Sustainable urban form: socio-demographic and permeability factors as determinants of crime spots in cities, case of Akure, Nigeria

Figures & data

Figure 1. Akure in the context of Ondo State, Nigeria.

Figure 2. Open road map of the study area.

Figure 3. Shows a geospatial analysis of the level of crime in the study area.

Figure 4. Streets accessibility of the study area: (a) Connectivity (b) Control (c) local mean depth (d)local integration.

Table 1. Spatial level of accessibility of street segments.

Street accessibility	Line colour	Value	Frequency	Percentage %
Connectivity	Blue (low)	1.0–3.5	17	15
	Green(medium)	3.6–7.5	57	50
	Red (high)	7.6–9.0	40	35
Control	Blue (low)	0.01–1.0	17	15
	Green(medium)	1.0–3.0	29	25
	Red (high)	3.0–5.06	68	60
Local mean depth	Blue (low)	0>1.0	11	10
	Green(medium)	1.0–1.8	68	60
	Red (high)	1.8<1.9	29	25
Local integration	Blue (low)	0–2.0	3	2.63
	Green(medium)	2.0–4.0	42	36.8
	Red (high)	4.0–8.0	70	79.8
Total			114	100

Figure 5. Combined map of socio-demographic and connectivity crime hot spots.

Table 2. Parameter estimates.

Parameter	B	Std error	95%Wald Confidence interval		Hypothesis Test			Exp (B)	95% Wald Confidence Interval [Exp(B)]
			Lower	Upper	Wald chi-square	df	sig		Lower	Upper
intercept	2.332	0.4101	1.528	3.138	32.323	1	.000	10.296	4.609	23.003
gender (1)	0a	.	.	.	.	1	.	.	.	.
age (2)	.098	.2205	−.335	530	.196	1	.058	1.103	.716	1.699
education (1)	−.066	.2654	−.586	.454	.062	1	.803	.936	.556	1.574
employment (2)	−.058	.0861	−.227	.110	.458	1	.098	.943	.797	1.117
connectivity (3)	.283	.3322	−.369	.934	.724	1	.095	1.327	.692	2.544
control (2)	−.023	.0767	−.173	.127	.089	1	.066	.977	.841	1.136
local mean depth (2)	.139	.0886	−.030	.309	2.539	1	.007	1.150	.970	1.362
local integration (2)	.151	.0959	−.037	.339	2.466	1	.016	1.163	.963	1.403
local street	.064	.0997	−.132	.259	.407	1	.023	1.066	.877	1.296
arterial road	−.244	.1330	−.504	.017	3.355	1	.067	.784	.604	1.017
cul-de-sac	−.164	2.332	.1608	−.479	.152	1	.309	.849	.620	1.164
scale	1a

Table

Socio-demographic variable	characteristics	Frequency
		NO	%
Gender	Male	296	92.2
	Female	25	7.78
Age
	18–25	61	19.0
	26–35	135	42.0
	36–45	100	31.2
	46–55	25	7.78
Education	Primary Sch cert	60	18.6
	Secondary school	205	63.9
	University/higher education	56	17.4
	Trader	224	69.7
Employment	Civil servant	60	18.7
	No job	37	11.5
Total		321	100

Table

Neighbourhood	Street segments	Count (no of burglaries)	percentage
Araromi	10	17	5.2
Cathedral	10	15	4.65
Erekesan	8	20	6.23
Eruoba	12	38	11.8
Ijomu	15	40	12.5
Imuagun	6	25	7.78
Isolo	13	34	10.6
Obanla	12	39	12.1
Odo-Ijoka	10	37	11.5
Odo Ikoyi	10	38	11.8
Stadium	8	19	5.9
	114	321	100

Table

	N	Minimum	Maximum	Mean	Std. Deviation
Dependent Variable burglary	321	.00	12.00	6.2927	3.65714

Table

	Value	df	Value/df
Deviance	153.855	182	.845
Scaled Deviance	153.855	182
Pearson Chi-Square	140.285	182	.771
Scaled Pearson Chi-Square	140.285	182
Log Likelihood −423.479	892.958
Akaike’s Information Criterion (AIC)	899.057
Finite Sample Corrected AIC (AICC)	969.387
Bayesian Information Criterion (BIC)	992.387
Consistent AIC (CAIC)

Dependent Variable: burglary.

Model: (Intercept), gender, age, education, employment, connect, control,

Lmeandept, Locintgratn, Locstreet, coltoroad, areterialroad, culdesac

(a) Information criteria are in mall-is-better form.

(b) The full log-likelihood function is displayed and used in computing information criteria