Table 4 Calculation and meaning of spatial characteristic indicators

Spatial network

Road connectivity(RC)

\(RC = \frac{L}{{3\left( {V - 2} \right)}}\)

L is the number of corridors,

V is the number of nodes

Degree of road circulation(DRC)

\(DRC = (L - V)/(2V - 5)\)

L is the number of corridors,

V is the number of nodes

Road density (RD)

\(RD = L/A\)

L is the total length of the road axis within the study scope,

A is the settlement land use area

Spatial morphology

Fractal dimension(FD)

\(FD = \mathop {\lim }\limits_{n \to \infty } \frac{\ln N(\varepsilon )}{{\ln (1/\varepsilon )}}\)

Assuming that F is a bounded set point on a plane, it is always possible to find a rectangle to include F, dividing the rectangle into several small squares with sides long r, and the number of small squares occupied by F is N(r)

Patch fragmentation(PF)

\(PF = Ni/Ai\)

Ni is the total number of patches of a certain spatial type, and Ai is the total area of the settlement

Choice (C)

Choice is the choice calculated based on axial map. It measures how often an axial line lies on the shortest topological paths any pair of axial lines. The higher the choice of an axis, the higher the traffic potential for that axis to be traversed

Connectivity (Con)

\(Coni = k\)

Connectivity is the number of other lines an axial line intersects. In the actual spatial system, the higher the spatial connectivity, the better the permeability of the space. Where, k is the number of nodes directly connected to node i

Integration (I)

\(I_{i} = \frac{n - 2}{{2(MDi - 1)}}\)

Integration is a measure of integration of axial lines. High values means an axial line with a high degree of integration. It characterizes the overall spatial properties of a specific area, reflecting the accessibility of a space, the higher the integration of space, the higher its accessibility. Where, n is the total number of nodes in the network (number of axes); MD_i is mean depth

Control (Ctrl)

\(Ctrl = \sum\limits_{1}^{n} \frac{1}{Ci}\)

Control measures the degree to which one axial line controls its immediately neighboring lines, which can indicate the degree of control of a spatial node over the space intersecting it, and reflect the degree of aggregation between local spaces

Where, C_i is the connectivity of road_i

Mean depth (MD)

\(MDi = \frac{Di}{{n - 1}}\)

Mean depth is defend as the arithmetic mean of depths from each line to all others. The higher the value, the lower the convenience of that spatial node

Where, D_i is global depth, n is the total number of nodes in the network (number of axes)