The overarching motivation for developing the cityseer package is to quantify urban characteristics in a manner that is as sensitive as possible to local particularities and variations. The methods implemented in the package are designed to work well for localised urban analysis at the pedestrian scale, meaning for observations that are tailored towards pedestrian walking tolerances in the range of 50m to 800m and infrequently exceeding 2,000m. It can, furthermore, be beneficial to add spatial impedances to the methods, thus making these measures particularly sensitive to spatial nuances. These approaches aim for sufficient precision to be pertinent to the day-to-day decisions made by architects and urban designers, thus reflecting potentially varied outcomes in response to differently planned scenarios.

cityseer is developed from the ground-up to address a particular range of issues that are prevalent in pedestrian-scale urban analysis:

  • It uses localised forms of network analysis (as opposed to global forms of analysis) based on network methods applied over the graph through a ‘moving-window’ methodology. The graph is isolated at the specified distance thresholds for a selected node, and the process subsequently repeats for every other node in the network. These thresholds are conventionally based on either crow-flies euclidean distances or actual network distances (Cooper (2015)): cityseer takes the position that network distances are more representative when working at smaller pedestrian distance thresholds, especially when applied to land-use accessibilities and mixed-use calculations;
  • It is common to use either shortest-distance or simplest-path (shortest angular ‘distance’) impedance heuristics. When using simplest-path heuristics, it is necessary to modify the underlying shortest-path algorithms to prevent side-stepping of sharp angular turns (Turner (2007)); otherwise, two smaller side-steps can be combined to ‘short-cut’ sharp corners. It is also common for methods to be applied to either primal graph representations (generally used with shortest-path methods such as those applied by multiple centrality assessment (Porta et al. (2006)) analysis) or dual graph representations (typically used with simplest-path methods in the tradition of space syntax(Hillier & Hanson (1984)));
  • There is a range of possible centrality and mixed-use methods, many of which can be weighted by distances or street lengths. These methods and their implications are explored in detail in the localised centrality methods and localised land-use diversity methods papers. Some conventional methods, even if widely used, have not necessarily proved suitable for localised urban analysis;
  • Centrality methods are susceptible to topological distortions arising from ‘messy’ graph representations as well as due to the conflation of topological and geometrical properties of street networks. cityseer addresses these through the inclusion of graph cleaning functions; procedures for splitting geometrical properties from topological representations; and the inclusion of segmentised centrality measures, which are less susceptible to distortions introduced by varying intensities of nodes;
  • Hyperlocal analysis requires approaches facilitating the evaluation of respective measures at finely-spaced intervals along street fronts. Further, granular evaluation of land-use accessibilities and mixed-uses requires that land uses be assigned to the street network in a contextually precise manner. These are addressed in cityseer by applying network decomposition combined with algorithms incorporating bidirectional assignment of data points to network nodes based on the closest adjacent street edge.

The broader emphasis on localised methods and how cityseer addresses these is broached in the accompanying paper. cityseer includes a variety of convenience methods for the general preparation of networks and their conversion into (and out of) the lower-level data structures used by the underlying algorithms. These graph utility methods are designed to work with NetworkX to facilitate ease of use. A complement of code tests has been developed to maintain the codebase’s integrity through general package maintenance and upgrade cycles. Shortest-path algorithms, harmonic closeness, and betweenness algorithms are tested against NetworkX. Mock data and test plots have been used to visually confirm the intended behaviour for divergent simplest and shortest-path heuristics and testing data assignment to network nodes given various scenarios.

Graph Cleaning

You can find a notebook of this guide at google colaboratory.

Good sources of street network data, such as the Ordnance Survey’s OS Open Roads, typically have two distinguishing characteristics:

  • The network has been simplified to its essential structure: i.e. unnecessarily complex representations of intersections, on-ramps, divided roadways, etc., have been reduced to a simpler representation concurring more readily with the core topological structure of street networks. Simplified forms of network representation contrast those focusing on completeness (e.g. for route way-finding, see OS ITN Layer): these introduce unnecessary complexity serving to hinder rather than help shortest-path algorithms in the sense used by pedestrian centrality measures.
  • The topology of the network is kept distinct from the geometry of the streets. Often-times, as can be seen with Open Street Map, additional nodes are added to streets to represent geometric twists and turns along a roadway. These additional nodes cause topological distortions that impact network centrality measures.

When a high-quality source is available, it may be best not to attempt additional clean-up unless there is a particular reason. On the other hand, many indispensable sources of network information, particularly Open Street Map data, can be particularly messy for network analysis purposes.

cityseer uses customisable graph cleaning methods that reduce topological idiosyncrasies which confound centrality measures. It can, for example, remove dual carriageways while merging nodes and roadways in a manner that is as ‘tidy’ as possible.

Downloading data

This example will make use of OSM data downloaded from the OSM API. To keep things interesting, let’s pick London Soho, which will be buffered and cleaned for a 1,250m radius.

from shapely import geometry
import utm

from import graphs, plot, mock

# Let's download data within a 1,250m buffer around London Soho:
lng, lat = -0.13396079424572427, 51.51371088849723
G_utm = mock.make_buffered_osm_graph(lng, lat, 1250)

# As an alternative, you can use OSMnx to download data. Set simplify to False:
# e.g.: OSMnx_multi_di_graph = ox.graph_from_point((lat, lng), dist=1250, simplify=False)
# Then convert to a cityseer compatible MultiGraph:
# e.g.: G_utm = graphs.nX_from_OSMnx(OSMnx_multi_di_graph, tolerance=10)

# select extents for plotting
easting, northing = utm.from_latlon(lat, lng)[:2]
# buffer
buff = geometry.Point(easting, northing).buffer(1000)
# extract extents
min_x, min_y, max_x, max_y = buff.bounds

# reusable plot function
def simple_plot(_G, plot_geoms=True):
    # plot using the selected extents
                 x_lim=(min_x, max_x),
                 y_lim=(min_y, max_y),
                 figsize=(20, 20),

simple_plot(G_utm, plot_geoms=False)

The raw graph from OSM The pre-consolidation OSM street network for Soho, London. © OpenStreetMap contributors.

Deducing the network topology

Once OSM data has been converted to a NetworkX MultiGraph, the tools.graphs module can be used to clean the network.

The convenience method used for this demonstration has already converted the graph from a geographic WGS to projected UTM coordinate system; however, if working with a graph which is otherwise in a WGS coordinate system then it must be converted to a projected coordinate system prior to further processing. This can be done with graphs.nX_wgs_to_utm.

Now that raw OSM data has been loaded into a NetworkX graph, the methods can be used to further clean and prepare the network prior to analysis.

At this stage, the raw OSM graph is going to look a bit messy. Note how that nodes have been used to represent the roadway geometry. These nodes need to be removed and will be abstracted into shapely LineString geometries assigned to the respective street edges. So doing, the geometric representation will be kept distinct from the network topology.

# the raw osm nodes denote the road geometries by the placement of nodes
# the first step generates explicit LineStrings geometries for each street edge
G = graphs.nX_simple_geoms(G_utm)
# We'll now strip the "filler-nodes" from the graph
# the associated geometries will be welded into continuous LineStrings
# the new LineStrings will be assigned to the newly consolidated topological links
G = graphs.nX_remove_filler_nodes(G)
# and remove dangling nodes: short dead-end stubs
# these are often found at entrances to buildings or parking lots
# The removed_disconnected flag will removed isolated network components
# i.e. disconnected portions of network that are not joined to the main street network
G = graphs.nX_remove_dangling_nodes(G, despine=20, remove_disconnected=True)
# removing danglers can cause newly orphaned filler nodes, which we'll remove for good measure
G = graphs.nX_remove_filler_nodes(G)

Initial graph cleaning After removal of filler nodes, dangling nodes, and disconnected components.

Refining the network

Things are already looked much better, but we still have areas with large concentrations of nodes at complex intersections and many parallel roadways, which will confound centrality methods. We’ll now try to remove as much of this as possible. These steps involve the consolidation of nodes to clean-up extraneous nodes, which may otherwise exaggerate the intensity or complexity of the network in certain situations.

In this case, we’re trying to get rid of parallel road segments so we’ll do this in three steps, though it should be noted that, depending on your use-case, Step 1 may already be sufficient:

Step 1: An initial pass to cleanup complex intersections will be performed with the graphs.nX_consolidate_nodes function. The arguments passed to the parameters allow for a number of different strategies, such as whether to ‘crawl’; minimum and maximum numbers of nodes to consider for consolidation; and to set the policies according to which nodes and edges are consolidated. These are explained more fully in the documentation. In this case, we’re accepting the defaults except for explicitly setting the buffer distance and bumping the minimum size of node groups to be considered for consolidation from 2 to 3.

G1 = graphs.nX_consolidate_nodes(G, buffer_dist=10, min_node_group=3)

First step of node consolidation After an initial pass of node consolidation.

Complex intersections have now been simplified, for example, the intersection of Oxford and Regent has gone from 17 nodes to a single node.

In Step 2, we’ll use graphs.nX_split_opposing_geoms to intentionally split edges in near proximity to nodes located on an adjacent roadway. This is going to help with the final pass of consolidation in Step 3.

G2 = graphs.nX_split_opposing_geoms(G1, buffer_dist=15)

Splitting opposing geoms After “splitting opposing geoms” on longer parallel segments.

In the final step, we can now rerun the consolidation to clean up any remaining clusters of nodes. In this case, we’re setting the crawl parameter to False, setting min_node_degree down to 2, and prioritising nodes of degree=4 for determination of the newly consolidated centroids:

G3 = graphs.nX_consolidate_nodes(G2,

Final step of node consolidation After the final step of node consolidation.

Manual Cleaning

When using shortest-path methods, automated graph simplification and consolidation can arguably eliminate the need for manual checks; however, it is worth plotting the graph and performing a quick look-through to be sure there aren’t any unexpectedly odd situations.

When using simplest-path (angular) centralities, manual checks become more important because automated simplification and consolidation can result in small twists and turns where nodes and edges have been merged. cityseer uses particular methods that attempt to keep these issues to a minimum, though there may still be some situations necessitating manual checks. From this perspective, it may be preferable to use a cleaner source of network topology (e.g. OS Open Roads) if working with simplest-path centralities; else, if only OSM data is available, to instead consider the use of shortest-path methods if the graph has too many unresolvable situations to clean-up manually.

The above recipe should be enough to get you started, but there are innumerable other strategies that may also work for any variety of scenarios.

Relation to other packages


OSMnx centres on connecting the Open Street Map (OSM) API to networkX graphs, on which it builds for network analysis and from which it connects to a broader Python ecosystem of packages.

In the first instance, cityseer is not about networkX, nor is it about OSM.

cityseer is not designed for direct ingestion of OSM data sources but is instead intended to be data-source agnostic. That said, OSM queries can be piped to cityseer, else OSMnx and cityseer can be used together, an example of which is provided in the code snippet which follows below.

cityseer uses networkX primarily as an in-out and graph preparation tool for ease of use, not as its representation for algorithmic analysis. It avoids networkX for algorithmic analysis for two reasons. First, the algorithms employed in cityseer are intended for localised (windowed) graph analysis within an urban analysis context: they use explicit distance thresholds; engage unique variants of centrality measures; handle cases such as simplest-path heuristics and segmentised forms of analysis; and extend these algorithms to handle the derivation of land-use accessibilities, mixed-uses, and statistical aggregations using similarly windowed and network-distance-weighted methods. Second, networkX scales poorly to larger graphs.

The following points may be helpful when using OSMnx and cityseer together:

  • Whereas some basic OSM ingestion and conversion functions are included in the cityseer tools.mock module, these are primarily intended for internal code development. If used directly, these assume that the end-user will have some direct knowledge of how these APIs work and how to manipulate the recipes and conversion functions for specific situations. i.e. unless you want to roll-your-own OSM queries, it is best to stick with OSMnx for purposes of ingesting OSM networks.
  • OSMnx prepared graphs can be converted to cityseer compatible graphs by using the tools.graphs.nX_from_OSMnx method. In doing so, keep the following in mind:
    • OSMnx uses networkX multiDiGraph graph structures that use directional edges. As such, it can be used for understanding vehicular routing, i.e. where one-way routes can have a major impact on the available shortest-routes. cityseer is only concerned with pedestrian networks and therefore uses networkX MultiGraphs on the premise that pedestrian networks are not ordinarily directional. When using the tools.graphs.nX_from_OSMnx method, be cognisant that all directional information will be discarded.
    • cityseer graph simplification and consolidation workflows will give subtly different results to those employed in OSMnx. If you’re using OSMnx to ingest networks from OSM but wish to simplify and consolidate the network as part of a cityseer workflow, set the OSMnx simplify argument to False so that the network is not automatically simplified.
  • cityseer uses internal validation workflows to check that the geometries associated with an edge remain connected to the coordinates of the nodes on either side. If performing graph manipulation outside of cityseer before conversion, the conversion function may complain of disconnected geometries. In these cases, you may need to relax the tolerance parameter used for error checking upon conversion to a cityseer MultiGraph, in which case geometries disconnected from their end-nodes (within the tolerance parameter) will be “snapped” to meet their endpoints as part of the conversion process.
  • For graph cleaning and simplification: cityseer is oriented less towards automation and ease-of-use and more towards explicit and intentional use of potentially varied processing steps. This entails a tradeoff, whereas some recipes are provided as a starting point (see Graph Cleaning), you may find yourself needing to do more up-front experimentation and fine-tuning, but with more flexibility in how these methods are applied for a given network topology: e.g. steps can be included or omitted, used in different sequences, or repeated. Some of these methods, particularly tools.graphs.nX_consolidate_nodes, may have severable tunable parameters which can have markedly different outcomes. This philosophy is by design, and if you want a simplified method that you can easily repeat, you’ll need to configure and wrap your preferred sequence of steps in a utility function.
from cityseer import tools
import osmnx as ox
from shapely import geometry
import utm

# centre-point
lng, lat = -0.13396079424572427, 51.51371088849723

# select extents for plotting
easting, northing = utm.from_latlon(lat, lng)[:2]
buff = geometry.Point(easting, northing).buffer(1000)
min_x, min_y, max_x, max_y = buff.bounds

# reusable plot function
def simple_plot(_G):
    # plot using the selected extents
                       x_lim=(min_x, max_x),
                       y_lim=(min_y, max_y),
                       figsize=(20, 20),

# Let's use OSMnx to fetch an OSM graph
# We'll use the same raw network for both workflows (hence simplify=False)
multi_di_graph_raw = ox.graph_from_point((lat, lng),

# Workflow 1: Using OSMnx to prepare the graph
# ============================================
# explicit simplification and consolidation via OSMnx
multi_di_graph_utm = ox.project_graph(multi_di_graph_raw)
multi_di_graph_simpl = ox.simplify_graph(multi_di_graph_utm)
multi_di_graph_cons = ox.consolidate_intersections(multi_di_graph_simpl,
# let's use the same plotting function for both scenarios to aid visual comparisons
multi_graph_cons = tools.graphs.nX_from_OSMnx(multi_di_graph_cons, tolerance=50)

# WORKFLOW 2: Using cityseer to prepare the graph
# ===============================================
# let's convert the OSMnx graph to a cityseer compatible `multiGraph`
G_raw = tools.graphs.nX_from_OSMnx(multi_di_graph_raw)
# convert to UTM
G = tools.graphs.nX_wgs_to_utm(G_raw)
# infer geoms
G = tools.graphs.nX_simple_geoms(G)
# remove degree=2 nodes
G = tools.graphs.nX_remove_filler_nodes(G)
# remove dangling nodes
G = tools.graphs.nX_remove_dangling_nodes(G, despine=10)
# repeat degree=2 removal to remove orphaned nodes due to despining
G = tools.graphs.nX_remove_filler_nodes(G)
# let's consolidate the nodes
G1 = tools.graphs.nX_consolidate_nodes(G, buffer_dist=10, min_node_group=3)
# and we'll try to remove as many parallel roadways as possible
G2 = tools.graphs.nX_split_opposing_geoms(G1, buffer_dist=15)
G3 = tools.graphs.nX_consolidate_nodes(G2,

Example OSMnx simplification and consolidation An example OSMnx simplification and consolidation workflow.

Example OSMnx to cityseer workflow An example OSMnx to cityseer conversion followed by simplification and consolidation workflow in cityseer.

Optimised packages

Computational methods for network-based centrality and land-use analysis make extensive use of shortest-path algorithms: these present substantial computational complexity due to nested-loops. Centrality methods implemented in pure Python, such as those contained in NetworkX, are particularly slow and may hinder timely application to large urban street networks (though, for the record, NetworkX is an exquisitely designed package). Speed improvements are attainable by running intensive algorithms against packages such as Graph-Tool or igraph, which wrap underlying optimised code libraries implemented in more performant languages such as C++. However, these offer a limited choice of analytic methods that are not necessarily suited for application to urbanism.

Note that cityseer does not explicitly implement globalised forms of network analysis and has no intention of doing so because these approaches make it tricky to compare metrics across locations. Therefore, if the aim is conventional forms of global centralities applied to the network as a city-wide entity, then it may be worth sticking with a package such as Graph-Tool, which wraps heavily optimised code designed for these forms of analysis. On the other hand, if using localised forms of graph analysis considering factors such as localised distance thresholds, specialised centralities, shortest vs simplest-path heuristics, or land-use accessibilities and mixed-uses: then cityseer offers methods that are not available through other more generic off-the-shelf network analysis packages.

cityseer consists of pure python and numpy, but with computationally intensive algorithms implemented in numba for the sake of performant JIT compilation. The use of numba has made it feasible to scale these methods to large and, optionally, decomposed networks while releasing Python’s GIL and using all available CPUs. Further, numba permits a style of programming more in keeping with lower-level languages, i.e. it is possible to use loops explicitly, which can in many cases be simpler to reason-with than nested array indices more typical of numpy.

Cooper, C.H.V., 2015. Spatial localization of closeness and betweenness measures: a self-contradictory but useful form of network analysis. International Journal of Geographical Information Science, 29(8), pp.1293–1309. Available at:
Hillier, B. & Hanson, J., 1984. The Social Logic of Space, Cambridge: Cambridge University Press.
Porta, S., Crucitti, P. & Latora, V., 2006. The Network Analysis of Urban Streets: A Primal Approach. Environment and Planning B: Planning and Design, 33, pp.705–725. Available at:
Turner, A., 2007. From axial to road-centre lines: a new representation for space syntax and a new model of route choice for transport network analysis. Environment and Planning B: Planning and Design, 34, pp.539–555. Available at: