versus square meters).

16/12/2023

What is the ArcGIS Network Analyst extension

What is a network?
With the ArcGIS Network Analyst extension, you can answer questions like the following:
What is the quickest way to get from point A to point B?
Which houses are within five minutes of a fire station?

What market areas does a business cover?The green points represent warehouses in various cities, and the polygons represent their market areas, which are divided into three rings. The surrounding green polygons can be reached by trucks within two hours; orange, within four hours; and red, within six hours. �
A person wants to visit a store. Which branch should the potential customer visit to minimize travel time?
Which ambulances or patrol cars can respond quickest to an incident?The nearest police cruisers are assigned to incidents. The number of police officers needed at each location depends on the severity of the incident. Routes and expected response times for each car are generated. �
How can a fleet of delivery or service vehicles improve customer service and minimize transportation costs?Three food delivery trucks at a distribution center are assigned grocery stores and routes to the stores that minimize transportation costs. Vehicle capacities, lunch breaks, and maximum travel time constraints are included in the analysis. �
Where can a business open a store to maximize market share?
If a company has to downsize, which stores should it close to maintain the most overall demand?
What are live or historical traffic conditions like, and how do they affect my network analysis results?

Businesses, public services, and other organizations benefit from the ArcGIS Network Analyst extension because it helps them run their operations more efficiently and make better strategic decisions. These organizations can better understand dynamic markets, both current and potential, once they know who can access their goods or services. Transportation costs can be reduced by optimally sequencing stops and finding the shortest paths between the stops while considering several constraints such as time windows, vehicle capacities, and maximum travel times. Customer service can be improved through quicker response times or more convenient facility locations. The ArcGIS Network Analyst extension facilitates understanding and solving problems of this nature.
Researchers and analysts commonly benefit from the extension's ability to determine the least-cost network paths between several origins and destinations. The origin-destination cost matrices that the ArcGIS Network Analyst extension creates often become input for larger analyses. For instance, predicting travel behavior frequently incorporates the distances people would need to travel to reach certain attractions. These network distances are applied in mathematical expressions to help make trip forecasts.

The OD cost matrix analysis calculates the least-cost network paths from origins to destinations. It outputs line features that link origins to destinations. Each line feature stores the total network cost of the trip as an attribute value. Analysts often take the attribute table and use it as input for linear programming applications.
Similarly, some analyses in spatial statistics provide more accurate results when network distances are used in place of straight-line distances. Consider as an example traffic-incident analysis, which has the aim of locating clusters of traffic accidents, pinpointing their causes, and taking action to reduce the number of accidents. Since cars travel on roads, determining clusters of car accidents with network distances is far more effective than using straight-line distances.
Before you can perform network analyses to answer questions like those listed above, you need a network dataset, which models a transportation network.
What is a network?
A network is a system of interconnected elements, such as edges (lines) and connecting junctions (points), that represent possible routes from one location to another.
People, resources, and goods tend to travel along networks: cars and trucks travel on roads, airliners fly on predetermined flight paths, oil flows in pipelines. By modeling potential travel paths with a network, it is possible to perform analyses related to the movement of the oil, trucks, or other agents on the network. The most common network analysis is finding the shortest path between two points.
ArcGIS groups networks into two categories: geometric networks and network datasets.
Geometric networks (utility and river networks)
River networks and utility networks—like electrical, gas, sewer, and water lines—allow travel on edges in only one direction at a time. The agent in the network—for instance, the oil flowing in a pipeline—can't choose which direction to travel; rather, the path it takes is determined by external forces: gravity, electromagnetism, water pressure, and so on. An engineer can control the flow of the agent by controlling how external forces act on the agent.

Note:
In ArcGIS, utility and river networks are best modeled by geometric networks.

River networks and utility networks, such as a pipeline, are best modeled in ArcGIS using geometric networks, which don't require an ArcGIS Network Analyst extension.
Network datasets (transportation networks)
Transportation networks—like street, pedestrian, and railroad networks—can allow travel on edges in both directions. The agent on the network—for instance, a truck driver traveling on roads—is generally free to decide the direction of traversal as well as the destination.
Note:
In ArcGIS, transportation networks are best modeled by network datasets.
License:
The ArcGIS Network Analyst extension is required to create and edit network datasets.

Transportation networks, such as roads, are best modeled in ArcGIS by network datasets. Working with network datasets and performing analyses on them requires the ArcGIS Network Analyst extension.
Multimodal network datasets
A network dataset is capable of modeling a single mode of transportation, like roads, or a multimodal network made up of several transportation modes like roads, railroads, and waterways.

A least-cost route is shown for a pedestrian who can walk along the street network and ride on the subway network.

3D network datasets
Three-dimensional network datasets enable you to model the interior pathways of buildings, mines, caves, and so on.

A quickest route connects a stop on the first floor of a building to one on the third floor. Using restrictions, you can perform analyses that avoid staircases for wheelchair-accessible routes or that avoid elevators for evacuation planning.

If you have street features with accurate z-coordinate values, you can use them with z-aware features that model pathways inside buildings to create 3D networks of campuses or even cities. This allows you to answer questions like the following:
What is the best wheelchair-accessible route between rooms in different buildings?
What floors of a high-rise building can't be reached by a fire department within eight minutes?